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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Small scale mechanical characterization of thin foil materials via pin load microtesting

In situ scanning electron microscope (SEM) experiments, where small-scale mechanical tests are conducted on micro- and nanosized specimens, allow direct visualization of elastic and plastic responses over the entirety of the volume being deformed. This enables precise spatial and temporal correlation of slip events contributing to the plastic flow evidenced in a stress–strain curve. A new pin-loading methodology has been employed, in situ within the SEM, to conduct microtensile tests on thin polycrystalline metal foils. This approach can be tailored to a specific foil whose particular grain size may range from microns to tens of microns. Manufacture of the specialized pin grip was accomplished via silicon photolithography-based processing followed by subsequent focused ion beam finishing. Microtensile specimen preparation was achieved by combining a stencil mask methodology employing broad ion beam sputtering along with focused ion beam milling in the study of several metallic foil materials. Finite-element analyses were performed to characterize the stress and strain distributions in the pin grip and micro-specimen under load. Furthermore, under appropriately conceived test conditions, uniaxial stress–strain responses measured within these foils by pin-load microtensile testing exhibit properties consistent with larger scale tests.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Anomalous transport in disordered fracture networks: Spatial Markov model for dispersion with variable injection modes

Here we investigate tracer transport on random discrete fracture networks that are characterized by the statistics of the fracture geometry and hydraulic conductivity. While it is well known that tracer transport through fractured media can be anomalous and particle injection modes can have major impact on dispersion, the incorporation of injection modes into effective transport modeling has remained an open issue. The fundamental reason behind this challenge is that—even if the Eulerian fluid velocity is steady—the Lagrangian velocity distribution experienced by tracer particles evolves with time from its initial distribution, which is dictated by the injection mode, to a stationary velocity distribution. We quantify this evolution by a Markov model for particle velocities that are equidistantly sampled along trajectories. This stochastic approach allows for the systematic incorporation of the initial velocity distribution and quantifies the interplay between velocity distribution and spatial and temporal correlation. The proposed spatial Markov model is characterized by the initial velocity distribution, which is determined by the particle injection mode, the stationary Lagrangian velocity distribution, which is derived from the Eulerian velocity distribution, and the spatial velocity correlation length, which is related to the characteristic fracture length. This effective model leads to a time-domain random walk for the evolution of particle positions and velocities, whose joint distribution follows a Boltzmann equation. Finally, we demonstrate that the proposed model can successfully predict anomalous transport through discrete fracture networks with different levels of heterogeneity and arbitrary tracer injection modes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Incorporating leaf chlorophyll content into a two-leaf terrestrial biosphere model for estimating carbon and water fluxes at a forest site

Chlorophyll is the main light-harvesting pigment in leaves, facilitating photosynthesis and indicating the supply of nitrogen for photosynthetic enzymes. In this study, we explore the feasibility of integrating leaf chlorophyll content (Chl leaf ) into a Terrestrial Biosphere Model (TBM), as a proxy for the leaf maximum carboxylation rate at 25 °C (V$_{max}^{25}$), for the purpose of improving carbon and water flux estimation. Measurements of Chl leaf and V$_{max}^{25}$ were made in a deciduous forest stand at the Borden Forest Research Station in southern Ontario, Canada, where carbon and water fluxes were measured by the eddy covariance method. The use of Chl leaf -based V$_{max}^{25}$ in the TBM significantly reduces the bias of estimated gross primary productivity (GPP) and evapotranspiration (ET) and improves the temporal correlations between the simulated and the measured fluxes, relative to the commonly employed cases of using specified constant V$_{max}^{25}$, leaf area index (LAI)-based V$_{max}^{25}$ or specific leaf area (SLA)-based V$_{max}^{25}$. The biggest improvements are found in spring and fall, when the mean absolute errors (MAEs) between modelled and measured GPP are reduced from between 2.2–3.2 to 1.8 g C m -2 d -1 in spring and from between 2.1–2.8 to 1.8 g C m -2 d -1 in fall. The MAEs in ET estimates are reduced from 0.7–0.8 mm d -1 to 0.6 mm d -1 in spring, but no significant improvement is noted in autumn. A two-leaf upscaling scheme is used to account for the uneven distribution of incoming solar radiation inside canopies and the associated physiological differences between leaves. We found that modelled V$_{max}^{25}$ in sunlit leaves is 34% larger than in the shaded leaves of the same Chl leaf , which echoes previous physiological studies on light acclimation of plants. This study represents the first case of the incorporation of chlorophyll as a proxy for V$_{max}^{25}$ in a two-leaf TBM at a forest stand and demonstrates the efficacy of using chlorophyll to constrain V$_{max}^{25}$ and reduce the uncertainties in GPP and ET simulations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Comprehensive assessment of deep reinforcement learning approaches for economic dispatch in nuclear-driven microgrids

