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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Severe Strain‐Induced Olivine‐Ringwoodite Transformation at Room Temperature: Key to Enigmas of Deep‐Focus Earthquake

Deep‐focus earthquakes at 350–660 km are presumably caused by olivine‐spinel phase transformation (PT). This cannot, however, explain the observed high seismic strain rate, which requires PT to complete within seconds, while metastable olivine does not transform for over a million years. Recent theory quantitatively describes how severe plastic deformations (SPD) can solve this dilemma but lacking experimental proof. Here, we introduce dynamic rotational diamond anvil cell with rough diamond anvils to impose SPD on San Carlos olivine. While olivine never transformed to spinel at room temperature, we obtained reversible olivine‐ringwoodite PT under SPD at 15–28 GPa within tens of seconds. The PT pressure reduces with increasing dislocation density, microstrain, plastic strain, and decreasing crystallite size. Results demonstrate a new strain‐induced PT mechanism compared to a pressure/temperature‐induced one. Combined with SPD during olivine subduction, this mechanism can accelerate olivine‐ringwoodite PT from millions of years to timescales relevant to earthquakes.

Lin, F. [Iowa State Univ., Ames, IA (United States↗
DOE OSTI2020

Probing Slow Earthquakes With Deep Learning

Abstract Slow earthquakes may trigger failure on neighboring locked faults that are stressed sufficiently to break, and slow slip patterns may evolve before a nearby great earthquake. However, even in the clearest cases such as Cascadia, slow earthquakes and associated tremor have only been observed in intermittent and discrete bursts. By training a convolutional neural network to detect known tremor on a single seismic station in Cascadia, we isolate and identify tremor and slip preceding and following known larger slow events. The deep neural network can be used for the detection of quasi‐continuous tremor, providing a proxy that quantifies the slow slip rate. Furthermore, the model trained in Cascadia recognizes tremor in other subduction zones and also along the San Andreas Fault at Parkfield, suggesting a universality of waveform characteristics and source processes, as posited from experiments and theory.

58 GEOSCIENCES↗
DOE OSTI2025

Differential Seismic Phase Detection Probability as a Potential Discriminant of Explosions and Earthquakes

Deep learning models trained to estimate the probability of seismic P and S phases are rapidly expanding the scale of local event detections. Here, we evaluate the potential for deep learning model output phase detection probabilities to contribute to event‐type classification, particularly discrimination of single‐fired borehole explosions and earthquakes at local distances (<300 km). Motivated by the empirical success of P/S amplitude ratios, we consider the difference between P and S pick probability output from previously developed phase detection models, P prob −S prob ⁠, as a discriminant. Test data include M L ∼1–4 earthquakes and explosions observed by common seismographs in ten geologically diverse localities. Depending on the picking model and training data, binary classification using P prob −S prob with at least three stations can achieve approximately equivalent classification accuracy as P/S amplitude ratios without requiring any customization. Joint classification with P/S and P prob −S prob improves accuracy for most quality control scenarios. Pick probabilities are an efficient attribute to consider in explosion discrimination because they can be automated byproducts of event detection. They avoid the binary choice of picking or not picking weakly visible S waves common to explosions.

Duan, Chenglong [Rice Univ., Houston, TX (United S↗
DOE OSTI2024

Transformational faulting in Mn 2 GeO 4 from olivine to wadsleyite structure: Implications for physical mechanism of deep-focus earthquakes

