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Enero de 2025 Terrestrial, aquatic, and marine ecosystems regulate climate at local to global scales through exchanges of energy and matter with the atmosphere and assist with climate change mitigation through nature-based climate solutions. Climate science is no longer a study of the physics of the atmosphere and oceans, but also the ecology of the biosphere. This is the promise of Earth system science: to transcend academic disciplines to enable study of the interacting physics, chemistry, and biology of the planet. However, long-standing tension in protecting, restoring, and managing forest ecosystems to purposely improve climate evidences the difficulties of interdisciplinary science. For four centuries, forest management for climate |
betterment was argued, legislated, and ultimately dismissed, when nineteenth century atmospheric scientists narrowly defined climate science to the exclusion of ecology. Today's Earth system science, with its roots in global models of climate, unfolds in similar ways to the past. With Earth system models, geoscientists are again defining the ecology of the Earth system. Here we reframe Earth system science so that the biosphere and its ecology are equally integrated with the fluid Earth to enable Earth system prediction for planetary stewardship. Central to this is the need to overcome an intellectual heritage to the models that elevates geoscience and marginalizes ecology and local land knowledge. The call for kilometer-scale atmospheric and ocean models, without concomitant scientific and computational investment in the land and biosphere, perpetuates the geophysical view of Earth and will not fully provide the comprehensive actionable information needed for a changing climate. |
Diciembre de 2024 The interplay between seismic activity and fluid flow is essential during the evolution of hydrothermal systems. Although earthquakes can trigger transient fluid flow and phase changes in dilational jogs, the temporal scale and the geologic conditions that enhance such process are poorly quantified. Here, we use numerical simulations of deformation and |
fluid flow to constrain the conditions that maximize adiabatic boiling—referred to as flashing—and estimate the extent and duration of such process. We show that there is an optimal geometry for a dilational jog that maximizes co-seismic flashing within the jog. Fluid flow simulations indicate that the duration, intensity, and propagation of the flashing front are limited and highly dependent on the magnitude of the co-seismic slip and the initial pressure-enthalpy conditions. Our results are valuable to better understand the implications of pressure fluctuations during the seismogenic cycle, as well the mineralization processes in the Earth's crust. |
Diciembre de 2024 While deterministically predicting the time and location of earthquakes remains impossible, earthquake forecasting models can provide estimates of the probabilities of earthquakes occurring within some region over time. To enable informed decision-making of civil protection, governmental agencies, or the public, Operational Earthquake Forecasting (OEF) systems aim to provide authoritative earthquake forecasts based on current earthquake activity in near-real time. Establishing OEF systems involves several nontrivial choices. This review captures the current state of OEF worldwide and analyzes expert recommendations on the development, testing, and communication of earthquake forecasts. An introductory summary of OEF-related research is |
followed by a description of OEF systems in Italy, New Zealand, and the United States. Combined, these two parts provide an informative and transparent snapshot of today's OEF landscape. In Section 4, we analyze the results of an expert elicitation that was conducted to seek guidance for the establishment of OEF systems. The elicitation identifies consensus and dissent on OEF issues among a non-representative group of 20 international earthquake forecasting experts. While the experts agree that communication products should be developed in collaboration with the forecast user groups, they disagree on whether forecasting models and testing methods should be user-dependent. No recommendations of strict model requirements could be elicited, but benchmark comparisons, prospective testing, reproducibility, and transparency are encouraged. Section 5 gives an outlook on the future of OEF. Besides covering recent research on earthquake forecasting model development and testing, upcoming OEF initiatives are described in the context of the expert elicitation findings. |
Diciembre de 2024 In this study, we present a machine-learning model capable of predicting food insecurity in the Horn of Africa, which is one of the most vulnerable regions worldwide. The region has frequently been affected by severe droughts and food crises over the last several decades, which will likely increase in future. Therefore, exploring novel methods of increasing early warning capabilities is of vital importance to reducing food-insecurity risk. We present a XGBoost machine-learning model to predict food-security crises up to 12 months in advance. We used >20 data sets and the FEWS IPC current-situation |
estimates to train the machine-learning model. Food-security dynamics were captured effectively by the model up to 3 months in advance (R2 > 0.6). Specifically, we predicted 20% of crisis onsets in pastoral regions (n = 96) and 20%–50% of crisis onsets in agro-pastoral regions (n = 22) with a 3-month lead time. We also compared our 8-month model predictions to the 8-month food-security outlooks produced by FEWS NET. Over a relatively short test period (2019–2022), results suggest the performance of our predictions is similar to FEWS NET for agro-pastoral and pastoral regions. However, our model is clearly less skilled in predicting food security for crop-farming regions than FEWS NET. With the well-established FEWS NET outlooks as a basis, this study highlights the potential for integrating machine-learning methods into operational systems like FEWS NET. |
Diciembre de 2024 There is a growing consensus that Venus is seismically active, although its level of seismicity could be very different from that of Earth due to the lack of plate tectonics. Here, we estimate upper and lower bounds on the expected annual seismicity of Venus by scaling the seismicity of the Earth. We consider different scaling factors for different |
tectonic settings and account for the lower seismogenic thickness of Venus. We find that 95–296 venusquakes equal to or bigger than moment magnitude (Mw) 4 per year are expected for an inactive Venus, where the global seismicity rate is assumed to be similar to that of continental intraplate seismicity on Earth. For the active Venus scenarios, we assume that the coronae, fold belts, and rifts of Venus are currently seismically active. This results in 1,161–3,609 venusquakes ≥Mw4 annually as a realistic lower bound and 5,715–17,773 venusquakes ≥Mw4 per year as a maximum upper bound for an active Venus. |
Noviembre de 2024 The mineralogy of planetary mantles formed under reducing conditions, as documented in the inner regions of the solar system, is not well constrained. We present thermodynamic models of mineral assemblages that would constitute the mantles of exo-Mercuries. We investigated reduced materials such as enstatite chondrites, CH, and CB chondrites, and aubrites, as precursor bulk compositions in phase equilibrium modeling. The |
resulting isochemical phase diagram sections indicate that dominant phases in these reduced mantles would be pyroxenes rather than olivine, contrasting with the olivine-rich mantles found within Earth, Mars, and Venus. The pyroxene abundances in the modeled mantles assemblages depend on the silica content shown by precursor materials. The silica abundance in the mantle is closely related to Si abundance in the core, particularly in reduced environments. In addition, we propose that pyroxene-rich mantles exhibit more vigorous convective and tectonic activity than olivine-rich mantles, given that pyroxene-rich mantles would have lower viscosity and a lower solidus temperature (Ts). |
Noviembre de 2024 In the 1970s, two types of seismometers were installed on the nearside of the Moon. One type is called the Long-Period (LP) seismometer, which is sensitive below 1.5 Hz. The other is called the Short-Period (SP) seismometer, whose sensitivity is high around 2–10 Hz. So far, more than 13,000 seismic events have been identified through analyzing the LP data, which allowed us to investigate lunar seismicity and its internal structure. On the other hand, most of the SP data have remained unanalyzed because they include numerous artifacts. This fact leads to the hypotheses that (a) we have missed lots of high-frequency seismic events and (b) lunar seismicity could be e the |
conventional views of lunar seismicity. underestimated. To verify these ideas, I conducted an analysis of the SP data. In the analysis, I denoised the original SP data and performed the event detections by comparing the spectral features between the cataloged high-frequency events (such as shallow moonquakes) and the continuous SP data. Eventually, I discovered 22,000 new seismic events, including thermal moonquakes, impact-induced events, and shallow moonquakes. Among these, I focused on analyzing shallow moonquakes—tectonic-related quakes. Consequently, it turned out that there were 2.6 times more tectonic events than considered before. Furthermore, additional detections of shallow moonquakes enabled me to see the regionality in seismicity. Comparing three landing sites (Apollo 14, 15, and 16), I found that the Apollo 15 site was more seismically active than others. These findings can chang |
Noviembre de 2024 Numerous studies have reported anomalous ultralow frequency (ULF) electromagnetic fields preceding earthquakes. In this paper, we estimate the current intensity responsible for generating the earthquake-related ULF fields under the assumption that the origin is a current flowing at the hypocenter and that it has the same frequency |
dependence for all cases. To estimate current intensity, we perform ULF electromagnetic field simulations with an absorbing boundary condition developed in this study, taking into account the conductivity distribution of the Earth's crust. We analyze 11 earthquakes, including those that occurred in Loma Prieta, Spitak, Guam, Biak, Kagoshima, Iwateken Nairiku Hokubu, Izu swarm, Jammu and Kashmir, Alum Rock, Wenchuan, and L’Aquila. Our results show that, for nine out of the 11 events, there is a positive correlation between current intensity and earthquake magnitude, suggesting that the measured ULF fields originate from seismic activity and supporting our assumptions. |
Noviembre de 2024 This study presents a comprehensive assessment of the geomorphology, crater distributions, and tectonic structures within Enceladus' cratered terrains. We analyzed the distributions of impact craters and tectonic structures in seven regions of interest to inform an interpretation of the geological history of this terrain in the context of Enceladus' global evolution. We found that the tectonic structures, including both ancient, subdued troughs and young, narrow fractures, point to a cratered terrain that not only experienced early tectonic modification but also shows evidence of recent geological activity. Ancient troughs present in the equatorial cratered terrains are similar in scale and orientation to troughs present in the Leading and |
Trailing Hemisphere Terrains, an observation that supports possible non-synchronous rotation of the ice shell. A dearth of impact craters in the equatorial regions as identified previously does not hold for craters <3 km in diameter in the anti-Saturnian hemisphere. The anomalous presence of excess small craters in this region could be due to secondary or sesquinary impacts from a catastrophic event occurring at Enceladus or a neighboring moon. Finally, narrow fractures are pervasive across the cratered terrains and are most commonly oriented parallel or sub-parallel to the most proximal cratered terrain boundary. This directionality of pervasive recent fracturing could be related to the vertical movement of an isostatically uncompensated ice shell. Enceladus' cratered terrains provide insight into the long-term evolution of the satellite, an important component to assessing its role in Solar System evolution and its potential for habitability. |