As the electrical grid integrates more variable renewable energy sources such as wind and solar, the demand for distributed and flexible systems to address this increased variability becomes critical. Nuclear-driven microgrids provide a promising solution by offering stable generation to complement intermittent renewables, ensuring grid reliability and operating efficiency. This paper proposes a recurrent deep reinforcement learning framework for optimal economic dispatch in a nuclear-powered microgrid integrating renewable energy sources, small modular reactors, battery storage systems, and balance-of-plant dynamics. A three-agent control architecture is developed, where demand and renewable energy agents act as forecasters, and a reinforcement learning-based dispatch agent performs real-time energy allocation. A nonlinear programming formulation is first used to generate an optimal baseline for benchmarking. The proposed dispatch controller, based on Proximal Policy Optimization enhanced with Long Short-Term Memory networks, exploits temporal correlations in system dynamics by taking advantage of the time series used as inputs to improve policy robustness under uncertainty. Comparative analysis against established deep reinforcement learning methods, including Proximal Policy Optimization with a feedforward architecture, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient, demonstrates superior performance. Numerical results indicate that the proposed controller achieves a 0.39% cost reduction relative to the nonlinear programming benchmark and outperforms other learning-based methods by generating additional revenue of up to 0.35%. All reinforcement learning controllers compute dispatch actions in less than 0.3 s, resulting in a computational speedup of more than three orders of magnitude over the nonlinear programming baseline. The findings of this paper highlight their applicability for real-time operation and control in nuclear-integrated microgrids under volatile operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

What can the GRACE seasonal cycle tell us about lake-aquifer interactions?

Lake-aquifer interactions have been the subject of investigation and debate for decades. Traditional investigation methods include measurement of water flux across the groundwater-surface water interface, application of heat and environmental tracer methods, conducting numerical simulations of the water flow, and mass balance-based approaches. We first review, evaluate and describe the different approaches that have been applied for examining lake-aquifer interactions and propose an additional complementary approach. While the leakage from lakes and its slow movement through porous media (laminar flow) is well characterized by existing methods, modeling the rapid and turbulent flow through preferred pathways (faults/karst) remains a challenging task. To better understand the nature, and the full scale, of the lake aquifer interactions, and using Lake Nasser and the underlying Dakhla subbasin of the Nubian Sandstone Aquifer System (NSAS) in the Western Desert of Egypt (area: 0.66 x 10 6 km 2 ) as a test site, we examine, model, and correlate temporally and spatially, the variations in Gravity Recovery and Climate Experiment terrestrial water storage (GRACE TWS ) solutions, with precipitation, lake levels, area, and water volume. We review current GRACE applications in hydrology and present our novel approach. Findings include: firstly, large seasonal fluctuations (peak: Nov./Dec.; trough: July/Aug.) in Lake Nasser's surface water levels (average annual fluctuations: from 3.7 m to 7.5 m), area (average area: 3622 km 2 but up to 4530 km 2 ), and volume (annual average: 13.4 km 3 , and up to 34 km 3 ) are observed throughout years 2006 to 2015. These fluctuations are accompanied by an increase in GRACE TWS (average: 50 ± 13 mm/yr, up to 77 ± 18 mm/yr) over Lake Nasser and by a progression of a front of increasing GRACE TWS values (> 50 ± 13 mm) away from the lake reaching distances of up to 700 km some 3 to 5 months following peak lake level periods. The areas witnessing the seasonal increase in GRACE TWS display a progressive increase in phase and decrease in amplitude with distance from the lake. Secondly, the negligible precipitation over the Dakhla subbasin cannot account for the observed seasonal GRACE TWS patterns and neither can the leakage signal from Lake Nasser. Thirdly, overall similarities in the distribution of GRACE TWS seasonal spatial patterns are observed. During high lake level periods (e.g., Dec. 2007; Feb. 2015) additional, and more distant, areas from Lake Nasser saw increases in GRACE TWS and vice versa during low lake level periods (e.g., July 2006; Aug. 2010). These observations are consistent with Lake Nasser being the main source of modern recharge for the Dakhla subbasin and suggest a new conceptual model for the subbasin: a slow groundwater flow through a porous matrix and a rapid groundwater flow along a network of faults, fractures, and karst topography across the matrix. We suggest that the proposed conceptual model for the interaction between Lake Nasser and the Dakhla subbasin could be applicable to aquifers of similar geologic, climatic, and hydrologic settings worldwide and that approaches similar to those advocated here could be used to investigate the validity of this suggestion.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Sequence2Self: Self-supervised image sequence denoising of pixel-level spray breakup morphology

