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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

A feasibility study for long-path multiple detection using a neural network

Least-squares inverse filters have found widespread use in the deconvolution of seismograms and the removal of multiples. The use of least-squares prediction filters with prediction distances greater than unity leads to the method of predictive deconvolution which can be used for the removal of long path multiples. The predictive technique allows one to control the length of the desired output wavelet by control of the predictive distance, and hence to specify the desired degree of resolution. Events which are periodic within given repetition ranges can be attenuated selectively. The method is thus effective in the suppression of rather complex reverberation patterns. A back propagation(BP) neural network is constructed to perform the detection of first arrivals of the multiples and therefore aid in the more accurate determination of the predictive distance of the multiples. The neural detector is applied to synthetic reflection coefficients and synthetic seismic traces. The processing results show that the neural detector is accurate and should lead to an automated fast method for determining predictive distances across vast amounts of data such as seismic field records. The neural network system used in this study was the NASA Software Technology Branch's NETS system.

Feuerbacher, G. A.↗
DOE OSTI2023

Seismoacoustic Signatures Observed During a Long-Term Deployment of Infrasound Sensors at the Nevada National Security Site

Earthquakes have repeatedly been shown to produce inaudible acoustic signals (<20 Hz), otherwise known as infrasound. These signals can propagate hundreds to thousands of kilometers and still be detected by ground-based infrasound arrays depending on the source strength, distance between source and receiver, and atmospheric conditions. Another type of signal arrival at infrasound arrays is the seismic induced motion of the sensor itself, or ground-motion-induced sensor noise. Measured acoustic and seismic waves produced by earthquakes can provide insight into properties of the earthquake such as magnitude, depth, and focal mechanism, as well as information about the local lithology and atmospheric conditions. Large earthquakes that produce strong acoustic signals detected at distances greater than 100 km are the most commonly studied; however, more recent studies have found that smaller magnitude earthquakes ($M$ w <2.0) can be detected at short ranges. In that vein, this study will investigate the ability for a long-term deployment of infrasound sensors (deployed as part of the Source Physics Experiments [SPE] from 2014 to 2020) to detect both seismic and infrasonic signals from earthquakes at local ranges (<50 km). Methods used include a combination of spectral analysis and automated array processing, supported by U.S. Geological Survey earthquake bulletins. Further, this investigation revealed no clear acoustic detections for short range earthquakes. However, secondary infrasound from an $M$ w 7.1 earthquake over 200 km away was detected. Important insights were also made regarding the performance of the SPE networks including detections of other acoustic sources such as bolides and rocket launches. Finally, evaluation of the infrasound arrays is performed to provide insight into optimal deployments for targeting earthquake infrasound.

58 GEOSCIENCES↗
DOE OSTI2026

Constructing a High‐Resolution Aftershock Catalog for the 2017 Mw 8.2 Tehuantepec Earthquake Sequence Using a Machine Learning–Based Workflow

The 8 September 2017 Mw 8.2 Tehuantepec earthquake was the largest instrumentally recorded normal‐faulting earthquake in Mexico. The mainshock occurred offshore within the Tehuantepec seismic gap, generating >30,000 aftershocks in the following year. We applied an open‐source, machine learning (ML)–assisted workflow to construct a high‐resolution aftershock catalog using data from temporary and permanent seismic networks in southern Mexico. The workflow integrates PhaseNet for phase detection; GaMMA for phase association; and VELEST, HypoInverse, and HypoDD for velocity modeling and relocation. We processed seven months of continuous waveform data from 29 broadband stations, including a temporary rapid‐response deployment that improved station coverage of the offshore rupture zone. To evaluate performance, we compared our results against analyst‐reviewed picks and event locations from the Servicio Sismológico Nacional catalog. The resulting catalog contains 11,374 relocated earthquakes and represents the most comprehensive published dataset for this sequence, incorporating the first full use of the temporary network. Relocated hypocenters show improved depth control and align well with the Slab2.0 subduction geometry, revealing clearer separation between offshore slab events and onshore crustal seismicity. This study demonstrates that combining ML‐based detection with established methods provides a scalable and reproducible approach for constructing high‐quality earthquake catalogs in tectonically complex environments and offers practical guidance for adapting similar workflows to other earthquake sequences.