Octubre de 2024 Vegetation gross primary productivity (GPP) is the single largest carbon flux of the terrestrial biosphere which, in turn, is responsible for sequestering 25%–30% of anthropogenic carbon dioxide emissions. The ability to model GPP is therefore critical for calculating carbon budgets as well as understanding climate feedbacks. Earth system models (ESMs) have the capability to simulate GPP but vary greatly in their individual estimates, resulting in large uncertainties. We describe a machine learning (ML) approach to investigate two key factors responsible for differences in simulated |
GPP quantities from ESMs: the relative importance of different atmospheric drivers and differences in the representation of land surface processes. We describe the different steps in the development of our interpretable ML framework including the choice of algorithms, parameter tuning, training and evaluation. Our results show that ESMs largely agree on the physical climate drivers responsible for GPP as seen in the literature, for instance drought variables in the Mediterranean region or radiation and temperature in the Arctic region. However differences do exist since models don't necessarily agree on which individual variable is most relevant for GPP. We also explore a distance measure to attribute GPP differences to climate influences versus process differences and provide examples for where our methods work (South Asia, Mediterranean) and where they are inconclusive (Eastern North America). |
Octubre de 2024 Earth went through at least two periods of global glaciation (i.e., “Snowball Earth” states) during the Neoproterozoic, the shortest of which (the Marinoan) may not have lasted sufficiently long for its termination to be explained by the gradual volcanic build-up of greenhouse gases in the atmosphere. Large asteroid impacts and supervolcanic eruptions have been suggested as stochastic geological |
events that could cause a sudden end to global glaciation via a runaway melting process. Here, we employ an energy balance climate model to simulate the evolution of Snowball Earth's surface temperature after such events. We find that even a large impactor (diameters of d ∼ 100 km) and the supervolcanic Toba eruption (74 Kyr ago), are insufficient to terminate a Snowball state unless background CO2 has already been driven to high levels by long-term outgassing. We suggest, according to our modeling framework, that Earth's Snowball states would have been resilient to termination by stochastic events. |
Octubre de 2024 Recent geophysical studies have highlighted the potential utility of integrating both seismic and infrasound data to improve source characterization and event discrimination efforts. However, the influence of each of these data types within an integrated framework is not yet well-understood by |
the geophysical community. To help elucidate the role of each data type within a merged structure, we develop a neural network which fuses seismic and infrasound array data via a gated multimodal unit for earthquake-explosion discrimination within the Korean Peninsula. Model performance is compared before and after adding the infrasound branch. We find that the seismoacoustic model outperforms the seismic model, with the majority of the improvements stemming from the explosions class. The influence of infrasound is quantified by analyzing gated multimodal activations. Results indicate that the model relies comparatively more on the infrasound branch to correct seismic predictions. |
Octubre de 2024 Vegetation gross primary productivity (GPP) is the single largest carbon flux of the terrestrial biosphere which, in turn, is responsible for sequestering 25%–30% of anthropogenic carbon dioxide emissions. The ability to model GPP is therefore critical for calculating carbon budgets as well as understanding climate feedbacks. Earth system models (ESMs) have the capability to simulate GPP but vary greatly in their individual estimates, resulting in large uncertainties. We describe a machine learning (ML) approach to investigate two key factors responsible for differences in simulated |
GPP quantities from ESMs: the relative importance of different atmospheric drivers and differences in the representation of land surface processes. We describe the different steps in the development of our interpretable ML framework including the choice of algorithms, parameter tuning, training and evaluation. Our results show that ESMs largely agree on the physical climate drivers responsible for GPP as seen in the literature, for instance drought variables in the Mediterranean region or radiation and temperature in the Arctic region. However differences do exist since models don't necessarily agree on which individual variable is most relevant for GPP. We also explore a distance measure to attribute GPP differences to climate influences versus process differences and provide examples for where our methods work (South Asia, Mediterranean) and where they are inconclusive (Eastern North America). |
Octubre de 2024 There is a growing consensus that Venus is seismically active, although its level of seismicity could be very different from that of Earth due to the lack of plate tectonics. Here, we estimate upper and lower bounds on the expected annual seismicity of Venus by scaling the seismicity of the Earth. We consider different scaling factors for different |
tectonic settings and account for the lower seismogenic thickness of Venus. We find that 95–296 venusquakes equal to or bigger than moment magnitude (Mw) 4 per year are expected for an inactive Venus, where the global seismicity rate is assumed to be similar to that of continental intraplate seismicity on Earth. For the active Venus scenarios, we assume that the coronae, fold belts, and rifts of Venus are currently seismically active. This results in 1,161–3,609 venusquakes ≥Mw4 annually as a realistic lower bound and 5,715–17,773 venusquakes ≥Mw4 per year as a maximum upper bound for an active Venus. |
Septiembre de 2024 Is it possible for a rocky planet to have too much internal heating to maintain a habitable surface environment? In the Solar System, the best example of a world with high internal heating is Jupiter's moon Io, which has a heat flux of approximately 2 W |
investigate the habitability of these worlds, referred to as Ignan Earth's We demonstrate how the mantle will remain largely solid despite high internal heating, allowing for the formation of a convectively buoyant and stable crust. In addition, we model the long-term climate of Ignan Earth's by simulating the carbonate-silicate cycle in a vertical tectonic regime (known as heat-pipe tectonics, expected to dominate on such worlds) at varying amounts of internal heating. We find that Earth-mass planets with internal heating fluxes below 15 W produce average surface temperatures that Earth has experienced in its past (below 30 C), and worlds with significantly higher heat fluxes still result in surface temperatures far below that of 100 C, indicating a wide range of internal heating rates may be conducive with habitability. |
Septiembre de 2024 Spatiotemporal forecasting involves generating temporal forecasts for system state variables across spatial regions. Data-driven methods such as Convolutional Long Short-Term Memory (ConvLSTM) are effective in capturing both spatial and temporal correlations, but they suffer from error accumulation and accuracy loss as forecasting time increases due to the nonlinearity and uncertainty in physical processes. To address this issue, we propose to combine data-driven and data assimilation (DA) methods for spatiotemporal forecasting. The accuracy of the data-driven ConvLSTM model can be improved by periodically assimilating real-time observations using the |
ensemble Kalman filter (EnKF) approach. This proposed hybrid ConvLSTM-EnKF method is demonstrated through PM2.5 forecasting in China, which is a challenging task due to the complexity of topographical and meteorological conditions in the region, the need for high-resolution forecasting over a large study area, and the scarcity of observations. The results show that the ConvLSTM-EnKF method outperforms conventional methods and can provide satisfactory operational PM2.5 forecasts for up to 1 month with spatially averaged RMSE below 20 μg/m3 and correlation coefficient (R) above 0.8. In addition, the ConvLSTM-EnKF method shows a substantial reduction in CPU time when compared to the commonly used NAQPMS-EnKF method, up to three orders of magnitude. Overall, the use of data-driven models provides efficient forecasts and speeds up DA. This hybrid ConvLSTM-EnKF is a novel operational forecasting technique for spatiotemporal forecasting and is used in real spatiotemporal forecasting for the first time. |
Septiembre de 2024 Earthquake swarms are most commonly composed of small-magnitude earthquakes. However, a recent study by Yoshida, Uchida, et al. (2023, https://doi.org/10.1029/2023GL106023) analyzed a swarm beneath the Noto Peninsula in Japan that, after more than two years of moderate-magnitude seismicity, triggered the moment magnitude (Mw) |
6.2 Suzu mainshock in May 2023. Based on high- precision earthquake locations and a slip inversion of the mainshock, these authors found that the Mw 6.2 Suzu earthquake occurred on the updip extension of a fault that was active during the swarm, likely driven by increased fluid pressure. After publication of that paper, a much larger and more destructive Mw 7.5 event occurred nearby on 1 January 2024. These events underscore the potential for swarms to be precursors to large, damaging earthquakes. Forecasting the eventual evolution of swarms is currently very challenging but could be aided in the future by new observations and models. |
Septiembre de 2024 To deeply understand various geodynamic processes, including volcanic activities and earthquakes, it is essential to extract detailed information about Earth materials, such as lithology and geofluid, from geophysical, petrological, and geochemical observations of Earth's interior. We developed a Bayesian probabilistic framework that can estimate the lithology and geofluid type (aqueous fluid or melt), geofluid amount (porosity), and parameters related to the fluid geometry (aspect ratio and critical fluid fraction related to connectivity) from P-wave and S-wave seismic velocities and electrical conductivity data obtained from geophysical tomography. By conducting synthetic inversion tests, we showed that methods based on |
a joint probability distribution, which simultaneously determines all parameters, sometimes fail to narrow the number of possible answers from 78 lithologies (e.g., basalt, granite, and eclogite) × two geofluid type (melt or aqueous fluid) candidate sets. This failure is derived directly from the difficult nature of inversion problems with relatively large data uncertainties. The proposed method uses a marginalization technique that first estimates lithology and geofluid type and then quantifies geofluid parameter values, which can narrow the probable lithology, geofluid type, and geofluid parameter sets in many cases. In addition, the computational cost of the proposed marginalization method is comparative to a former joint-estimation method, which is theoretically identical to a previous heuristic method based on the least squares method. Therefore, the marginalization method is useful for geophysical data analyses that involve large amounts of observational data with relatively large uncertainties. |
Agosto de 2024 Heat flow is a geothermal parameter for indicating the heat source distribution and evaluating geothermal reservoirs. Only 1,230 heat flow points are distributed unevenly in China, mainly concentrated in high-temperature geothermal and southeast regions. The Songliao Basin is a potential geothermal field in China. Still, only 20 measurement points are known, making evaluating the geothermal genetic mechanism difficult. Sparse data interpolation using deep learning methods is highly accurate and widely used in fields such as image processing. In this work, we propose a deep neural network for predicting heat flow in the Songliao Basin. More than 4,000 global heat flows |