Optical imaging of fast and transient phenomena such as the turbulent breakup of liquid sprays exhibit low signal-to-noise ratios due to the limited illumination intensity relative to the short exposure time. Image denoising is required to facilitate physical studies over these data but is challenging due to the absence of clean ground-truths and the stringency of the denoising task (e.g., strong and complex noise, limited resolution, preserving physical fidelity), preventing supervised and existing un-/self-supervised deep learning methods. To this end, Sequence2Self (Seq2S) is proposed, an extension of Self2Self (S2S) to image sequences that leverages both the signal’s spatial and temporal correlation. Seq2S is demonstrated on time-resolved x-ray phase contrast imaging of liquid jet fuel sprays in a gas turbine combustor, which possesses all of challenges detailed above. Experiments are conducted across four fuels with different breakup morphology using various state-of-the-art methods. Overall, many of the methods failed and Seq2S was most successful: (1) Accurate spray structures were reconstructed with consistent evolution across frames void of artifacts. (2) The performance was robust, invariant to the hyperparameter choice. (3) Computational time is short and can be made eligible for real-time denoising. In particular, the images denoised by Seq2S showed spray droplet diameter distributions with near-zero Kullback–Leibler divergence (0.01 ± 0.01) to a cleaner reference, whereas the second best method yielded 0.06 ± 0.03. In conclusion, this suggests that Seq2S can be reliably used prior to subsequent quantitative spray analyses as it retains (if not, improves) the statistical physical properties of the data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

FIND: A Synthetic weather generator to control drought Frequency, Intensity, and Duration

Water systems worldwide are experiencing climate change-induced shifts in drought properties like frequency, intensity, and duration, affecting water security and reliability. To develop and test effective drought preparedness plans, researchers often use synthetic weather generators to create hydrological scenarios that explore drought variability beyond historical records. Existing weather generators typically allow users to adjust streamflow statistics like percentiles or temporal correlation but do not directly control drought properties of frequency, intensity, and duration. To fill this gap, we propose FIND (Frequency, INtensity, and Duration) synthetic weather generator. FIND incorporates a standardized drought index to directly and in dependently control drought frequency, intensity, and duration in generated streamflow time series while preserving observed hydrological variability. Use cases for FIND include i) water systems analysis applications that seek to train and test drought strategies under historical and plausible future drought conditions, and ii) bottom-up vulnerability studies relating system vulnerability outcomes to specific changes in drought properties of frequency, intensity, and duration. Here, we demonstrate FIND’s versatility through three experiments: replicating historically observed drought properties, generating streamflow scenarios for multiple sites preserving correlation between their drought conditions, and generating a set of scenarios with direct and independent changes in drought properties. FIND source code is openly available for applications beyond the scope of this paper.

42 ENGINEERING↗
DOE OSTI2020

U-Pb zircon age constraints on the earliest eruptions of the Deccan Large Igneous Province, Malwa Plateau, India