Garcia, Marc [The University of Texas at El Paso, ↗
NASA NTRS1984

A tomographic image of mantle structure beneath southern California

The variations in seismic structure beneath southern California were determined by using a tomographic method of inversion on teleseismic P delays recorded with the Southern California Array. The inversion reveals two prominent features beneath the region. The first is a thin, vertical wedge directly beneath the Transverse Ranges that is 2-3 percent faster than the surrounding region. This feature deepens to the east, attaining a maximum depth of about 250 km beneath the San Bernardino Mountains. The second feature is a major zone of low velocity material that is 2-4 percent slow under the Salton Trough rift valley, extending to a depth of about 125 km. Two possible explanations for the spatial association of the Transverse Ranges with the velocity anomaly below are lithospheric subduction or small-scale sublithospheric convection in the region of the Big Bend of the San Andreas Fault. The low velocity anomaly beneath the Salton Trough is consistent with convective upwelling there.

Humphreys, E.↗
DOE OSTI2025

Evaluation of Station Performance of the Idaho National Laboratory Seismic Monitoring Network Using Network Detection Thresholds

The Idaho National Laboratory (INL) Seismic Monitoring Network is located in eastern Idaho and monitors a portion of the intermountain seismic belt. It has been in place for 50 yr and has undergone several major changes, the most recent of which has been the transition to the Antelope real‐time acquisition system and the implementation of automatic phase picking algorithms to aid in analysis. This study discusses the efforts to evaluate the performance of the INL seismic monitoring network (and other surrounding stations) using the new real‐time acquisition system. The method outlined by Wilson et al. (2021) is used to develop an empirical relationship between the observability of local earthquakes as a function of magnitude and distance. This relationship is used to produce detection thresholds for Pwaves for all stations of interest. The INL seismic network has two main goals: monitor tectonic‐and volcanic‐related events and measure ground motions for input into seismic hazard analysis. Because of these two overall objectives, several seismic stations have been installed near critical facilities and, therefore, are not as quiet as stations that are used primarily for earthquake detection. This is reflected in their detection thresholds, which are much smaller for stations away from facilities. This study shows that the INL Seismic Monitoring Network is able to detect earthquakes near INL facilities with M L > 1.2, with redundancies built in to ensure this sensitivity even if data became unavailable from some stations. This study also shows “holes” in the monitoring network where the detection of smaller earthquakes is highly dependent on sparsely placed seismic stations. In conclusion, the results of this study will be used to govern plans for expansion of earthquake monitoring in Idaho and the surrounding region and to fine‐tune the detection thresholds for individual stations.

58 - GEOSCIENCES↗
DOE OSTI2020

Seismicity and Stress Associated With a Fluid-Driven Fracture: Estimating the Evolving Geometry

A coupled approach, combining the theory of rate- and state-dependent friction and methods from poroelasticity, forms the basis for a quantitative relationship between displacements and fluid leak-off from a growing fracture and changes in the rate of seismic events in the region surrounding the fracture. Poroelastic Green's functions link fracture aperture changes and fluid flow from the fracture to changes in the stress field and pore pressure in the adjacent formation. The theory of rate- and state-dependent friction provides a connection between Coulomb stress changes and variations in the rate of seismic events. Numerical modeling indicates that the Coulomb stress changes can vary significantly between formations with differing properties. The relationship between the seismicity rate changes and the changes in the formation stresses and fluid pressure is nonlinear, but a transformation produces a quantity that is linearly related to the aperture changes and fluid leak-off from the fracture. The methodology provides a means for mapping changes in seismicity into fracture aperture changes and to image an evolving fracture. An application to observed microseismicity associated with a hydrofracture reveals asymmetric fracture propagation within two main zones, with extended propagation in the upper zone. Furthermore, the time-varying volume of the fracture agrees with the injected volume, given by the integration of rate changes at the injection well, providing validation of the estimated aperture changes.

58 GEOSCIENCES↗
NASA NTRS2020

The InSight APSS Data Return Anomaly: Development of an Automated Detection and Response Method

The Auxiliary Payload Sensor Suite (APSS), a collection of environmental sensors carried by the Interior exploration using Seismic Investigations, Geodesy, and Heat Transport (InSight) lander, is capable of measuring Martian air temperature, wind speed, atmospheric pressure, and local magnetic fields. After beginning Mars surface operations, the instrument experienced an anomaly that prevented it from returning science data. The anomaly affected not only the instrument, but also had impacts at the system level. APSS returned to normal operations, however the anomaly occurred again just several weeks later. This proved the need for a streamlined recovery response that would be adaptable to the operations planning cycle and workforce, that would limit the system-level impacts of the anomaly, and that would minimize the instrument downtime. The recovery response evolved from a ground-in-the-loop response to an onboard method for detecting occurrences of the anomaly and automatically recovering the instrument. Ultimately, the automated detection and response method reduced instrument downtime from days to hours and significantly minimized science data loss.