and 23 geological and geophysical parameters are used as reference constraints for training. The uncertainty error of the prediction is estimated based on the correlation and distance-based generalized sensitivity analysis. The results show that the maximum heat flow is 85 mW/m2, the average is 67.1 mW/m2, and the error with the measured data is 10.64%. The previous geophysical and geological interpretation results indicate that the heat flow is higher in the west and lower in the east, with high anomalies in the central region, which may be related to the uplift of the deep mantle and the depression of the shallow low-velocity sedimentary layer. Some high-temperature melt bodies are in the deep layers, forming the current potential geothermal field. The measured data validates that the DNN is an effective method for predicting regional-scale heat flow, providing reliable heat source information for evaluating geothermal resources. |
Agosto de 2024 Various earthquake models predict that aseismic slip modulates the seismic rupture process but actual observations of such seismic-aseismic interaction are scarce. We analyze seismic and aseismic processes during the 2014 Iquique earthquake sequence. High-rate Global Positioning System displacements demonstrate that most of the early afterslip is located downdip of the M 8.1 |
mainshock and is accompanied by decaying aftershock activity. An intriguing secondary afterslip peak is located ∼120 km south of the mainshock epicenter. The area of this secondary afterslip peak likely acted as a barrier to the propagating mainshock rupture and delayed the M 7.6 largest aftershock, which occurred 27 hr later. Interevent seismicity in this secondary afterslip area ended with a M 6.1 near the largest aftershock epicenter, kicking the largest aftershock rupture in the same area. Hence, the interevent afterslip likely promoted the largest aftershock nucleation by destabilizing its source area, favoring a rate-dependent cascade-up model. |
Agosto de 2024 A tectonic Plate Motion Model (PMM) is essential for geodetic applications, while contributing to the understanding of geodynamic processes affecting the Earth's surface. We introduce a PMM derived from the horizontal velocities of 518 sites extracted from the ITRF2020 solution. These sites were chosen away from plate boundaries, Glacial Isostatic Adjustment regions, and other deforming |
zones. Unlike the ITRF2014-PMM, which showed no significant Origin Rate Bias (ORB), velocities used to determine the ITRF2020-PMM exhibit a statistically significant ORB (0.74 ± 0.09 mm/yr along the Z-component). Users are advised to add the estimated ORB to the horizontal velocities predicted by the ITRF2020-PMM rotation poles for full consistency with the ITRF2020. However, the predicted vertical velocities resulting from the addition of the ORB should be discarded. The overall precision with which the ITRF2020 velocity field is represented by the rigid ITRF2020-PMM is at the level of 0.25 mm/yr WRMS. |
Julio de 2024 Observations of seismicity prior to large earthquakes show that the slope of a Gutenberg-Richter magnitude-frequency relation, referred to as a b-value, sometimes decreases with time to the mainshock. Yet, underlying physical processes associated with the temporal change of a b-value remain unclear. Here we utilize continuum models of fully dynamic earthquake cycles with fault frictional heterogeneities and aim to simulate the temporal variation of a b-value. We first identify a parameter regime in which the model gives rise to an active and accelerating foreshock behavior prior to the mainshock. We then focus on the spatio-temporal |
pattern of the simulated foreshocks and analyze their statistics. We find that the b-value of simulated foreshocks decreases with time prior to the mainshock. A marked decrease in the resulting b-value occurs over the duration of less than a few percent of the mainshock recurrence interval, broadly consistent with foreshock behaviors and b-value changes as observed in nature and laboratory, rock-friction experiments. In this model, increased shear stresses on creeping (or velocity-strengthening) fault patches resulting from numerous foreshocks make these creeping patches more susceptible to future coseismic slip, increasing the likelihood of large ruptures and leading to a smaller b-value with time. This mechanism differs from a widely invoked idea that the decrease of a b-value is caused by a rapid increase in shear stress that promotes micro-crack growth, and offers a new interpretation of b-value changes prior to a large earthquake. |
Julio de 2024 We investigate the relation between frictional heating on a fault and the resulting conductive surface heat flow anomaly using the fault's long-term energy budget. Analysis of the surface heat flow surrounding the fault trace leads to a constraint on the frictional power generated on the fault—the mechanism behind the San Andreas fault (SAF) heat flow paradox. We revisit this paradox from a new perspective using an estimate of the long-term accumulating elastic power in the region surrounding the fault, and analyze the paradox using two parameters: the seismic efficiency and the |
elastic power. The results show that the constraint on frictional power from the classic interpretation is incompatible with the accumulating elastic power and the radiated power from earthquake catalogs. We then explore four mechanisms that can resolve this extended paradox. First, stochastic fluctuations of surface heat flow could mask the fault-generated anomaly (we estimate 21% probability). Second, the elastic power accumulating in the region could be overestimated (≥550 MW required). Third, the seismic efficiency—ratio of radiated energy to elastic work—of the SAF could be higher than that of the remaining faults in the region (≥5.8% required). Fourth, the scaled energy—ratio of radiated energy to seismic moment—on the SAF could be lower than on the remaining faults in the region (a factor 5 difference required). In the last three hypotheses, we analyze the interplay of the energy budget on a single fault with the total energy budget of the region. |
Julio de 2024 The differential axial rotation of the solid inner core (IC) is suggested by seismic observations and expected from core dynamics models. A rotation of the IC by an angle α takes its degree 2, order 2 topography (peak-to-peak amplitude δh) out of its gravitational alignment with the mantle. This creates |
a gravity variation of degree 2, order 2 proportional to δh and to α. Here, we use gravity observations from Satellite Laser Ranging, the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On to reconstruct the time-variable S2,2 Stokes coefficient. We show that for δh = 90 m, S2,2 provides upper bounds on α of 0.09°, 0.3°, and 0.4° at periods of ∼4, ∼6, and ∼12 years, respectively. These are overestimates, as our reconstructed S2,2 signal likely remains polluted by hydrology, although viscous relaxation of the IC can permit larger amplitudes. |
Julio de 2024 We investigate error growth from small-amplitude initial condition perturbations, simulated with a recent artificial intelligence-based weather prediction model. From past simulations with standard physically-based numerical models as well as from theoretical considerations it is expected that such small-amplitude initial condition |
perturbations would grow very fast initially. This fast growth then sets a fixed and fundamental limit to the predictability of weather, a phenomenon known as the butterfly effect. We find however, that the AI-based model completely fails to reproduce the rapid initial growth rates and hence would incorrectly suggest an unlimited predictability of the atmosphere. In contrast, if the initial perturbations are large and comparable to current uncertainties in the estimation of the initial state, the AI-based model basically agrees with physically-based simulations, although some deficits are still present. |
Julio de 2024 The emergence of large language models (LLMs), such as ChatGPT, has garnered significant attention, particularly in academic and scientific circles. Researchers, scientists, and instructors hold varying perspectives on the advantages and disadvantages of using ChatGPT for research and teaching purposes. ChatGPT will be used by many scientists going forward for creating content and driving scientific progress. This commentary offers a brief explanation of the fundamental principles |
behind ChatGPT and how it can be applied in the fields of hydrology and other Earth sciences. The article examines the primary applications of this open artificial intelligence tool within these fields, specifically its ability to assist with writing and coding tasks, and highlights both the advantages and concerns associated with using such a model. Moreover, the study brings up some other limitations of the model, and the dangers of potential miss-uses. Finally, we suggest that the academic community adapts its regulations and policies to harness the potential benefits of LLMs while mitigating its pitfalls, including establishing a structure for utilizing LLMs and presenting clear regulations for their implementation. We also outline some specific steps on how to accomplish this structure. |
Junio de 2024 Slow-earthquake signals are generally smaller than or comparable to noise levels at almost all seismological frequencies. Comprehensive detection of these events requires continuous waveforms from many stations, but such data are not always available, even in regions with high slow-earthquake activity. We therefore need a simple and stable detection method that is also applicable to regions with sparse seismic observation networks to truly advance our understanding of slow earthquakes. Here, we utilize the proportionality between the seismic energy rate and seismic moment rate of slow earthquakes to develop a slow-earthquake detection method using broadband |
waveforms from a single station. We introduce the method and estimate its performance using continuous waveform data in Japan. The new method only detects events when the tectonic tremors occur near the station, for example, 89.1% of the detections have corresponding tectonic tremor activities within 60 km, which suggests that the false-positive rate is low. We then apply it to waveform data from the Guerrero, Oaxaca, and Jalisco regions along the Mexican subduction zone. The results for the Guerrero and Oaxaca regions are largely consistent with the geodetic and seismological results from a previous study. We also detect many events in the Jalisco region using a permanent station and provide the first seismological report of long-term slow-earthquake activity in this region. Some of the large-scale slow-earthquake activity that is detected in this study is consistent with the timings of known slow-slip events, which implies that other unknown slow-slip events may have occurred when we detected many events. |
Junio de 2024 Volcano geoengineering is the practice of altering the state of volcanic systems and/or volcanic eruptions to exploit them or mitigate their risk. Although many in the field insist there is little that can be done to mitigate the hazard, past examples of both intentional and inadvertent volcano interventions demonstrate that it is technically feasible to reach volcano plumbing systems or alter atmospheric processes following eruptions. Furthermore, we suggest that economical, political, and environmental pressures may make such interventions more common in the future. If volcano geoengineering ever becomes a discipline, it will need to overcome many safety and ethical concerns, including dealing with uncertainty, deciding on |
philosophical approaches such as a consequentialism or precautionary principle, justice and inequality, military uses, cultural values, and communication. We highlight that while volcano geoengineering has significant potential benefits, the risks and uncertainties are too great to justify its use in the short term. Despite this, because of the potential large benefits to society, we believe there is a strong ethical case to support research into the efficacy and safety of volcano geoengineering for its potential future use. We propose that rigorous governance and regulation of any volcano geoengineering is required to protect against potential risks, to enable potentially valuable and publicly available research (e.g., quantification of efficacy and safety), to ensure that any future policy must be co-created through community engagement, and that volcano geoengineering should only be considered as part of larger mitigation practices. |