High-pressure and temperature deformation experiments interfaced with acoustic emission (AE) monitoring have been conducted to study transformational faulting in Mn 2 GeO 4 olivine, which transforms to the β phase, isostructural to wadsleyite. Metastable Mn 2 GeO 4 olivine exhibits a marked embrittlement behavior at temperatures between 800 and 1100 K, emitting numerous AEs. At each temperature, brittle deformation is characterized by a two-stage process: (1) a “preparation” stage with numerous diffusedly located low-magnitude AEs and large b values (>2), and (2) a failure stage where larger-magnitude AEs form a planar distribution with b values about 1. Microstructure analysis reveals extensive kink band development in olivine grains in the recovered samples. Kink band boundaries (KBBs), with a typical thickness of ∼100 nm, are filled with a nanometric β-Mn 2 GeO 4 “gouge”. A dense array of secondary shear localizations is often present within the kink bands, suggesting significant shear deformation therein. The combined observations suggest that faulting in metastable Mn 2 GeO 4 olivine is a self-similar process, from grain-scale to the sample-scale. Both observed embrittlement behavior and the microstructure of metastable Mn 2 GeO 4 olivine are essentially identical to those in Mg 2 GeO 4 olivine we have reported previously, indicating that the physical mechanism of faulting in metastable olivine is insensitive to the specific crystallographic structure of the high-pressure phase. The low b values (about 1) observed in the faulting process in our experiments are similar to those of deep focus earthquakes in cold subduction zones. Our observed mechanism explains deep focus seismicity in cold metastable mantle wedges, provided that the self-similarity assumption holds to geological scales.

58 GEOSCIENCES↗
DOE OSTI2024

Advancing the Limits of InSAR to Detect Crustal Displacement from Low-Magnitude Earthquakes through Deep Learning

Detecting surface deformation associated with low-magnitude (M w ≤ 5) seismicity using interferometric synthetic aperture radar (InSAR) is challenging due to the subtlety of the signal and the often challenging imaging environments. However, low-magnitude earthquakes are potential precursors to larger seismic events, and thus characterizing the crustal displacement associated with them is crucial for regional seismic hazard assessment. We combine InSAR time-series techniques with a Deep Learning (DL) autoencoder denoiser to detect the magnitude and extent of crustal deformation from the M w = 3.4 Gallina, New Mexico earthquake that occurred on 30 July 2020. Although InSAR alone cannot detect event-related deformation from such a low-magnitude seismic event, application of the DL method reveals maximum displacements as small as (±2.5 mm) in the vicinity of both the fault and earthquake epicenter without prior knowledge of the fault system. This finding improves small-scale displacement discernment with InSAR by an order of magnitude relative to previous studies. We additionally estimate best-fitting fault parameters associated with the observed deformation. The application of the DL technique unlocks the potential for low-magnitude earthquake studies, providing new insights into local fault geometries and potential risks from higher-magnitude earthquakes. This technique also permits low-magnitude event monitoring in areas where seismic networks are sparse, allowing for the possibility of global fault deformation monitoring.

58 GEOSCIENCES↗
DOE OSTI2023

Real-Time Fault Tracking and Ground Motion Prediction for Large Earthquakes With HR-GNSS and Deep Learning

Earthquake early warning (EEW) systems aim to forecast the shaking intensity rapidly after an earthquake occurs and send warnings to affected areas before the onset of strong shaking. The system relies on rapid and accurate estimation of earthquake source parameters. However, it is known that source estimation for large ruptures in real-time is challenging, and it often leads to magnitude underestimation. In a previous study, we showed that machine learning, HR-GNSS, and realistic rupture synthetics can be used to reliably predict earthquake magnitude. This model, called Machine-Learning Assessed Rapid Geodetic Earthquake model (M-LARGE), can rapidly forecast large earthquake magnitudes with an accuracy of 99%. Here, we expand M-LARGE to predict centroid location and fault size, enabling the construction of the fault rupture extent for forecasting shaking intensity using existing ground motion models. We test our model in the Chilean Subduction Zone with thousands of simulated and five real large earthquakes. The result achieves an average warning time of 40.5 s for shaking intensity MMI4+, surpassing the 34 s obtained by a similar GNSS EEW model. Our approach addresses a critical gap in existing EEW systems for large earthquakes by demonstrating real-time fault tracking feasibility without saturation issues. This capability leads to timely and accurate ground motion forecasts and can support other methods, enhancing the overall effectiveness of EEW systems. Additionally, the ability to predict source parameters for real Chilean earthquakes implies that synthetic data, governed by our understanding of earthquake scaling, is consistent with the actual rupture processes.