Mittal, Nimisha↗
DOE OSTI2021

Imaging Complex Subsurface Structures for Geothermal Exploration at Pirouette Mountain and Eleven-Mile Canyon in Nevada

Accurate imaging of subsurface complex structures with faults is crucial for geothermal exploration because faults are generally the primary conduit of hydrothermal flow. It is very challenging to image geothermal exploration areas because of complex geologic structures with various faults and noisy surface seismic data with strong and coherent ground-roll noise. In addition, fracture zones and most geologic formations behave as anisotropic media for seismic-wave propagation. Properly suppressing ground-roll noise and accounting for subsurface anisotropic properties are essential for high-resolution imaging of subsurface structures and faults for geothermal exploration. We develop a novel wavenumber-adaptive bandpass filter to suppress the ground-roll noise without affecting useful seismic signals. This filter adaptively exploits both characteristics of the lower frequency and the smaller velocity of the ground-roll noise than those of the signals. Consequently, this filter can effectively differentiate the ground-roll noise from the signal. We use our novel filter to attenuate the ground-roll noise in seismic data along five survey lines acquired by the U.S. Navy Geothermal Program Office at Pirouette Mountain and Eleven-Mile Canyon in Nevada, United States. We then apply our novel anisotropic least-squares reverse-time migration algorithm to the resulting data for imaging subsurface structures at the Pirouette Mountain and Eleven-Mile Canyon geothermal exploration areas. The migration method employs an efficient implicit wavefield-separation scheme to reduce image artifacts and improve the image quality. Our results demonstrate that our wavenumber-adaptive bandpass filtering method successfully suppresses the strong and coherent ground-roll noise in the land seismic data, and our anisotropic least-squares reverse-time migration produces high-resolution subsurface images of Pirouette Mountain and Eleven-Mile Canyon, facilitating accurate fault interpretation for geothermal exploration.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

ML-Based Rock Properties and Seismic Volume Enhancement

This project aims to improve field-scale Carbon Capture and Storage (CCS) assessments by enhancing petrophysical and geophysical log predictions through machine learning and neural networks. In our work during EY23, we applied Conditional Variational Autoencoders (CVAEs) to predict compressional velocity (Vp) and assess CO2 saturation levels in geological formations at the Illinois Basin Decatur Project (IBDP). In another task, we improved full-waveform inversion (FWI) methods with machine-learning approaches using lithological constraints. Full-waveform inversion (FWI) of seismic data estimates the elastic properties of subsurface rocks with high spatial resolution.

Nathanail, Athanasios↗
DOE OSTI2022

Real-time deep-learning inversion of seismic full waveform data for CO 2 saturation and uncertainty in geological carbon storage monitoring

Deep-learning inversion has recently drawn attention in geological carbon storage research due to its potential of imaging and monitoring carbon storage in real time, significantly improving efficiency and safety of carbon storage operations. We present a deep-learning full waveform inversion method that after the neural network has been trained can image CO 2 saturation and its uncertainty in real time. Our deep-learning inversion method is based on the U-Net architecture with the neural network trained on pairs of synthetic seismic data and CO 2 saturation models. Accordingly, our training establishes a mapping relationship between seismic data and CO 2 saturation models and once fully trained directly estimates CO 2 saturation as a function of subsurface location. We further quantify uncertainties of CO 2 saturation estimates using the Monte Carlo dropout method and a bootstrap aggregating method. For this proof-of-concept study, the CO 2 training models and data are derived from the Kimberlina 1.2 model, a hypothetical 3D geological carbon storage model that is constructed based on various geological and hydrological data from the Southern San Joaquin Basin, California. We perform deep-learning inversion experiments using noise-free and noisy training and test data sets and compare the results. Our modelling experiments show that (1) the deep-learning inversion can estimate 2D distributions of CO 2 fairly well even in the presence of Gaussian random noise and (2) both CO 2 saturation imaging and uncertainty quantification can be done in real time. Our results suggest that the deep-learning inversion method can serve as a robust real-time monitoring tool for geological carbon storage and/or other time-varying reservoir/aquifer properties that result from injection, extraction, and/or other subsurface transport phenomena.