Junio de 2024 The detection of preslip, occurring hours to days before a large earthquake, using geodetic measurements has been a major focus in earthquake prediction research. A recent study claims to have detected a preseismic signal interpreted as accelerating slip near the hypocenter of the 2011 great Tohoku-oki earthquake, starting |
approximately 2 hr before the mainshock. This claim is based on a stacking procedure using GNSS (Global Navigation Satellite System) data. However, a follow-up study demonstrated that the signal disappeared when specific GNSS noise was corrected. Here we utilize tiltmeter records, independent on GNSS, to check whether the claimed preseismic signal is detected using a similar stacking procedure. Our results show no acceleration-like deformation from 2 hr before the mainshock. This indicates that no precursory slip exceeded the noise level of the tilt data, and if any preslip occurred, it was less than 5.0 × 1018 Nm in seismic moment. |
Junio de 2024 The application of deep-learning-based seismic phase pickers has surged in recent years. However, the efficacy of these models when applied to monitoring volcano seismicity has yet to be fully evaluated. Here, we first compile a data set of seismic waveforms from various volcanoes globally. We then show that the performances of two widely used deep-learning pickers deteriorate systematically as the earthquakes' frequency |
content decreases. Therefore, the performances are especially poor for long-period earthquakes often associated with fluid/magma movement. Subsequently, we train new models which perform significantly better, including when tested on two data sets where no training data were used: volcanic earthquakes along the Cascadia subduction zone and tectonic low-frequency earthquakes along the Nankai Trough. Our model/workflow can be applied to improve monitoring of volcano seismicity globally while our compiled data set can be used to benchmark future methods for characterizing volcano seismicity, especially long-period earthquakes which are difficult to monitor. |
Junio de 2024 Understanding the generation of damaging, high-frequency ground motions during earthquakes is essential both for fundamental science and for effective hazard preparation. Various theories exist regarding the origin of high-frequency ground motions, including the standard paradigm linked to slip heterogeneity on the rupture plane, and alternative perspectives associated with fault |
complexity. To assess these competing hypotheses, we measure ground motion amplitudes in different frequency bands for 3 = M = 5.8 earthquakes in Southern California and compare them to empirical ground motion models. We utilize a Bayesian inference technique called the Integrated Nested Laplace Approximation (INLA) to identify earthquake source regions that produce higher or lower ground motions than expected. Our analysis reveals a strong correlation between fault complexity measurements and the high-frequency ground motion event terms identified by INLA. These findings suggest that earthquakes on complex faults (or fault networks) lead to stronger-than-expected ground motions at high frequencies. |
Junio de 2024 The South Atlantic Ocean hosts several well-studied volcanic ridges and seamount chains, but the origin of their associated mantle plumes is debated. Reduced seismicity on the southern Mid-Atlantic Ridge (MAR) suggests anomalously ductile thermomechanical conditions at 52°S and 47.5°S. These low seismicity patches extend 120–560 km |
along-axis, and correspond with axial high spreading ridge morphology, geochemical anomalies, and mantle wave speed patterns likely associated with the Shona and Discovery plumes. Bathymetric data show that the northern extent of the Shona swell is associated with increased volcanism, elevated axial bathymetry, and a series of northward-propagating rifts, with the overall swell geometry suggesting a buoyancy flux of 0.4–0.5 Mg s−1. The nearby Bouvet Island may be a product of a branch of the larger Shona plume swell, which has influenced crustal accretion on the southern MAR for the past 24 million years. |
Junio de 2024
Strengthening aquatic resilience to prevent adverse shifts is critical for preserving global freshwater biodiversity and advancing Sustainable Development Goals. Nonetheless, understanding the long-term trends and underlying causes of lake ecosystem resilience at a global scale remains elusive. Here, we employ an innovative framework, integrating satellite-derived water quality indices |
with early warning signals and machine learning techniques, to investigate the dynamics of resilience in 1,049 lakes worldwide during 2000–2018. Our results indicate that 46.7% of lakes are experiencing a significant decline in resilience, particularly since the early 2010s, closely associated with higher human population density and anthropogenic eutrophication. In contrast, most lakes situated in alpine regions exhibit an increase in resilience, probably benefiting from climate warming and wetting. Together, this study provides a novel way to monitor lake resilience and predict undesired transitions, and reveals a widespread erosion in the ability of lakes to withstand stressors associated with global change. |
Junio de 2024 Machine learning-based approaches demonstrate a significant potential in radar quantitative precipitation estimation (QPE) applications. In contrast to conventional methods that depend on local raindrop size distributions, deep learning (DL) can establish an effective mapping from three-dimensional radar observations to ground rain rates. However, the lack of transparency in DL models poses challenges |
toward understanding the underlying physical mechanisms that drive their outcomes. This study aims to develop a DL-based QPE system and provide a physical explanation of radar precipitation estimation process. This research is designed by employing a deep neural network consisting of two modules. The first module is a quantitative precipitation estimation network that has the capability to learn precipitation patterns and spatial distribution from multidimensional polarimetric radar observations. The second module introduces a quantitative precipitation estimation shapley additive explanations method to quantify the influence of each radar observable on the model estimate across various precipitation intensities. |
Junio de 2024 La Palma, Canary Islands, had its largest historical eruption in 2021. From January 2022 to May 2023 there were >2,100 seismic events, primarily at depths ≤20 km, prompting us to update the deformation and modeling study, using interferometric synthetic aperture radar observations and a last generation interpretation tool. We detect |
the evolution of the remaining magmatic body in the SW portion of the island, with arrival of new magma moving into the oceanic crust out to sea, and a pressurized zone in the central-eastern area, at regions of structural weakness. The current source characteristics have some similarities to the early stage dynamics prior to the 2021 eruption. Operational and multidisciplinary studies must continue to monitor either their stabilization or growth and destabilization. The ability to identify magma ascent using only deformation data over short time periods allows us to characterize unrest patterns and provide new insights into volcanic processes. |
Mayo de 2024 Applying machine learning to continuous acoustic emissions, signals previously deemed noise, from laboratory faults and slowly slipping subduction-zone faults, demonstrates hidden signatures are emitted that describe physical details, including fault displacement and friction. However, no evidence currently exists to demonstrate that similar hidden signals occur during seismogenic stick-slip on |
earthquake faults—the damaging earthquakes of most societal interest. We show that continuous seismic emissions emitted during the 2018 multi-month caldera collapse sequence at the Kı̄lauea volcano in Hawai'i contain hidden signatures characterizing the earthquake cycle. Multi-spectral data features extracted from 30 s intervals of the continuous seismic emission are used to train a gradient boosted tree regression model to predict the GNSS-derived contemporaneous surface displacement and time-to-failure of the upcoming collapse event. This striking result suggests that at least some faults emit such signals and provide a potential path to characterizing the instantaneous and future behavior of earthquake faults. |
Mayo de 2024 Very low-frequency earthquakes (VLFs) are characterized by longer source duration and smaller stress drop than regular earthquakes of similar magnitude. Recent studies have shown their frequent correlation with low-frequency earthquakes (LFEs) on shared faults. The underlying source |
processes governing the occurrence of VLFs and their interaction with LFEs remain elusive. Here, we employ a slip-weakening model for slow earthquakes. By comparing the source parameters of simulations and observations, it is suggested that VLFs are slow self-arresting earthquakes that self-terminate within the nucleation patch. Additionally, we adopt a composite model to reproduce the records of the simultaneous occurrences of a VLF and an LFE in the Nankai area. Our results present the possibility that VLFs, LFEs, and regular earthquakes can be distinguished using a unified dynamic framework. |
Mayo de 2024 Earthquake nowcasting has been proposed as a means of tracking the change in large earthquake potential in a seismically active area. The method was developed using observable seismic data, in which probabilities of future large earthquakes can be computed using Receiver Operating Characteristic methods. Furthermore, analysis of the Shannon information content of the earthquake catalogs has been used to show that there is information contained in the catalogs, and that it can vary in time. So an important question remains, where does the information originate? In this paper, we examine this question using stochastic |
simulations of earthquake catalogs. Our catalog simulations are computed using an Earthquake Rescaled Aftershock Seismicity (“ERAS”) stochastic model. This model is similar in many ways to other stochastic seismicity simulations, but has the advantage that the model has only 2 free parameters to be set, one for the aftershock (Omori-Utsu) time decay, and one for the aftershock spatial migration away from the epicenter. Generating a simulation catalog and fitting the two parameters to the observed catalog such as California takes only a few minutes of wall clock time. While clustering can arise from random, Poisson statistics, we show that significant information in the simulation catalogs arises from the “non-Poisson” power-law aftershock clustering, implying that the practice of de-clustering observed catalogs may remove information that would otherwise be useful in forecasting and nowcasting. We also show that the nowcasting method provides similar results with the ERAS model as it does with observed seismicity. |
Mayo de 2024 Seismic faults are known to exhibit a high level of spatial and temporal complexity, and the causes and consequences of this complexity have been the topic of numerous research works in the past decade. In this paper, we investigate the origins and the structure of this complexity by considering a numerical model of laboratory earthquake experiment, where we introduce a fault with homogeneous mechanical properties but allow it to evolve spontaneously to its natural level of complexity. This is achieved by coupling the elastic deformability of the off-fault medium (and therefore allowing for heterogeneous stress fields to develop) |
and the discrete degradation and gouge formation at the fault plane (and therefore allowing for structural heterogeneity to develop). Numerical results show the development of persistent stress, damage, and gouge thickness heterogeneities, with a much larger variability in space than in time. Strong positive correlations are found between these quantities, which suggest a positive feedback between local normal stress and damage rate, only mildly mitigated by the mobility of the granular gouge in the interface. For a wide range of confining stresses, after a sufficient number of seismic cycles, the fault reaches a state of established disorder with a constant roughness, a certain amount of periodicity at the millimetric scale, and a power law decay of the Power Spectral Density at smaller spatial scales. The typical height-to-wavelength ratio of geometrical asperities and the correlations between stress and damage profiles are in good agreement with previous field or lab estimates. |