Climate instability driven by emission of volatiles during emplacement of large igneous provinces (LIPs) is frequently invoked as a potential cause of mass extinctions. However, documenting this process in the geologic record requires a holistic understanding of eruption rates, the location of eruptive centers, and potential sources of climate-changing volatiles. We present new chemical abrasion–isotope dilution–thermal ionization mass spectrometry (CA-ID-TIMS) U-Pb zircon geochronology from Malwa Plateau basalts on the northern margin of the Deccan LIP, India. These basalts have been previously interpreted as either an extension of the province's main volcanic stratigraphy or as an independent eruptive center active up to millions of years prior to the main eruptive phase. Our data instead demonstrate that the lower Malwa Plateau basalts are temporally correlative with the first pulse of Deccan volcanism and provide new constraints on its initiation and duration. Paleomagnetic data further indicate that upper Malwa Plateau basalts may be age-equivalent to the second, third, and fourth pulses of Deccan volcanism. The relative thicknesses of age-equivalent packages of basalt are consistent with eruption of the Deccan LIP from a southward-migrating eruptive center. The first eruptive pulse is coeval with a ~200 kyr Late Maastrichtian warming event preserved globally in contemporaneous stratigraphic sections. We propose that the first pulse of Deccan magmatism was more voluminous in the north, where it erupted through organic-rich sedimentary rocks of the Narmada-Tapti rift basin. Thermal metamorphism of these sediments could have been a source of sufficient CO 2 to drive the Late Maastrichtian warming event, which if true would reconcile the apparent dampened warming signals associated with later Deccan eruptive pulses.

58 GEOSCIENCES↗
DOE OSTI2022

New data-driven approach to bridging power system protection gaps with deep learning

Protection is a critical function in power systems to avoid equipment damage, maintain personnel safety, and support system reliability. However, current protective relay technology cannot adequately protect equipment and personnel from effects of some events; these deficiencies are termed protection gaps. In this paper, a data-driven approach is proposed to complement traditional protection technology and distinguish fault conditions from transients caused by normal operations. A combined convolutional neural network and long short-term memory (CNN-LSTM) network is implemented to achieve data translation invariance and capture the temporal correlation of the time-series input data. As a result, the data-driven method can accurately detect system faults despite variation and noise in the input data. In addition, using the CNN-LSTM--based method avoids the complicated, manual feature extraction procedure required by many traditional data-driven methods. The effectiveness of the proposed approach is tested on two kinds of protection gaps: high-impedance faults and transformer inter-turn faults. Lastly, a transfer learning method is also proposed to address the common issue of data-driven methods for which real-world training data are scarce. Extensive study results demonstrate that the proposed approach can accurately bridge power system protection gaps.

42 ENGINEERING↗
DOE OSTI2022

Enhanced deep neural networks with transfer learning for distribution LMP considering load and PV uncertainties

As the flexibility of generation and demand increases in distribution systems, the residential loads are emerging as a promising means to participate in demand response and the transactive energy market. Market pricing is an instrumental mechanism for the distribution system operator to exploit the full potential of the flexible resources. The distribution locational marginal price (DLMP) can be used to guide the residential load consumption. This type of market signal helps the distribution system operator to optimize the scheduling of all resources while satisfying related network constraints through a day-ahead market. However, solving the optimization problem for large-scale systems can be computationally expensive. To address the scalability and practicability limitations of the DLMP framework, a learning-based approach is proposed in this paper to complement the day-ahead distribution market framework. Here, the proposed approach combines long short-term memory and transfer learning to develop deep neural network that can capture the spatial–temporal correlation of the input data. The model can determine the optimal DLMP for each node in a distribution system without the system parameters required to formulate the optimization problem. Testing results on IEEE 33-bus and 123-bus systems show that the proposed approach can generate a comparable DLMP against the optimization solutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

A tightly-coupled domain-decomposition approach for highly nonlinear stochastic multiphysics systems

Multiphysics simulations often involve nonlinear components that are driven by internally generated or externally imposed random fluctuations. When used with a domain-decomposition (DD) algorithm, such components have to be coupled in a way that both accurately propagates the noise between the subdomains and lends itself to a stable and cost-effective temporal integration. We develop a conservative DD approach in which tight coupling is obtained by using a Jacobian-free Newton–Krylov (JfNK) method with a generalized minimum residual iterative linear solver. This strategy is tested on a coupled nonlinear diffusion system forced by a truncated Gaussian noise at the boundary. Enforcement of path-wise continuity of the state variable and its flux, as opposed to continuity in the mean, at interfaces between subdomains enables the DD algorithm to correctly propagate boundary fluctuations throughout the computational domain. Reliance on a single Newton iteration (explicit coupling), rather than on the fully converged JfNK (implicit) coupling, may increase the solution error by an order of magnitude. Increase in communication frequency between the DD components reduces the explicit coupling's error, but makes it less efficient than the implicit coupling at comparable error levels for all noise strengths considered. Finally, the DD algorithm with the implicit JfNK coupling resolves temporally-correlated fluctuations of the boundary noise when the correlation time of the latter exceeds some multiple of an appropriately defined characteristic diffusion time.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Multiscale assessment of land surface phenology from harmonized Landsat 8 and Sentinel-2, PlanetScope, and PhenoCam imagery

