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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
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↗
NASA NTRS1994

Thermochemistry of dense hydrous magnesium silicates

Recent experimental investigations under mantle conditions have identified a suite of dense hydrous magnesium silicate (DHMS) phases that could be conduits to transport water to at least the 660 km discontinuity via mature, relatively cold, subducting slabs. Water released from successive dehydration of these phases during subduction could be responsible for deep focus earthquakes, mantle metasomatism and a host of other physico-chemical processes central to our understanding of the earth's deep interior. In order to construct a thermodynamic data base that can delineate and predict the stability ranges for DHMS phases, reliable thermochemical and thermophysical data are required. One of the major obstacles in calorimetric studies of phases synthesized under high pressure conditions has been limitation due to the small (less than 5 mg) sample mass. Our refinement of calorimeter techniques now allow precise determination of enthalpies of solution of less than 5 mg samples of hydrous magnesium silicates. For example, high temperature solution calorimetry of natural talc (Mg(0.99) Fe(0.01)Si4O10(OH)2), periclase (MgO) and quartz (SiO2) yield enthalpies of drop solution at 1044 K to be 592.2 (2.2), 52.01 (0.12) and 45.76 (0.4) kJ/mol respectively. The corresponding enthalpy of formation from oxides at 298 K for talc is minus 5908.2 kJ/mol agreeing within 0.1 percent to literature values.

Bose, Kunal↗
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↗
DOE OSTI2024

Detection of thin film phase transformations at high-pressure and high-temperature in a diamond anvil cell

Abstract Quantifying how grain size and/or deviatoric stress impact (Mg,Fe) 2 SiO 4 phase stability is critical for advancing our understanding of subduction processes and deep-focus earthquakes. Here, we demonstrate that well-resolved X-ray diffraction patterns can be obtained on nano-grained thin films within laser-heated diamond anvil cells (DACs) at hydrostatic pressures up to 24 GPa and temperatures up to 2300 K. Combined with well-established literature processes for tuning thin film grain size, biaxial stress, and substrate identity, these results suggest that DAC-loaded thin films can be useful for determining how grain size, deviatoric stress, and/or the coexistence of other phases influence high-pressure phase stability. As such, this novel DAC-loaded thin film approach may find use in a variety of earth science, planetary science, solid-state physics, and materials science applications.

Environmental Sciences & Ecology↗
NASA NTRS1972

Deformation and instability of underthrusting lithospheric plates

Models of the underthrusting lithosphere are constructed for the calculation of displacement and deflection. First, a mathematical theory is developed that rigorously demonstrates the elastic instability in the decending lithosphere. The theory states that lithospheric thrust beneath island arcs becomes unstable and suffers deflection as the compression increases. Thus, in the neighborhood of the edges where the lithospheric plate plunges into the asthenosphere and mesosphere its shape will be contorted. Next, the lateral displacement is calculated, and it is shown that, before contortion, the plate will thicken and contract at different positions with the variation in thickness following a parabolic profile. Finally, the depth distribution of the intermediate and deep focus earthquakes is explained in terms of plate buckling and contortion.

Liu, H.↗
NASA NTRS1988

Rheological control of Wadati-Benioff zone seismicity

Intermediate and deep focus earthquakes in Wadati-Benioff zones are considered, and it is suggested that although seismicity in downgoing slabs is restricted to their cold and strong interior, the seismicity distribution is not easily described as being controlled by a single lithospheric strength value. A numerical thermal model is used to compute strength contours for flow law constants reported from laboratory experiments, and it is shown that the expected pressure strengthening is large enough that the slab should have considerable strength well below the deepest seismicity. The results indicate that either a strongly depth-dependent limiting strength in downgoing slabs exists, or factors in addition to strength control the distribution of subduction zone earthquakes.

Brodholt, John↗
NASA NTRS1991

Flow and fracture of ice and ice mixtures

Frozen volatiles make up an important volume fraction of the low density moons of the outer solar system. Understanding the tectonic history of the surfaces of these moons, as well as the evolution of their interiors, requires knowledge of the mechanical strength of these icy materials under the appropriate planetary conditions (temperature, hydrostatic pressure, strain rate). Ongoing lab research is being conducted to measure mechanical properties of several different ices under conditions that faithfully reproduce condition both at the moons' surfaces (generally low temperature, to about 100 K, and low pressures) and in the deep interiors (warmer temperatures, pressures to thousands of atmospheres). Recent progress is reported in two different phases of the work: rheology of ices in the NH3-H2O system at temperatures and strain rates lower than ever before explored, with application to the ammonia-rich moons of Saturn and Uranus; and the water ice I yields II phase transformation, which not only applies directly to process deep in the interiors of Ganymede and Callisto, but holds implications for deep terrestrial earthquakes as well.