Mayo de 2024 The significant risk associated with fault reactivation often necessitates slip tendency analyses for effective risk assessment. However, such analyses are challenging, particularly in large areas with limited or absent reliable stress measurements and where the cost of extensive geomechanical analyses or simulations is prohibitive. In this paper, |
we propose a novel approach using a physics- informed neural network that integrates stress orientation and satellite displacement observations in a top-down multi-scale framework to estimate two-dimensional slip tendency analyses even in regions lacking comprehensive stress data. Our study demonstrates that velocities derived from a continental scale analysis, combined with reliable stress orientation averages, can effectively guide models at smaller scales to generate qualitative slip tendency maps. By offering customizable data selection and stress resolution options, this method presents a robust solution to address data scarcity issues, as exemplified through a case study of the South Australian Eyre Peninsula. |
Mayo de 2024 An unexpected Mw7.45 earthquake struck the Noto Peninsula on 1 January 2024, preceded by several long-living earthquake swarms, providing a valuable opportunity to study seismic and aseismic slips, as well as their interactions. We derived coseismic and 19-day postseismic slip distributions by inverting co- and post-seismic displacements from Global |
Navigation Satellite System (GNSS) data. The inverted coseismic slip distribution shows two slip patches, with a maximum slip of ∼4 m. The early postseismic afterslip is 0.1–0.25 m within coseismic slip asperity and 0.1–0.6 m northward of the rupture area. The afterslip within the rupture area is accompanied by numerous aftershocks and coincides with a ∼6 MPa stress drop, suggesting that aftershocks are likely driven by the afterslip. The pattern of poroelastic rebound implies a potential effect of fluid flow on aftershock triggering. This study sheds lights on the intricate interplay between seismic and aseismic processes following large earthquakes. |
Mayo de 2024 The 26 January 1700 CE Cascadia subduction zone earthquake ruptured much of the plate boundary and generated a tsunami that deposited sand in coastal marshes from northern California to Vancouver Island. Although the depositional record of tsunami inundation is extensive in some of these marshes, few sites have been investigated in enough detail to map the inland extent of sand deposition and depict variability in tsunami deposit thickness and grain size. We collected 129 cores in marshes of the Salmon River estuary in Oregon and reanalyzed 114 core logs from a 1987–88 study that mapped the inland extent of circa 1700 CE sandy tsunami |
deposits. The ca. 1700 CE tsunami deposit in the Salmon River estuary is easily recognized in cores ≤1 m deep in which a buried marsh peat is overlain by a well sorted sand bed with a sharp lower contact that thins and fines inland. We use tsunami deposit data and models of sandy tsunami sediment transport (using Delft3D-FLOW) to test 15 rupture models that could represent a ca. 1700 CE earthquake. At least 12–16 m of slip offshore of the Salmon River, which results in 0.8–1.0 m of coastal coseismic subsidence, is required to match the ca. 1700 CE sand deposit's inland extent, which is consistent with models of heterogeneous megathrust slip in ca. 1700 CE. Our methods of detailed tsunami deposit mapping, combined with sediment transport modeling, can be used to test models of megathrust ruptures and their tsunamis to potentially improve earthquake and tsunami hazard assessments. |
Mayo de 2024 In the marginal regions along subduction zones, oceanic plates subducting beneath continental plates produce the largest number of earthquakes on Earth and sometimes devastating tsunamis. The lateral segmentation of earthquakes along the Sumatra subduction zone is well documented. However, the entirely different seismic behaviors among the segments indicate that local structures are key elements controlling coseismic slip |
propagation; in particular, frontal accretionary prism structures are closely associated with tsunami generation. Offshore of Central Sumatra, in the Mentawai segment, large earthquakes nucleated in 2007 and 2010 caused many human casualties and a great deal of property loss. Using seismic reflection data, we show the subsurface structure of accretionary over a significant portion of the frontal wedge that did not rupture during the 2007 earthquake, and this area remains locked. The subsurface deformation structure at the wedge front, which is similar to that in the 2010 Mw7.8 tsunami earthquake rupture zone, suggests the potential for a large tsunami earthquake in the near future. On the other hand, the along-strike variations of the effective basal friction at shallow depth may indicate that different coseismic behaviors are caused by a sudden failure of the deeper seismogenic zone. |
Abril de 2024
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conclusively demonstrates the superiority of machine learning algorithms over traditional statistical methods for earthquake prediction. Additionally, including sampling time in the data sets significantly improves the model's predictive performance. However, it is important to note that the model's predictive performance varies across different hot spring and indicators type, highlighting the importance of identifying optimal indicators for specific scenarios. The model parameters, including the anomaly detection rate (P) and earthquake response time threshold (M), significantly impact the model's predictive capabilities. Therefore, adjustments are needed to optimize the model's performance for practical use. Despite limitations such as the inability to differentiate pre-earthquake anomalies from post-earthquake anomalies and pinpoint the precise location of earthquakes, this study successfully showcases the potential of machine learning algorithms in earthquake prediction, paving the way for further research and improved prediction methods. |
Abril de 2024 Hadean zircons provide a potential record of Earth's earliest subduction 4.3 billion years ago. It remains enigmatic how subduction could be initiated so soon after the presumably Moon-forming giant impact (MGI). Earlier studies found an increase in Earth's core-mantle boundary (CMB) temperature due to the accumulation of the impactor's core, and |
our recent work shows Earth's lower mantle remains largely solid, with some of the impactor's mantle potentially surviving as the large low-shear velocity provinces (LLSVPs). Here, we show that a hot post-impact CMB drives the initiation of strong mantle plumes that can induce subduction initiation ∼200 Myr after the MGI. 2D and 3D thermomechanical computations show that a high CMB temperature is the primary factor triggering early subduction, with enrichment of heat-producing elements in LLSVPs as another potential factor. The models link the earliest subduction to the MGI with implications for understanding the diverse tectonic regimes of rocky planets. |
Abril de 2024 Understanding the impact of human-made structures on groundwater levels is essential, with structures like dams or weirs presenting unique challenges and opportunities for study. The Baekje weir in South Korea presents an interesting case as the weir has undergone full gate opening, which is generally not the case for weirs and reservoirs, providing valuable opportunity for simulating weir removal conditions. The main objectives are investigation of groundwater level fluctuations under various weir operations, distances from the weir, and seasonal variations. The study utilizes observed data that simulates conditions with and without the weir, including scenarios of full gate opening. Multiple machine learning algorithms—Random |
Forest (RF), Artificial Neural Network, Support Vector Regression (SVR), Gradient Boosting, and Extreme Gradient Boosting (XGBoost)—are used to develop accurate groundwater level prediction models. The models' performance is assessed using coefficient of determination, Root mean square error (RMSE), Mean Absolute Error (MAE) indices, and visualized through Taylor diagrams. Results indicate that XGBoost outperforms other models in all three groups during both training and testing phases. Specifically, XGBoost surpasses RF by 2.09% (R2), 5.66% (RMSE), and 10.1% (MAE) in training, and outperforms SVR by 11.2% (R2), 42.0% (RMSE), and 129.2% (MAE) in testing. Additionally, the study generates groundwater level maps, providing a practical tool for managing groundwater systems and informing decision-making in weir operations. This study not only sheds light on the dynamic relationship between weir operations and groundwater levels but also provides actionable insights for effective water management in similar hydrological settings. |
Abril de 2024 Seismic source locations are fundamental to many fields of Earth and planetary sciences, such as seismology, volcanology and tectonics. However, seismic source detection and location are challenging when events cluster closely in space and time with signals tangling together at observing stations, such as they often do in major aftershock sequences. Though emerging algorithms and artificial intelligence (AI) models have made processing high volumes of seismic data easier, their performance is still limited, especially for |
complex aftershock sequences. In this study, we propose a novel approach that utilizes three-dimensional image segmentation—a computer vision technique—to detect and locate seismic sources, and develop this into a complete workflow, Source Untangler Guided by Artificial intelligence image Recognition (SUGAR). In our synthetic and real data tests, SUGAR can handle complex, energetic earthquake sequences in near real time better than skillful analysts and other AI and non-AI based algorithms. We apply SUGAR to the 2016 Kaikōura, New Zealand sequence and obtain five times more events than the analyst-based GeoNet catalog. The improved aftershock distribution illuminates a continuous fault system with extensive fracture zones beneath the segmented, discontinuous surface ruptures. Our method has broader applicability to non-earthquake sources and other time series image data sets. |
Abril de 2024 Deep Martian aquifers harboring liquid water could hold vital insights for current and past habitability. We show that with seismo-electric interface responses (IRs) we can quantitatively characterize subsurface water on Mars. Full-waveform simulations and sensitivity analyses across diverse Martian aquifer scenarios demonstrate the technique's effectiveness. In contrast to how seismo-electric signals often appear on Earth, Mars' desiccated surface naturally removes co-seismic fields and exposes useful IRs that allow us to characterize several aquifer properties. Changing |
the aquifer depth, thickness, or quantity changes the IR arrival times or shape: aquifer depth is a strong control on evanescent IRs, thickness affects the relative timing of IRs, and increasing the number of aquifers introduces more dipole sources to the waveform. Other factors, such as aquifer saturation, chemistry, and salinity, strongly affect IR amplitude but have minimal or no effect on waveform shape. Notably, for a deep low-porosity aquifer, the salinity and brine chemistry (perchlorate vs. chloride) are the strongest controls on signal amplitude. Analyzing the effects of epicentral distance shows that radiating and evanescent IRs separate at large source-receiver offset, allowing analyses of both signals and accurate event distance derivation. From this numerical investigation of the sensitivity of IRs to deep Martian aquifers, we anticipate future analyses of electromagnetic data from the InSight lander or future missions to Mars and other planets |
Abril de 2024 Estimating an earthquake's magnitude and location may not be necessary to predict shaking in real time; instead, wavefield-based approaches predict shaking with few assumptions about the seismic source. Here, we introduce GRAph Prediction of Earthquake Shaking (GRAPES), a deep learning |