58 GEOSCIENCES↗
DOE OSTI2022

Earthquake Nowcasting with Deep Learning

We review previous approaches to nowcasting earthquakes and introduce new approaches based on deep learning using three distinct models based on recurrent neural networks and transformers. We discuss different choices for observables and measures presenting promising initial results for a region of Southern California from 1950–2020. Earthquake activity is predicted as a function of 0.1-degree spatial bins for time periods varying from two weeks to four years. The overall quality is measured by the Nash Sutcliffe efficiency comparing the deviation of nowcast and observation with the variance over time in each spatial region. The software is available as open source together with the preprocessed data from the USGS.

Fox, Geoffrey Charles (ORCID:0000000310171391)↗
DOE OSTI2021

Periodicity Analysis of Earthquake Occurrence and Hypocenter Depth Near Parkfield, California, 1994–2002 Versus 2006–2014

In central California, periodic earthquake occurrence suggests a relationship with annual hydrological, atmospheric, thermal, and tidal loadings. Here, in catalogs of declustered earthquakes within 100 km from Parkfield, CA, we study a semiannual periodicity for the monthly number of ≤7.2-km-deep earthquakes and the monthly median hypocenter depth over 1994–2002 and 2006–2014. Peak-trough months in fitted periodic components differ between time spans and event populations. Deeper earthquakes present no semiannual and a weak annual periodicity. Although modeled pore pressure shows a Spring peak, when added to the elastic Coulomb stress from surface hydrospheric loads, it fails to predict a 6-month periodicity for 2006–2014. In 1994–2002, the pore-pressure amplitude appears to be of same order as the elastic stress and may have had a stronger effect. In 2006–2014, we did not find load model parameters explaining the observed pattern or apparent changes following the 2003 San Simeon and 2004 Parkfield earthquakes.

58 GEOSCIENCES↗
DOE OSTI2025

Simulating High-Frequency Seismograms in Realistic Earth Models to Better Understand Source Discrimination Based on Differential Magnitudes ( M L− M c)

Discriminating low-yield underground nuclear explosions from small earthquakes is a key task in monitoring nuclear test ban treaties. P/S amplitude ratios have been an effective discriminant for moderate-sized events recorded at regional distances, but it is unclear if they are as effective in discriminating small seismic events recorded at local distances (<150 km). The difference between local magnitude (M L ) and coda duration magnitude (M c ) has been proposed as a new discriminant that may complement P/S amplitude ratios at local distances. Here, in this work, we calculate high-frequency (up to ∼4 Hz) synthetic seismograms at epicentral distances of 0–30 km in realistic models of the Salt Lake basin (Utah, United States) to better understand how variations in source type and depth affect M L −M c values. The Earth models incorporate simplified 1D and deterministic 3D structures, small-wavelength stochastic velocity perturbations, and surface topography. Coda waves are enhanced for the more complicated models compared to the base 1D model, but still underpredict observed durations by about a factor of two, which results in overprediction of amplitude to duration ratios (i.e., M L −M c values) for a near-surface explosion and a 7 km deep earthquake. For both source types, the predicted M L and M c values decrease as source depth increases, and M L −M c shows only minor variation with depth; however, M L −M c is on average ∼0.5 units smaller for explosions than earthquakes. This finding may imply that M L −M c has sensitivity to source type, in addition to being a depth discriminant, but more modeling is needed given the limitations of the current study. Future modeling should incorporate higher-frequency (≳5 Hz) simulations over a larger distance range (0–150 km), where M L and M c are commonly measured, while honoring low shear velocities (<300 m/s) near the surface and sampling a wider range of earthquake and explosion source mechanisms.

Hutchings, Sean J. [Univ. of Utah, Salt Lake City,↗
DOE OSTI2022

An End-To-End Earthquake Detection Method for Joint Phase Picking and Association Using Deep Learning