Durham, W. B.↗
DOE OSTI2022

Combining Deep Learning With Physics Based Features in Explosion-Earthquake Discrimination

This paper combines the power of deep-learning with the generalizability of physics-based features, to present an advanced method for seismic discrimination between earthquakes and explosions. The proposed method contains two branches: a deep learning branch operating directly on seismic waveforms or spectrograms, and a second branch operating on physics-based parametric features. These features are high-frequency P/S amplitude ratios and the difference between local magnitude (ML) and coda duration magnitude (MC). The combination achieves better generalization performance when applied to new regions than models that are developed solely with deep learning. Further, we also examined which parts of the waveform data dominate deep learning decisions (i.e., via Grad-CAM). Such visualization provides a window into the black-box nature of the machine-learning models and offers new insight into how the deep learning derived models use data to make decisions.

58 GEOSCIENCES↗
DOE OSTI2024

Probing the evolution of fault properties during the seismic cycle with deep learning

We use seismic waves that pass through the hypocentral region of the 2016 M6.5 Norcia earthquake together with Deep Learning (DL) to distinguish between foreshocks, aftershocks and time-to-failure (TTF). Binary and N-class models defined by TTF correctly identify seismograms in test with > 90% accuracy. We use raw seismic records as input to a 7 layer CNN model to perform the classification. Here we show that DL models successfully distinguish seismic waves pre/post mainshock in accord with lab and theoretical expectations of progressive changes in crack density prior to abrupt change at failure and gradual postseismic recovery. Performance is lower for band-pass filtered seismograms (below 10 Hz) suggesting that DL models learn from the evolution of subtle changes in elastic wave attenuation. Tests to verify that our results indeed provide a proxy for fault properties included DL models trained with the wrong mainshock time and those using seismic waves far from the Norcia mainshock; both show degraded performance. Our results demonstrate that DL models have the potential to track the evolution of fault zone properties during the seismic cycle. If this result is generalizable it could improve earthquake early warning and seismic hazard analysis.

58 GEOSCIENCES↗
NASA NTRS1993

Low-frequency source parameters of twelve large earthquakes

A global survey of the low-frequency (1-21 mHz) source characteristics of large events are studied. We are particularly interested in events unusually enriched in low-frequency and in events with a short-term precursor. We model the source time function of 12 large earthquakes using teleseismic data at low frequency. For each event we retrieve the source amplitude spectrum in the frequency range between 1 and 21 mHz with the Silver and Jordan method and the phase-shift spectrum in the frequency range between 1 and 11 mHz with the Riedesel and Jordan method. We then model the source time function by fitting the two spectra. Two of these events, the 1980 Irpinia, Italy, and the 1983 Akita-Oki, Japan, are shallow-depth complex events that took place on multiple faults. In both cases the source time function has a length of about 100 seconds. By comparison Westaway and Jackson find 45 seconds for the Irpinia event and Houston and Kanamori about 50 seconds for the Akita-Oki earthquake. The three deep events and four of the seven intermediate-depth events are fast rupturing earthquakes. A single pulse is sufficient to model the source spectra in the frequency range of our interest. Two other intermediate-depth events have slower rupturing processes, characterized by a continuous energy release lasting for about 40 seconds. The last event is the intermediate-depth 1983 Peru-Ecuador earthquake. It was first recognized as a precursive event by Jordan. We model it with a smooth rupturing process starting about 2 minutes before the high frequency origin time superimposed to an impulsive source.

Harabaglia, Paolo↗
NASA NTRS1984

The distribution of earthquakes with depth and stress in subducting slabs

The global variation of Benioff zone seismicity with depth and the orientation of stress axes of deep and intermediate earthquakes is explained using numerical models of subducting slabs. Models that match the seismicity and stress require a barrier to flow at the 670 km seismic discontinuity. The barrier may be a viscosity increase of at least an order of magnitude or a chemical discontinuity. Instantaneous flow is subparallel to the slabs for models with a viscosity increase but contorted for models with a chemical barrier. Log N (number of earthquakes) decreases linearly to 250-300 km depth and increases thereafter. Stress magnitude in the models shows the same pattern, in accord with experiments showing N proportional to e(k-sigma), with k a constant and sigma the stress magnitude. The models predict downdip compression in the slabs at depths below 300-400 km, as observed for earthquake stress axes.

Vassiliou, M. S.↗