model trained to characterize and propagate earthquake shaking across a seismic network. We show that GRAPES’ internal activations, which we call “seismic vectors”, correspond to the arrival of distinct seismic phases. GRAPES builds upon recent deep learning models applied to earthquake early warning by allowing for continuous ground motion prediction with seismic networks of all sizes. While trained on earthquakes recorded in Japan, we show that GRAPES, without modification, outperforms the ShakeAlert earthquake early warning system on the 2019 M7.1 Ridgecrest, CA earthquake. |
Abril de 2024
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Marzo de 2024 Volcanic ash provides information that can help understanding the evolution of volcanic activity during the early stages of a crisis and possible transitions toward different eruptive styles. Ash consists of particles from a range of origins within the volcanic system and its analysis can be indicative of the processes driving the eruptive activity. However, classifying ash particles into different types is not straightforward. Diagnostic observations for particle classification are not standardized and vary across samples. Here we explore the use of machine learning (ML) to improve the classification accuracy and reproducibility. We use a curated database of ash particles (VolcAshDB) to optimize and train two ML-based |
models: Extreme Gradient Boosting (XGBoost) that uses the measured physical attributes of the particles, from which predictions are interpreted by the SHapley Additive exPlanations (SHAP) method, and a Vision Transformer (ViT) that classifies binocular, multi-focused, particle images. We find that the XGBoost has an overall classification accuracy of 0.77 (macro F1-score), and specific features of color (hue_mean) and texture (correlation) are the most discriminant between particle types. Classification using the particle images and the ViT is more accurate (macro F1-score of 0.93), with performances varying from 0.85 for samples of dome explosions, to 0.95 for phreatic and subplinian events. Notwithstanding the success of the classification algorithms, the training dataset is limited in number of particles, ranges of eruptive styles, and volcanoes. Thus, the algorithms should be tested further with additional samples, and it is likely that classification for a given volcano is more accurate than between volcanoes. |
Marzo de 2024 A coherent portrayal of global bathymetry requires that depths are inferred between sparsely distributed direct depth measurements. Depths can be interpolated in the gaps using alternate information such as satellite-derived gravity and a mapping from gravity to depth. We designed and trained a neural network on a collection of 50 million depth soundings to predict bathymetry globally using gravity anomalies. We find the best result is achieved by pre-filtering depth and gravity in |
accordance with isostatic admittance theory described in previous predicted depth studies. When training the model, if the training and testing split is a random partition at the same resolution as the data, the training and testing sets will not be independent, and model misfit is underestimated. We solve this problem by partitioning the training and testing set with geographic bins. Our final predicted depth model improves on old predicted depth model RMSE by 16%, from 165 to 138 m. Among constrained grid cells, 80% of the predicted values are within 128 m of the true value. Improvements to this model will continue with additional depth measurements, but predictions at higher spatial resolution, being limited by upward continuation of gravity, should not be attempted with this method. |
Marzo de 2024 Given the robust nonlinear regression capabilities of Artificial Intelligence (AI) technology, its commendable performance in numerous geophysical tasks is expected. Yet, AI technology suffers from (a) its “black box” nature and (b) the fact that some complicated artificial neural networks (ANNs) claiming superior performance do not surpass some simple geophysical models that clearly describe the underlying physical processes. Numerous reports rely on standard machine learning metrics, often using a spatially uniform Poisson (SUP) distribution as their reference. A good performance just means that the artificial neural network (ANN) outperforms this basic reference, potentially offering little novelty to the scientific community. Worse, this can lead to spurious inference. We demonstrate this by using |
the monthly average human-made Nighttime Light Map and the cumulative energy of earthquakes in various space-time units as inputs for an Long short-term memory model. The goal is to predict earthquakes with a magnitude of M ≥ 5.0 across the entire Chinese Mainland. With the SUP reference model, the ANN concludes that human-made Nighttime Light possesses substantial earthquake prediction capability. This is evidently flawed reasoning. We show that this stems from the poor reference model and this spurious inference disappears when using a better benchmark consisting of a spatially varying Poisson (SVP) model informed from statistical seismology. This is implemented by weighting the punishments/rewards of our ANN associated with failed/successful predictions by prior probabilities provided by the stronger SVP model. Scores obtained with the time-space Molchan diagram demonstrate the strong performance improvement obtained by training ANN with a better reference model. |
Marzo de 2024 We analyze nearest-neighbor proximities of earthquakes in California based on the joint distribution (T, R) of rescaled time T and rescaled distance R between pairs of earthquakes (Zaliapin & Ben-Zion, 2013a, https://doi.org/10.1002/jgrb.50179), using seismic catalogs from several regions and several catalogs for the San Jacinto Fault Zone (SJFZ). The study aims to identify informative modes in nearest-neighbor diagrams beyond the general background and clustered modes, and to assess seismic catalogs derived by different methods. The results show that earthquake clusters with large and small-to-medium mainshocks have approximately |
diagonal and horizontal (T, R) distributions of the clustered mode, respectively, reflecting different triggering distances of mainshocks. Earthquakes in the creeping section of San Andreas Fault have a distinct “repeaters mode” characterized by very large rescaled times T and very small rescaled distances R, due to nearly identical locations of repeating events. Induced seismicity in the Geysers and Coso geothermal fields follow mostly the background mode, but with larger rescaled times T and smaller rescaled distances R compared to tectonic background seismicity. We also document differences in (T, R) distributions of catalogs constructed by different techniques (analyst-picks, template-matching and deep-learning) for the SJFZ, and detect a mode with very large R and small T in the template-matching and deep-learning based catalogs. This mode may reflect dynamic triggering by passing waves and/or catalog artifacts. |
Marzo de 2024 All instrumented basaltic caldera collapses have generated Mw > 5 very long period earthquakes. However, previous studies of source dynamics have been limited to lumped models treating the caldera block as rigid, leaving open questions related to how ruptures initiate and propagate around the ring fault, and the seismic expressions of those dynamics. We present the first 3D numerical model capturing the nucleation and propagation of ring fault rupture, the mechanical coupling to the underlying viscoelastic magma, and the associated seismic wavefield. We demonstrate that seismic radiation, neglected in previous models, acts as a damping mechanism reducing coseismic slip by up to half, with effects most pronounced for large |
magma chamber volume/ring fault radius or highly compliant crust/compressible magma. Viscosity of basaltic magma has negligible effect on collapse dynamics. In contrast, viscosity of silicic magma significantly reduces ring fault slip. We use the model to simulate the 2018 Kı̄lauea caldera collapse. Three stages of collapse, characterized by ring fault rupture initiation and propagation, deceleration of the downward-moving caldera block and magma column, and post-collapse resonant oscillations, in addition to chamber pressurization, are identified in simulated and observed (unfiltered) near-field seismograms. A detailed comparison of simulated and observed displacement waveforms corresponding to collapse earthquakes with hypocenters at various azimuths of the ring fault reveals a complex nucleation phase for earthquakes initiated on the northwest. Our numerical simulation framework will enhance future efforts to reconcile seismic and geodetic observations of caldera collapse with conceptual models of ring fault and magma chamber dynamics. |
Febrero de 2024 We investigate similarities and differences between natural and simulated slow earthquakes using nonlinear dynamical system tools. We use spatio-temporal slip potency rate data derived from Global Navigation Satellite System (GNSS) position time series in the Cascadia subduction zone and numerical simulations intended to reproduce their pulse-like behavior and scaling laws. We provide . |
metrics to evaluate the accuracy of simulations in mimicking slow earthquake dynamics. We investigate the influence of spatio-temporal coarsening as well as observational noise. Despite the use of many degrees of freedom, numerical simulations display a surprisingly low average dimension, akin to natural slow earthquakes. Instantaneous dynamical indices can reach large values (>10) instead, and differences persist between numerical simulations and natural observations. We propose to use the suggested metrics as an additional tool to narrow the divergence between slow earthquake observations and dynamical simulations |
Febrero de 2024 Pressure and flow estimation in water distribution networks (WDNs) allows water management companies to optimize their control operations. For many years, mathematical simulation tools have been the most common approach to reconstructing an estimate of the WDNs hydraulics. However, pure physics-based simulations involve several challenges, for example, partially observable data, high uncertainty, and extensive manual calibration. Thus, data-driven approaches have gained traction |
to overcome such limitations. In this work, we combine physics-based modeling and graph neural networks (GNN), a data-driven approach, to address the pressure estimation problem. Our work has two main contributions. First, a training strategy that relies on random sensor placement making our GNN-based estimation model robust to unexpected sensor location changes. Second, a realistic evaluation protocol that considers real temporal patterns and noise injection to mimic the uncertainties intrinsic to real-world scenarios. As a result, a new state-of-the-art model, GAT with Residual Connections, for pressure estimation is available. Our model surpasses the performance of previous studies on several WDNs benchmarks, showing a reduction of absolute error of ≈40% on average. |
Enero de 2024 Volcano geoengineering is the practice of altering the state of volcanic systems and/or volcanic eruptions to exploit them or mitigate their risk. Although many in the field insist there is little that can be done to mitigate the hazard, past examples of both intentional and inadvertent volcano interventions demonstrate that it is technically feasible to reach volcano plumbing systems or alter atmospheric processes following eruptions. Furthermore, we suggest that economical, political, and environmental pressures may make such interventions more common in the future. If volcano geoengineering ever becomes a discipline, it will need to overcome many safety and ethical concerns, including dealing with uncertainty, deciding on |
philosophical approaches such as a consequentialism or precautionary principle, justice and inequality, military uses, cultural values, and communication. We highlight that while volcano geoengineering has significant potential benefits, the risks and uncertainties are too great to justify its use in the short term. Despite this, because of the potential large benefits to society, we believe there is a strong ethical case to support research into the efficacy and safety of volcano geoengineering for its potential future use. We propose that rigorous governance and regulation of any volcano geoengineering is required to protect against potential risks, to enable potentially valuable and publicly available research (e.g., quantification of efficacy and safety), to ensure that any future policy must be co-created through community engagement, and that volcano geoengineering should only be considered as part of larger mitigation practices. |