Earthquake monitoring by seismic networks typically involves a workflow consisting of phase detection/picking, association, and location tasks. In recent years, the accuracy of these individual stages has been improved through the use of machine learning techniques. Here, in this study, we introduce a new end-to-end approach that improves overall earthquake detection accuracy by jointly optimizing each stage of the detection pipeline. We propose a neural network architecture for the task of multi-station processing of seismic waveforms recorded over a seismic network. This end-to-end architecture consists of three sub-networks: a backbone network that extracts features from raw waveforms, a phase picking sub-network that picks P- and S-wave arrivals based on these features, and an event detection sub-network that aggregates the features from multiple stations to associate and detect earthquakes across a seismic network. We use these sub-networks together with a shift-and-stack module based on back-projection that introduces kinematic constraints on arrival times, allowing the neural network model to generalize to different velocity models and to variable station geometry in seismic networks. We evaluate our proposed method on the STanford EArthquake Dataset (STEAD) and on the 2019 Ridgecrest, CA earthquake sequence. The results demonstrate that our end-to-end approach can effectively pick P- and S-wave arrivals and achieve earthquake detection accuracy rivaling that of other state-of-the-art approaches. Because our approach preserves information across tasks in the detection pipeline, it has the potential to outperform approaches that do not.

58 GEOSCIENCES↗
DOE OSTI2022

Toward improved urban earthquake monitoring through deep-learning-based noise suppression

Earthquake monitoring in urban settings is essential but challenging, due to the strong anthropogenic noise inherent to urban seismic recordings. Here, we develop a deep-learning-based denoising algorithm, UrbanDenoiser, to filter out urban seismological noise. UrbanDenoiser strongly suppresses noise relative to the signals, because it was trained using waveform datasets containing rich noise sources from the urban Long Beach dense array and high signal-to-noise ratio (SNR) earthquake signals from the rural San Jacinto dense array. Application to the dense array data and an earthquake sequence in an urban area shows that UrbanDenoiser can increase signal quality and recover signals at an SNR level down to ~0 dB. Earthquake location using our denoised Long Beach data does not support the presence of mantle seismicity beneath Los Angeles but suggests a fault model featuring shallow creep, intermediate locking, and localized stress concentration at the base of the seismogenic zone.

58 GEOSCIENCES↗
DOE OSTI2022

DeepShake: Shaking Intensity Prediction Using Deep Spatiotemporal RNNs for Earthquake Early Warning

Abstract We propose a deep spatiotemporal recurrent neural network, DeepShake, to project future shaking intensity directly from current ground-motion observations. DeepShake is a network-based forecasting model, able to predict future shaking intensity at all stations within a network given previously measured ground shaking. The model is not given any a priori knowledge of station locations; instead, it learns wave propagation amplitudes and delays solely from training data. We developed DeepShake with the 35,679 earthquakes from the 2019 Ridgecrest sequence. Tasked with alerting for modified Mercalli intensity (MMI) IV+ shaking on 3568 validation earthquakes at least 5 s in advance, DeepShake achieves an equal error rate of 11.4%. For the Mw 7.1 earthquake that hit Ridgecrest on 5 July 2019, DeepShake was able to provide targeted alerts to all stations inside the network 5 s prior to the arrival of MMI IV+ waveforms. DeepShake demonstrates that deep spatiotemporal neural networks can effectively provide one-step earthquake early warning with reasonable accuracy and latency.

Geochemistry & Geophysics↗
DOE OSTI2024

Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting

Advancing the capabilities of earthquake nowcasting, the real-time forecasting of seismic activities, remains crucial for reducing casualties. This multifaceted challenge has recently gained attention within the deep learning domain, facilitated by the availability of extensive earthquake datasets. Despite significant advancements, the existing literature on earthquake nowcasting lacks comprehensive evaluations of pre-trained foundation models and modern deep learning architectures; each focuses on a different aspect of data, such as spatial relationships, temporal patterns, and multi-scale dependencies. This paper addresses the mentioned gap by analyzing different architectures and introducing two innovative approaches called Multi Foundation Quake and GNNCoder. We formulate earthquake nowcasting as a time series forecasting problem for the next 14 days within 0.1-degree spatial bins in Southern California. Earthquake time series are generated using the logarithm energy released by quakes, spanning 1986 to 2024. Our comprehensive evaluations demonstrate that our introduced models outperform other custom architectures by effectively capturing temporal-spatial relationships inherent in seismic data. The performance of existing foundation models varies significantly based on the pre-training datasets, emphasizing the need for careful dataset selection. However, we introduce a novel method, Multi Foundation Quake, that achieves the best overall performance by combining a bespoke pattern with Foundation model results handled as auxiliary streams.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Rapid wavefield forecasting for earthquake early warning via deep sequence to sequence learning