58 GEOSCIENCES↗
DOE OSTI2024

Proliferation Detection via Acoustic Monitoring of Pyroprocessing Equipment and Related Systems

In support of nuclear safeguards and nonproliferation, this effort is leveraging acoustic and seismic sensors to advance detection of pyroprocessing activities. Pyroprocessing, or electrochemical processing, is a method of separating irradiated nuclear fuel into its actinide and fission product components, enabling the reuse of fuel materials and reducing the radiotoxicity of the remaining waste. Verification and accountability in the process poses several challenges because of the high radiation environment and the presence of material holdup through the facility. Irregular or unaccounted-for equipment operation can signify diversion or misuse of critical materials. Acoustic and seismic sensors offer the capability to identify periods of equipment operation and help verify adherence to accountancy records.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Multi-phenomenology Yield Characterization

This report serves as the first delivery of a four-year applied science effort to transform and advance the error bounds for the yield estimate of an explosion. Each year’s delivery will be in this form, culminating in the submission of this work for peer review to a scientific journal. Importantly, the yearly progress reports can then also be viewed as expanding drafts working towards a formal journal article submission. For the first tranche of funding, we collaborated with Air Force Technical Applications Center (AFTAC) scientists to identify unclassified real-world data that demonstrate and validate our advanced error propagation methods. Collaboration includes visits to AFTAC and telecons. For this development, we illustrate the fusion of seismic, acoustic, optical, and surface effect signatures from an explosion. The mathematics and code being adapted to this specific application (Williams et al., 2021) involves physics models of multiple sensor signatures. We have also identified related physics models and have integrated them into code. Current methods of underground explosion yield estimation for the Threshold Test Ban Treaty (TTBT) have served the US treaty monitoring mission well for decades. A research objective of the Defense Nuclear Nonproliferation Research and Development (DNN R&D) office of the National Nuclear Security Administration (NNSA) has always been to provide new technical capabilities for monitoring lower thresholds. The general model and error propagation code to be developed in this project is based on significant advances in error modeling and propagation needed to analyze data at lower detection thresholds. The second tranche of funding for this project began on May 1, 2022, and planned work for the second tranche includes: i) completing the integration of physical model code into the general error model framework; this code accommodates a wide range of linear/nonlinear source models, fixed/ random effects, and frequentist/Bayesian analyses (the purpose of which is not to dictate to users how to analyze data, but instead to allow users the maximum flexibility in their work); ii) illustrative application of code to identified data, and; iii) initial planning with AFTAC researchers on delivery of code to the Common Development Environment at AFTAC, and continued writing of the planned final journal article submission (year two of this progress report), with particular emphasis on descriptions of data identified for this effort.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS1986

Historical seismicity near Chagos - A complex deformation zone in the equatorial Indian Ocean

The historical seismicity of the Chagos region of the Indian Ocean is analyzed, using earthquake relocation methods and a moment variance technique to determine the focal mechanisms of quakes occurring before 1964. Moment variance analysis showed a thrust faulting mechanism associated with the earthquake of 1944 near the Chagos-Laccadive Ridge; a strike-slip mechanism was associated with a smaller 1957 event occurring west of the Chagos Bank. The location of the 1944 event, one of the largest intraplate earthquakes known (1.4 x 10 to the 27th dyne/cm), would imply that the Chagos seismicity is due to a zone of tectonic deformation stretching across the equatorial Indian Ocean. The possibility of a slow diffuse boundary extending west of the Central Indian Ridge is also discussed. This boundary is confirmed by recent plate motion studies which suggest that it separates the Australian plate from a single Indo-Arabian plate.

Wiens, D. A.↗
DOE OSTI2024

Reverse Time Migration of Explosion-generated Infrasound and Ground-Coupled Airwaves

Accurately locating sources of seismic and infrasonic energy is integral to global monitoring of earthquakes and explosions. Infrasound arrivals times can be used to calculate the origins of events that generate acoustic energy. Picking times of emergent infrasound arrivals, however, can be difficult and prone to uncertainty. Reverse time migration (RTM) is a waveform based location method that does not rely on picked arrival times. Here we use RTM to locate a known chemical explosion that generated acoustic and acoustic-to-seismic signals on 26 and 108 receivers, respectively. All location predictions are less than 24 km from the known location with time errors of less than three minutes. We find strong overall agreement between our results and those of existing RTM and arrival time based methods. Our initial results suggest that RTM is a promising method of event location using acoustic arrivals recorded on both infrasound and seismic instrumentation.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

A Comparison of Machine Learning Methods of Association Tested on Dense Nodal Arrays