Enero de 2024 This study aims to explain the nonuniform earthquake pattern along the Cascadia Subduction Zone. In particular, we investigate the relationship between the tectonic features of the subducting oceanic Juan de Fuca slab and the onshore seismicity pattern. We have integrated multiple geophysical data sets toward three general objectives. The first study intends to study variations in physical properties along three 2-dimensional models through regions of different seismicities that combine public gravity, magnetic, and seismic data sets. These models reveal multiple zones of decreased crustal density that we interpret as |
regions of weaker oceanic crust. The second objective is to delineate major tectonic features by performing spatial analysis of potential fields. The overall methodology comprises gravity and magnetic data filtering, followed by lineaments mapping and cross-referencing interpretation with available seismic reflection data. This process allows delineating zones of crustal weakness by extrapolating outside our three 2-D models. We also map multiple seamounts that appear to cluster along identified zones of weaker crust. Third, we investigate the relationship between the mapped tectonic elements, namely the zones of weak crust with accompanying seamounts, and the observed seismicity trends within the subducted slab. The alignment between those suggests that mapped weak crust zones and associated seamounts may have an influence on the overall subduction process. As more of these structures are heading toward the Washington portion of the margin than to the Oregon portion, more earthquakes are observed in the north than in the south. |
Enero de 2024 In active volcanoes, magmatic fluids rising toward the surface may interact with shallow waters, thereby forming hydrothermal systems that record variations in magma dynamics at depth. Here, we report on a data set comprising the chemical and isotopic composition of thermal waters and dissolved gases from Stromboli Island (Aeolian |
Volcanic Arc, Southern Italy) that spans 14 years (2004–2018) of continuous observations. We show that the shallow thermal aquifer of Stromboli results from variable mixing between meteoric water, seawater and magmatic fluids. Gas-water-rock interactions occur, which induce a large spectrum of variation in both water and gas chemistry. These shallow processes do not affect the 3He/4He of helium dissolved in thermal waters, which records a magmatic signature that varies in response to changes in magma supply at depth. We show that in periods of more intense volcanic activity, the helium isotopic composition of thermal waters approaches that of the gas emitted from the magma residing |
Diciembre de 2023 Latent heat (LH) released from precipitation during the water phase change process is the primary energy source driving atmospheric circulation. Current satellite LH retrieval algorithms are mainly physical-based or lookup table-based. In this study, a fully connected neural network LH algorithm (FCNH) was developed and tested by weather research and forecasting model (WRF) simulations and global precipitation measurement (GPM) satellite observations. FCNH uses three types of modules: feature representation, feature fusion, and regression. Using satellite observable vertical derivation of precipitation rate (urn:x-wiley:2169897X:media:jgrd58973:jgrd58973-math- |
0001) and air temperature (T) as inputs into FCNH achieved the best LH retrieval performance; increasing the number of input variables covering environmental or precipitation characteristics degraded the retrieval accuracy. Compared to the WRF simulated true LH, the FCNH retrieval captured the main features of horizontal and vertical structures with high correlation coefficients and showed improved performance over the associated physical-based LH algorithm on the same inputs. The FCNH algorithm can alleviate the overestimation of cooling near the surface and the overestimation of positive heating in the mixing layer. The LH retrievals from FCNH and the other three algorithms using inputs of GPM observations were compared, and all achieved basically consistent results. This study is the first attempt to use an artificial neural network method for satellite remote sensing of LH inside precipitation clouds. It promotes understanding of the learning efficiency, accuracy, and limitations of using a fully connected neural network to retrieve LH. |
Diciembre de 2023 The emergence of large language models (LLMs), such as ChatGPT, has garnered significant attention, particularly in academic and scientific circles. Researchers, scientists, and instructors hold varying perspectives on the advantages and disadvantages of using ChatGPT for research and teaching purposes. ChatGPT will be used by many scientists going forward for creating content and driving scientific progress. This commentary offers a brief explanation of the fundamental principles |
behind ChatGPT and how it can be applied in the fields of hydrology and other Earth sciences. The article examines the primary applications of this open artificial intelligence tool within these fields, specifically its ability to assist with writing and coding tasks, and highlights both the advantages and concerns associated with using such a model. Moreover, the study brings up some other limitations of the model, and the dangers of potential miss-uses. Finally, we suggest that the academic community adapts its regulations and policies to harness the potential benefits of LLMs while mitigating its pitfalls, including establishing a structure for utilizing LLMs and presenting clear regulations for their implementation. We also outline some specific steps on how to accomplish this structure. |
Diciembre de 2023 The population of Santiago, Chile, experiences air pollution above global health guidelines that is attributable in part to large anthropogenic emissions. This is compounded by geographic features and meteorological conditions that are prone to pollution accumulation as well as secondary pollution production. In recent years, there have been improvements in air quality; however, the future of air pollution in Santiago remains unclear due to its growing population and increased vehicle use. Mitigation efforts can be supported by characterizing sources of air pollution and estimating how changes in emissions could affect air quality in future years. In this study, we conduct simulations using a chemical transport |
model (GEOS-Chem) and perform adjoint calculations to characterize the relationship between health impacts associated with exposure to PM2.5, O3, and NO2 and anthropogenic emissions. We incorporate model updates in a new nested domain simulation over Central South America including local and regional anthropogenic emissions inventories for Chile. We estimate that 2,490 (1,360, 4,060) PM2.5- and O3-related premature deaths and 5,350 (1,320, 11,330) NO2-related new pediatric asthma cases were associated with pollution exposure in Santiago in 2015 and that a majority of these health impacts were attributable to anthropogenic emissions. We identify emissions from transportation, energy generation, and residential combustion as the leading contributors to these health impacts. Additionally, we estimate that Chile's commitment to attain carbon neutrality by 2050 could result in benefits in Santiago of 3,230 (1,240, 7,160) avoided deaths and 2,590 (640, 5,500) avoided pediatric asthma cases in 2050 compared to business-as-usual emissions. |
Diciembre de 2023 The role of lithospheric heterogeneities, presence or absence of melt, local and regional stresses, and gravitational potential energy in strain localization in continental rifts remains debated. We use new seismic and geodetic data to identify the location and orientation of the modern Nubia-Somalia plate boundary in the 300-km-wide zone between the southern Main Ethiopian Rift (MER) and Eastern Rift (ER) across the Mesozoic Anza rift in the Turkana Depression. This region exhibits lithospheric heterogeneity, 45 Ma-Recent magmatism, and more than 1,500 m of base-level elevation change, enabling the assessment of strain localization mechanisms. We relocate 1716 earthquakes using |
a new 1-D velocity model. Using a new local magnitude scaling with station corrections, we find 1 ≤ ML ≤ 4.5, and a b-value of 1.22 ± 0.06. We present 59 first motion and 3 full moment tensor inversions, and invert for opening directions. We use complementary geodetic displacement vectors and strain rates to describe the geodetic strain field. Our seismic and geodetic strain zones demonstrate that only a small part of the 300 km-wide region is currently active; low elevation and high-elevation regions are active, as are areas with and without Holocene magmatism. Variations in the active plate boundary's location, orientation and strain rate appear to correspond to lithospheric heterogeneities. In the MER-ER linkage zone, a belt of seismically fast mantle lithosphere generally lacking Recent magmatism is coincident with diffuse crustal deformation, whereas seismically slow mantle lithosphere and Recent magmatism are characterized by localized crustal strain; lithospheric heterogeneity drives strain localization. |
Diciembre de 2023 In active volcanoes, magmatic fluids rising toward the surface may interact with shallow waters, thereby forming hydrothermal systems that record variations in magma dynamics at depth. Here, we report on a data set comprising the chemical and isotopic composition of thermal waters and dissolved gases from Stromboli Island (Aeolian Volcanic Arc, Southern Italy) that spans 14 years (2004–2018) of continuous observations. We show that the shallow thermal aquifer of Stromboli results |
from variable mixing between meteoric water, seawater and magmatic fluids. Gas-water-rock interactions occur, which induce a large spectrum of variation in both water and gas chemistry. These shallow processes do not affect the 3He/4He of helium dissolved in thermal waters, which records a magmatic signature that varies in response to changes in magma supply at depth. We show that in periods of more intense volcanic activity, the helium isotopic composition of thermal waters approaches that of the gas emitted from the magma residing at 7–10 km depth. Investigation of hydrothermal waters at active volcanoes is a promising tool to examine magmatic fluids and their shallow circulation, as well as to evaluate the state of activity of a volcano, particularly when summit areas are inaccessible. |
Diciembre de 2023 Someday, a great earthquake will erupt from the San Andreas fault, which cuts through Southern California from Los Angeles to San Francisco. Geologic records make it clear. It has happened, and it will happen again. |
violently than official hazard models suggest. The good news for Angelenos stems from five rocks balanced precariously on top of other rocks in Lovejoy Buttes, a place in northern Los Angeles County that sits just 15 kilometers from the fault. By dating when the rocks first became fragile and analyzing their structures to assess the maximum shaking they could withstand, the researchers could test official predictions against thousands of years of earthquakes. Those predictions have been found wanting, says Anna Rood, a seismic hazard scientist at the Global Earthquake Model Foundation who led the work, which is accepted in Seismological Research Letters. “The hazard estimates are totally inconsistent with these precariously balanced rock data..." |
Diciembre de 2023 On 6 February 2023, a local tsunami was recorded in the southeastern Mediterranean Sea following the Mw 7.7 Turkey–Syria inland strike-slip earthquake. Due to the lack of underwater observation, the tsunami generation mechanism remains mysterious. To understand the source mechanisms, we analyzed the tsunami waveforms of four nearby tide gauges and located possible |
sources using a backward tsunami ray tracing approach. We then conducted forward numerical modelings for a range of possible source parameters. We show that there were probably two tsunami sources, inside and outside Iskenderun Bay, which may be related to thick coastal sediments. A source inside the Bay with a characteristic length of 7 km produced dominant periods of 10–30 min with negative initial motion, possibly generated by a landslide. Another source of 6 km length outside the Bay produced dominant periods of 2–10 min with positive initial motions, possibly related with liquefaction. |