We propose a deep learning model, WaveCastNet, to forecast high-dimensional wavefields. WaveCastNet integrates a convolutional long expressive memory architecture into a sequence-to-sequence forecasting framework, enabling it to model long-term dependencies and multiscale patterns in both space and time. By sharing weights across spatial and temporal dimensions, WaveCastNet requires significantly fewer parameters than more resource-intensive models such as transformers, resulting in faster inference times. Crucially, WaveCastNet also generalizes better than transformers to rare and critical seismic scenarios, such as high-magnitude earthquakes. Here, we show the ability of the model to predict the intensity and timing of destructive ground motions in real time, using simulated data from the San Francisco Bay Area. Furthermore, we demonstrate its zero-shot capabilities by evaluating WaveCastNet on real earthquake data. Our approach does not require estimating earthquake magnitudes and epicenters, steps that are prone to error in conventional methods, nor does it rely on empirical ground-motion models, which often fail to capture strongly heterogeneous wave propagation effects.

Geophysics↗
DOE OSTI2025

Prediction of Complex Observed Shear Wave Splitting Patterns at Ryukyu Subduction Zone Using a Strong Intra-Slab Anisotropy Model

Complex shear wave splitting (SWS) patterns in subduction zones are often interpreted geodynamically as resulting from complex mantle flow; however, this may not always be necessary. We analyzed 7,093 high-quality SWS measurements from teleseismic S waves recorded by Hi-net stations across the Ryukyu arc in Japan. Our findings show a systematic rotation of the fast S polarization from trench-parallel to trench-perpendicular depending on the earthquake backazimuth. For the same earthquake, the measured splitting patterns also vary spatially across the southwest Japan. Using full-wave seismic modeling, we showed that a dipping slab with ~30% shear anisotropy of the tilted transverse isotropy (TTI) type, with a symmetry axis perpendicular to the slab interface, can predict the observed delay times and polarization rotation. Our results highlight the importance of considering dipping anisotropic slabs in interpreting SWS at subduction zones.

58 GEOSCIENCES↗
DOE OSTI2023

Effect of a micro-scale dislocation pileup on the atomic-scale multi-variant phase transformation and twinning

In this paper, we perform concurrent atomistic–continuum (CAC) simulations to assess the contribution of the internal stress induced by the microscale dislocation pileup at an atomically structured interface to the atomic-scale phase transformations (PTs), reverse PTs, and twinning. The main novelty of this work is to unify the atomistic description of the interface and the coarse-grained (CG) description of the lagging dislocations away from the interface within one single framework. Our major findings are: (a) the interface dynamically responds to a pileup by forming steps/ledges, the height of which is proportional to the number of dislocations arriving at the interface; (b) the pileup-induced internal stress concentration profile follows neither the classical Eshelby model nor the super-dislocation model alone, but a combination of them; (c) when the pre-sheared sample is compressed, a direct square-to-hexagonal PT occurs ahead of the pileup tip and eventually grows into a wedge shape. The two variants of the hexagonal phases form a twin with respect to each other; (d) upon a further increase of the loading, part of the newly formed hexagonal phase transforms back to the square phase. The square product phase resulting from this reverse PT forms a twin with respect to the initial square phase. All phase boundaries (PBs) and twin boundaries (TBs) are stationary and correspond to zero thermodynamic Eshelby driving forces; and (e) the microscale dislocation pileup-induced internal shear stress and the structural change at the atomic-scale interface reduces the stress required for initiating a PT by a factor of 5.5, comparing with that in the sample containing no dislocations. This work is the first characterization of the behavior of PTs/twinning resulting from the reaction between a microscale dislocation slip and an atomically structured interface. In conclusion, the gained knowledge will advance our understanding of how the multi-phase material behaves in many complex physical processes, such as the synthesis of multi-phase high-entropy alloys or superhard ceramics under high-pressure torsion, deep mantle earthquakes in geophysics, and so on, which all involve dislocation slip, PTs, twinning, and their interactions across from the atomistic to the microscale and beyond.

36 MATERIALS SCIENCE↗