As the spatial and temporal resolution of remotely sensed imagery has improved over the last four decades, algorithms for monitoring and mapping seasonal changes in surface properties have evolved rapidly. Most recently, the availability of daily PlanetScope imagery has created new opportunities for monitoring the land surface phenology (LSP) of terrestrial ecosystems at high spatial resolution. However, the quality and value of LSP information from PlanetScope imagery have not been systematically examined. In this paper, we evaluate the character and quality of LSP information derived from PlanetScope by comparing time series of vegetation indices and LSP metrics from PlanetScope to corresponding time series and LSP metrics derived from Harmonized Landsat 8 and Sentinel-2 (HLS) imagery and PhenoCams at six sites that span a diverse range of land cover types and climate. Results show that vegetation index time series from all three data sources show high temporal correlation, and LSP metrics derived from HLS, PlanetScope, and PhenoCam show high agreement with negligible bias. Semi-variograms for phenometrics estimated from PlanetScope imagery indicate that the majority of spatial variance captured in PlanetScope phenometrics occurs well below the spatial resolution HLS imagery. At the same time, LSP metrics from HLS are most strongly correlated with the 50–75% quantiles of 3 m LSP metrics from PlanetScope. This indicates that HLS captures the average phenology at sub-pixel scale captured in PlanetScope imagery. Here, our results represent the first comprehensive comparison of LSP metrics estimated from PlanetScope and publicly available moderate spatial resolution imagery, and provide insights regarding: (1) the quality and character of LSP metrics derived from HLS and PlanetScope; and (2) the relative merits and trade-offs associated with the use of each data source for LSP studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

A stochastic downscaling approach for generating high-frequency solar irradiance scenarios

Solar power is increasingly cost viable with solar photovoltaic (PV) installations becoming commonplace. PV planning and operational studies, however, require high-frequency solar irradiance scenarios to understand potential electric grid impacts due to the variability and uncertainty of the underlying solar resource. Existing remote sensing solar data products are often available over large spatial domains, but are limited in temporal resolution. For example, the global horizontal irradiance (GHI) component contained within the National Solar Radiation Database (NSRDB) is available at time resolution of 30 min on an approximately four-kilometer grid. In contrast, substantial solar variability is present at finer time scales and this article describes an algorithm to stochastically generate one-minute GHI from widely available sub-hourly NSRDB. A generalized linear modeling (GLM) framework is proposed, which includes non-Gaussian mixtures, and extends the literature involving the synthesis of GHI data. The model is trained on a set of sample locations around Oregon, USA, and validated across the USA using both the Surface Radiation Budget Network (SURFRAD) dataset and Solar Radiation Monitoring Laboratory (SRML) network. Simulated ensembles show good coverage properties and temporal correlation structure. The resulting downscaled ensembles allow for understanding the unpredictable variability inherent in GHI at locations without direct measurements. Here, future work can leverage the algorithm as part of a stochastic optimization of electric grid operations with high-penetration PV systems.

14 SOLAR ENERGY↗
DOE OSTI2017

Prolonged exposure to particulate chromate inhibits RAD51 nuclear import mediator proteins