The association of phase picks to form events is one of the fundamental components of seismology. Large and dense sensor networks, such as >1000 geophone arrays (and distributed acoustic sensing), offer unique challenges in association due to the vast numbers of observations and high likelihood of errant picks. In addition, the large number of stations can greatly increase the time it takes to perform the association. For this reason, machine learning (ML) methods might provide a more optimal method of association for such networks. In this work, we examine how well ML methods (e.g., Gaussian mixture model association, PhaseLink, and Graph Earthquake Neural Interpretation Engine) can incorporate dense seismic arrays into regional networks and how well they handle the increasing numbers of stations. Here, we test their capabilities on two dense seismic deployments, one within Rock Valley Nevada (52 nodes and a 9-station sparse local network), and the LArge-n Seismic Survey in Oklahoma dense nodal array (>1800 vertical-component geophones). Processing data from these two different styles of dense seismic deployments allows testing of how the ML algorithms can merge array data with a broader regional network, how they deal with poorly picked phases, and how they handle anthropogenic noise. We compare the ML-associated bulletins to those obtained using the Rapid Earthquake Association and Location algorithm, a more traditional method of association. We find that there are very small differences in results between the methods for small networks (<100 stations) with low pick rates. For large networks (>1000), there are enough errant picks that some of the ML methods start to create false events out of noise. We also find that the ML methods vary in computation time significantly but are all faster than the traditional method tested here.

58 GEOSCIENCES↗
DOE OSTI2022

Estimating Explosion Yields Using Moment Tensor Solutions and Seismic Moment

We report seismic moment, a measurable and well-understood quantity of seismic sources, is used to estimate the yield of explosions. Application of such a method in the past, as in the manner of m b -derived yields, has been complicated by the effect of variations in the explosion working point, depth, and secondary source effects (such as spalling and tectonic release) on the observed moment. We start using the full (six-element) moment tensor solution, which can capture the relevant source physics and, at least in theory, better isolate the primary explosion source. The moment-to-yield ratio is then estimated using an explosion source model which, provided with emplacement conditions, can relate the two parameters. We discuss the major sources of uncertainty associated with the method, and calibrate it with chemical and nuclear explosions at the Nevada National Security Site. We then apply the method to published moment tensor solutions for the six declared North Korean nuclear explosions that occurred between 2006 and 2017. The results are mostly consistent with other yield estimates made using a variety of high-frequency methods. This technique is a new approach to estimating explosive yield and simple to implement, as much of the complexity is captured by the source models.

58 GEOSCIENCES↗
DOE OSTI2025

Effect of Time Window and Spectral Measurement Options on Empirical Green’s Function Analysis Using DAS Array and Seismic Stations

The recorded seismic waveform is a convolution of event source term, path term, and station term. Removing high-frequency attenuation due to path effect is a challenging problem. Empirical Green’s function (EGF) method uses nearly collocated small earthquakes to correct the path and station terms for larger events recorded at the same station. However, this method is subject to variability due to many factors. Here, we focus on three events that were well recorded by the seismic network and a rapid response distributed acoustic sensing (DAS) array. Using a suite of high-quality EGF events, we assess the influence of time window, spectral measurement options, and types of data on the spectral ratio and relative source time function (RSTF) results. Increased number of tapers (from 2 to 16) tends to increase the measured corner frequency and reduce the source complexity. Extended long time window (e.g., 30 s) tends to produce larger variability of corner frequency. The multitaper algorithm that simultaneously optimizes both target and EGF spectra produces the most stable corner-frequency measurements. The stacked spectral ratio and RSTF from the DAS array are more stable than two nearby seismic stations, and are comparable to stacked results from the seismic network, suggesting that DAS array has strong potential in source characterization.

58 GEOSCIENCES↗
DOE OSTI2020

Nowcasting Earthquakes in Southern California With Machine Learning: Bursts, Swarms, and Aftershocks May Be Related to Levels of Regional Tectonic Stress

Abstract Seismic bursts in Southern California are sequences of small earthquakes strongly clustered in space and time and include seismic swarms and aftershock sequences. A readily observable property of these events, the radius of gyration ( R G ), allows us to connect the bursts to the temporal occurrence of the largest M ≥ 7 earthquakes in California since 1984. In the Southern California earthquake catalog, we identify hundreds of these potentially coherent space‐time structures in a region defined by a circle of radius 600 km around Los Angeles. We compute R G for each cluster then filter them to identify those bursts with large numbers of events closely clustered in space, which we call “compact” bursts. Our basic assumption is that these compact bursts reflect the dynamics associated with large earthquakes. Once we have filtered the burst catalog, we apply an exponential moving average to construct a time series for the Southern California region. We observe that the R G of these bursts systematically decreases prior to large earthquakes, in a process that we might term “radial localization.” The R G then rapidly increases during an aftershock sequence, and a new cycle of “radial localization” then begins. These time series display cycles of recharge and discharge reminiscent of seismic stress accumulation and release in the elastic rebound process. The complex burst dynamics we observe are evidently a property of the region as a whole, rather than being associated with individual faults. This new method allows us to improve earthquake nowcasting, which is a technique to evaluate the current state of hazard in a seismically active region.

58 GEOSCIENCES↗