Diciembre de 2023 This study proposes to construct a model using random forest method, an efficient machine learning-based method, to predict the spatial structure and temporal evolution of the sea surface temperature (SST) cooling induced by northwest Pacific tropical cyclones (TCs), a process of the so-called wind pump. The predictors in use include 12 |
predictors related to TC characteristics and pre-storm ocean conditions. The model is shown to skillfully predict the spatiotemporal evolutions of the cold wake generated by TCs of different intensity groups, and capture the cross-case variance in the observed SST response. Another model is further built based on the same method to assess the relative importance of the 12 predictors in determining the magnitude of the maximum cooling. Computations of feature scores of those predictors show that TC intensity, translation speed and size, and pre-storm mixed layer depth and SST dominate, depending on the area where the cooling is considered. |
Diciembre de 2023 Co-seismic Ionospheric disturbances (CID, or “ionoquakes”) are disturbances in the electron density or total electron content (TEC) of the ionosphere, produced by the ground motion due to earthquakes. Usually, ionoquakes are detected in the near-epicentral region within 8–10 min after an earthquake onset time. In this work, we present a new methodology that allows to estimate the CID arrival time based on determining the CID peak time |
in TEC measurements with respect to the peak time of seismic waves registered by the nearest seismic station. Our methodology also allows to understand the altitude of GNSS detection that otherwise remains ambiguous. We apply the newly developed techniques to detect CID signatures associated with three large earthquakes: the 2015 Illapel, the 2014 Iquique, and the 2011 Sanriku-Oki. We show that for these events, the CID arrive 250–430 s after the time of the seismic wave peak, or 350–700 s after the earthquake onset time. Our analysis show that the first CID are detected at the altitudes of 150–180 km (the Sanriku earthquake) and of 200–300 km (the Illapel and the Iquique earthquakes). The disturbances represent high-frequency acoustic oscillations that propagate with a horizontal speed faster than 0.75 km/s. |
Diciembre de 2023 Following laboratory experiments and friction theory, slow slip events and seismicity rate accelerations observed before mainshocks are sometimes interpreted as evidence of a nucleation phase. However, such precursory observations still remain scarce and are associated with different time and length scales, raising doubts about their actual preparatory nature. We study the 2017 Valparaiso Mw = 6.9 earthquake, which was preceded by aseismic slip accompanied by an intense seismicity, suspected to reflect its nucleation phase. We complement previous observations, which have focused only on precursory activity, with a continuous investigation of seismic and aseismic processes from the foreshock sequence to the post- |
mainshock phase. By building a high-resolution earthquake catalog and searching for anomalous seismicity rate increases compared to aftershock triggering models, we highlight an over-productive seismicity starting within the foreshock sequence and persisting several days after the mainshock. Using repeating earthquakes and high-rate GPS observations, we highlight a transient aseismic perturbation starting 1-day before the first foreshock and continuing after the mainshock. The estimated slip rate over time is lightly impacted by large magnitude earthquakes and does not accelerate toward the mainshock. Therefore, the unusual seismic and aseismic activity observed during the 2017 Valparaiso sequence might be interpreted as the result of a slow slip event starting before the mainshock and continuing beyond it. Rather than pointing to a possible nucleation phase of the 2017 Valparaiso mainshock, the identified slow slip event acts as an aseismic loading of nearby faults, increasing the seismic activity, and thus the likelihood of a large rupture. |
Noviembre de 2023 For decades, the Epidemic-Type Aftershock Sequence (ETAS) model has been the most popular way of forecasting earthquakes over short time spans (days/weeks). It is formulated mathematically as a point process, a general class of statistical model describing the random occurrence of points in time. Recently the machine learning community have used neural networks to make point processes more expressive and titled them neural point processes. In this study we investigate |
whether a neural point process can compete with the ETAS model. We find that the two models perform similarly on computer simulated data; however, the neural model is much faster with large data sets and is not hindered if there is missing data for smaller earthquakes. Most earthquake catalogs contain missing data due to varying capability in our detection methods, therefore we need models that are robust to this missingness. We then find that the neural model outperforms ETAS on a new catalog for the 2016–2017 Central Apennines earthquake sequence, which through machine learning detection contains thousands of previously undetected small magnitude events. We argue that some of this improvement can in fact be explained by missing data. These results present neural point processes as an encouraging competitor in earthquake forecasting. |
Noviembre de 2023 Co-seismic Ionospheric disturbances (CID, or “ionoquakes”) are disturbances in the electron density or total electron content (TEC) of the ionosphere, produced by the ground motion due to earthquakes. Usually, ionoquakes are detected in the near-epicentral region within 8–10 min after an earthquake onset time. In this work, we present a new methodology that allows to estimate the CID arrival time based on determining the CID peak time |
in TEC measurements with respect to the peak time of seismic waves registered by the nearest seismic station. Our methodology also allows to understand the altitude of GNSS detection that otherwise remains ambiguous. We apply the newly developed techniques to detect CID signatures associated with three large earthquakes: the 2015 Illapel, the 2014 Iquique, and the 2011 Sanriku-Oki. We show that for these events, the CID arrive 250–430 s after the time of the seismic wave peak, or 350–700 s after the earthquake onset time. Our analysis show that the first CID are detected at the altitudes of 150–180 km (the Sanriku earthquake) and of 200–300 km (the Illapel and the Iquique earthquakes). The disturbances represent high-frequency acoustic oscillations that propagate with a horizontal speed faster than 0.75 km/s. |
Octubre de 2023 Tsunami early warning requires the fast and reliable estimation of an earthquake magnitude provided by Earthquake Early Warning (EEW) systems. EEW systems are currently limited by the propagation speed of P-waves, which rely on as natural information carriers. Even more problematic, EEW systems based on the first seismic arrivals tend to saturate with earthquake magnitude, and can become unreliable for magnitudes above 8. The recent discovery of Prompt Elasto-Gravity Signals (PEGS), which comprise gravitational changes |
generated by earthquakes, has raised hope to overcome these limitations because they travel at the speed of light, much faster than P-waves. We use PEGS to re-train the previously developed deep learning model PEGSNet to track the magnitude evolution of big earthquakes in the Chilean subduction zone, historically affected by tsunamis. Given the scarcity of big earthquakes, we simulate signals to train the model using synthetic sources and the seismic stations available in 2010 and 2021, augmented with empirical noise recorded by those stations. PEGSNet tracks the moment release 90 s after the origin time. The performance of PEGSNet is limited by the seismic network configuration, the number of stations and the level of noise in the data, but could be useful for tsunami warning. |
Octubre de 2023 The main earthquake of Mw 7.8 occurred at 01:17:34 UTC and was followed by the three notable (Mw > 5.5) aftershocks within the next 18 min. Then, ∼9 hr later, the biggest aftershock with magnitude Mw 7.5 and a Mw 6.0 earthquake occurred to the north-east from the first main earthquake. In this work, we use data of ground-based Global Navigation Satellite Systems (GNSS) receivers in Turkey, Israel and Cyprus to analyze the ionospheric response to this series of earthquakes. We separate these events in |
two groups: the first sequence of earthquakes (at 01–02 UTC) and the second sequence (at 10–11 UTC). For the first sequence, we observe a clear N-shaped total electron content (TEC) response after the Mw 7.8 mainshock earthquake and Mw 6.7 aftershock, and a smaller TEC disturbance that is, most likely, caused by the Mw 5.6 earthquake. The latter is now the smallest earthquake detected by using ionospheric GNSS data. The co-seismic ionospheric disturbances (CSID) propagated from the epicentral area in the south-west direction with velocities of about 750–830 m/s. For the second sequence, we observed the response to the Mw 7.5 aftershock earthquake and the Mw 6.0 aftershock. The CSID propagated both to the south-west and the north-west to the epicentral area, with velocities of about 950–1,100 m/s. |
Septiembre de 2023 Seismology is witnessing explosive growth in the diversity and scale of earthquake catalogs. A key motivation for this community effort is that more data should translate into better earthquake forecasts. Such improvements are yet to be seen. Here, we introduce the Recurrent Earthquake foreCAST (RECAST), a deep-learning model based on recent developments in neural temporal point processes. |
The model enables access to a greater volume and diversity of earthquake observations, overcoming the theoretical and computational limitations of traditional approaches. We benchmark against a temporal Epidemic Type Aftershock Sequence model. Tests on synthetic data suggest that with a modest-sized data set, RECAST accurately models earthquake-like point processes directly from cataloged data. Tests on earthquake catalogs in Southern California indicate improved fit and forecast accuracy compared to our benchmark when the training set is sufficiently long (>104 events). The basic components in RECAST add flexibility and scalability for earthquake forecasting without sacrificing performance. |
Septiembre de 2023 Maar-diatreme volcanoes are small volcanic structures with a funnel-shaped crater surrounded by a tephra-ring. They are usually formed by the explosive phreatomagmatic eruptions when groundwater comes into the contact with magma. We focus on such a structure in the geodynamically active western Eger Rift (Czech Republic) and present results from multidisciplinary geophysical investigation calibrated by drilling in the newly discovered Pleistocene Bažina maar. We evaluated morphological (LiDAR-based DEM) data and confirmed the existence of a maar-diatreme structure by combined geophysical methods. In the map view, they revealed circular negative gravity anomaly, funnel-shape low-resistivity anomaly, and |
strong magnetic anomaly. These results allowed for the optimal location of two boreholes in the maar crater, which evinced its contact with country rocks and lithologies of the maar-diatreme filling. The drilling revealed coherent volcanic rocks and volcaniclastic deposits, moreover, it revealed a presence of a pyroclastic cone with the olivine nephelinite feeding conduit. Further investigations disclosed maar structure and subsequent pyroclastic cone(s) with several generations of eruptions and systematic decrease of water influence on the eruption style. Different eruption styles suggest a unique evolution of two volcanoes, one inside the other. The age of the Bažina maar eruption, estimated from the reverse polarity of the detected magnetic anomaly, implies that the effusion and solidification of the lava during the eruption must be older than 0.78 Ma (Pleistocene). This points to an active volcanism in the western Eger Rift in a span of 0.5 Ma, where Bažina represents the oldest (maybe opening) phase. |
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