Particulate hexavalent chromium (Cr(VI)) is a human lung carcinogen and a human health concern. The induction of structural chromosome instability is considered to be a driving mechanism of Cr(VI)-induced carcinogenesis. Homologous recombination repair protects against Cr(VI)-induced chromosome damage, due to its highly accurate repair of Cr(VI)-induced DNA double strand breaks. However, recent studies demonstrate Cr(VI) inhibits homologous recombination repair through the misregulation of RAD51. RAD51 is an essential protein in HR repair that facilitates the search for a homologous sequence. Recent studies show prolonged Cr(VI) exposure prevents proper RAD51 subcellular localization, causing it to accumulate in the cytoplasm. Since nuclear import of RAD51 is crucial to its function, this study investigated the effect of Cr(VI) on the RAD51 nuclear import mediators, RAD51C and BRCA2. We show acute (24 h) Cr(VI) exposure induces the proper localization of RAD51C and BRCA2. In contrast, prolonged (120 h) exposure increased the cytoplasmic localization of both proteins, although RAD51C localization was more severely impaired. These results correlate temporally with the previously reported Cr(VI)-induced RAD51 cytoplasmic accumulation. In addition, we found Cr(VI) does not inhibit interaction between RAD51 and its nuclear import mediators. Altogether, our results suggest prolonged Cr(VI) exposure inhibits the nuclear import of RAD51C, and to a lesser extent, BRCA2, which results in the cytoplasmic accumulation of RAD51. Cr(VI)-induced inhibition of nuclear import may play a key role in its carcinogenic mechanism since the nuclear import of many tumor suppressor proteins and DNA repair proteins is crucial to their function. - Highlights: • RAD51C nuclear localization is reduced after prolonged Cr(VI) exposure. • Prolonged Cr(VI) inhibits nuclear localization of BRCA2 less severely than RAD51C. • RAD51C deficiency induces the cytoplasmic accumulation of RAD51. • Cr(VI) does not inhibit interactions between RAD51 and its nuclear import partners.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO 2 Emissions

Satellite data provides essential insights into the spatiotemporal distribution of CO 2 concentrations. However, many atmospheric inverse models fail to adequately incorporate the spatial and temporal correlations inherent in satellite observations and often lack rigorous methods for estimating parameters like spatial length scales. We introduce an inference model that processes the spatiotemporal covariance in satellite data and estimates hyperparameters such as covariance length scales. Our approach uses the Gaussian process (GP) machine learning (ML) and modern probabilistic programming languages (PPLs) to perform atmospheric inversions of emissions from satellite data. We develop a GP ML inversion system based on modern PPLs and the GEOS-Chem chemical transport model, simulating atmospheric CO 2 concentrations corresponding to the Orbiting Carbon Observatory-2/3 (OCO-2/3) data for July 2020. In our supervised learning framework, we treat the GEOS-Chem simulated data set as the target, with predictors derived by scaling the target with sector-specific factors hidden from the GP machine. Our results show that the GP model, combined with GPU-enabled PPLs, effectively retrieves true emission scaling factors and infers noise levels concealed within the data. This suggests that our method could be applied over larger areas with more complex covariance structures, enabling comprehensive analysis of the spatiotemporal patterns observed in OCO-2/3 and similar satellite data sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Earthquake Swarms Triggered by Groundwater Extraction Near the Dead Sea Fault

Abstract In 2013 and 2018, earthquake swarms with a maximum moment magnitude of 4.5 occurred ~5 km from the northern section of the Dead Sea Transform Fault. Here we show that aquifer pressure data, interferometric synthetic aperture radar surface deformation time series, and seismic monitoring suggest that groundwater withdrawal triggered these earthquakes. Continuous groundwater extraction from several wells located ~10 km west of the swarms has accelerated since 2010 and resulted in a total decrease of ~50 m of the groundwater level at the time of the 2018 earthquake swarm. The withdrawal also corresponds to surface subsidence of ~10 mm/year based on repeat interferometric synthetic aperture radar measurements. The temporal correlation, extensive subsidence, anomalous swarm characteristics, and normal faulting orientation suggest a connection between the groundwater withdrawal and recent earthquakes. Poroelastic modeling demonstrates that pumping‐induced pore pressure decrease west of the earthquake could have caused significant dilatational stresses that led to normal faulting events outside the aquifer.

Wetzler, Nadav↗
DOE OSTI2025

Precursory Locking Precedes Slip Events on Laboratory Fault

Earthquakes occur through repeated cycles of locking and slip. What is the relationship between transiently locked patches and the size, location, and timing of the subsequent slip events? Here we use an experimental fault made of transparent rubber to image the slip history of over 18,000 events. The soft elastic system allows for contained ruptures and thus a natural evolution of stress heterogeneity over multiple earthquake cycles. We observe a precursory locking phase preceding slip nucleation for events of all sizes. The integrated locking distance increases linearly with the moment of the eventual event. The most intense (largest slip deficit) locking events are spatially and temporally correlated with the hypocenters of the largest rupture events. These results show that the transient locking is statistically, but not deterministically, predictive of the location, timing, and size of the eventual slip event.

earthquakes↗