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At least 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Nonlinear dynamics induced in a structure by seismic and environmental loading

In this study,we show that under very weak dynamic and quasi-static deformation, that is orders of magnitude below the yield deformation of the equivalent stress strain curve (around 10 -3 ), the elastic parameters of a civil engineering structure (resonance frequency and damping) exhibit nonlinear softening and recovery. These observations bridge the gap between laboratory and seismic scales where elastic nonlinear behavior has been previously observed. Under weak seismic or atmospheric loading, modal frequencies are modified by around 1% and damping by more than 100% for strain levels between 10 -7 and 10 -4 . These observations support the concept of universal behavior of nonlinear elastic behavior in diverse systems, including granular materials and damaged solids that scale from millimeter dimensions to the scale of structures to fault dimensions in the Earth.

58 GEOSCIENCES↗
DOE OSTI2017

Shear-wave anisotropy reveals pore fluid pressure–induced seismicity in the U.S. midcontinent

Seismicity in the U.S. midcontinent has increased by orders of magnitude over the past decade. Spatiotemporal correlations of seismicity to wastewater injection operations have suggested that injection-related pore fluid pressure increases are inducing the earthquakes. We present direct evidence linking earthquake occurrence to pore pressure increase in the U.S. midcontinent through time-lapse shear-wave (S-wave) anisotropy analysis. Since the onset of the observation period in 2010, the orientation of the fast S-wave polarization has flipped from inline with the maximum horizontal stress to inline with the minimum horizontal stress, a change known to be associated with critical pore pressure buildup. The time delay between fast and slow S-wave arrivals exhibits increased variance through time, which is common in critical pore fluid settings. Near-basement borehole fluid pressure measurements indicate pore pressure increase in the region over the earthquake monitoring period.

58 GEOSCIENCES↗
DOE OSTI2013

Physics-based seismic hazard analysis on petascale heterogeneous supercomputers, In: SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

We have developed a highly scalable and efficient GPU-based finite-difference code (AWP) for earthquake simulation that implements high throughput, memory locality, communication reduction and communication/computation overlap and achieves linear scalability on Cray XK7 Titan at ORNL and NCSA's Blue Waters system. We simulate realistic 0-10 Hz earthquake ground motions relevant to building engineering design using high-performance AWP. Moreover, we show that AWP provides a speedup by a factor of 110 in key strain tensor calculations critical to probabilistic seismic hazard analysis (PSHA). These performance improvements to critical scientific application software, coupled with improved co-scheduling capabilities of our workflow-managed systems, make a statewide hazard model a goal reachable with existing supercomputers. The performance improvements of GPU-based AWP are expected to save millions of core-hours over the next few years as physics-based seismic hazard analysis is developed using heterogeneous petascale supercomputers.

Computer Science↗
DOE OSTI2019

Porting a 3D seismic modeling code (SW4) to CORAL machines

Seismic waves fourth order (SW4) solves the seismic wave equations on Cartesian and curvilinear grids using large compute clusters with O (100,000) cores. In this article we discuss the porting of SW4 to run on the CORAL architecture using the RAJA performance portability abstraction layer. The performances of key kernels using RAJA and CUDA are compared to estimate the performance penalty of using the portability abstraction layer. Code changes required for efficiency on GPUs and minimizing time spent in Message Passing Interface (MPI) are discussed. This article describes a path for efficiently porting large code bases to GPU-based machines while avoiding the pitfalls of a new architecture in the early stages of its deployment. Current bottlenecks in the code are discussed along with possible architectural or software mitigations. SW4 runs 28× faster on one 4-GPU CORAL node than on a CTS-1 node (Dual Intel Xeon E5-2695 v4). SW4 is now in routine use on problems of unprecedented resolution (203 billion grid points) and scale on 1,200 nodes of Summit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Seismic Waves, 4th order accurate

SW4 is a program for simulating seismic wave propagation on parallel computers. SW4 colves the seismic wave equations in Cartesian corrdinates. It is therefore appropriate for regional simulations, where the curvature of the earth can be neglected. SW4 implements a free surface boundary condition on a realistic topography, absorbing super-grid conditions on the far-field boundaries, and a kinematic source model consisting of point force and/or point moment tensor source terms. SW4 supports a fully 3-D heterogeneous material model that can be specified in several formats. SW4 can output synthetic seismograms in an ASCII test format, or in the SAC finary format. It can also present simulation information as GMT scripts, whixh can be used to create annotated maps. Furthermore, SW4 can output the solution as well as the material model along 2-D grid planes.

Source record↗
DOE OSTI2023

Seismic Response Processing Module

The seismic response processing module is a Java based library that provides support for removing instrument response signals from seismic recordings. The response module provides support for EvalResp, PZF, PAZ, FAP, PAZFIR, PAZFAP, FIRFAP, and CSS response types and parallel processing support for transfer operations. Additionally, the response module uses the JSR-363 units of measurement specification to allow for transfer to and from a wide range of units types to represent the time-series.

Dodge, Douglas↗
DOE OSTI2024

Bayesian OED for Seismic Monitoring

SAND2024-13870O The Bayesian OED (Optimal Experiment Design) for Seismic Monitoring code provides the tools to analyze and optimize seismic monitoring networks using Bayesian OED. This method designs a utility function for experiments (network designs) using network analysis and network optimization. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Catanach, Thomas↗
DOE OSTI2016

A source-synchronous filter for uncorrelated receiver traces from a swept-frequency seismic source

We have developed a novel algorithm to reduce noise in signals obtained from swept-frequency sources by removing out-of-band external noise sources and distortion caused from unwanted harmonics. The algorithm is designed to condition nonstationary signals for which traditional frequency-domain methods for removing noise have been less effective. The source synchronous filter (SSF) is a time-varying narrow band filter, which is synchronized with the frequency of the source signal at all times. Because the bandwidth of the filter needs to account for the source-to-receiver propagation delay and the sweep rate, SSF works best with slow sweep rates and moveout-adjusted waveforms to compensate for source-receiver delays. The SSF algorithm was applied to data collected during a field test at the University of California Santa Barbara’s Garner Valley downhole array site in Southern California. At the site, a 45 kN shaker was mounted on top of a one-story structure and swept from 0 to 10 Hz and back over 60 s (producing useful seismic waves greater than 1.6 Hz). The seismic data were captured with small accelerometer and geophone arrays and with a distributed acoustic sensing array, which is a fiber-optic-based technique for the monitoring of elastic waves. The result of the application of SSF on the field data is a set of undistorted and uncorrelated traces that can be used in different applications, such as measuring phase velocities of surface waves or applying convolution operations with the encoder source function to obtain traveltimes. Lastly, the results from the SSF were used with a visual phase alignment tool to facilitate developing dispersion curves and as a prefilter to improve the interpretation of the data.

58 GEOSCIENCES↗
DOE OSTI2020

Gas hydrate quantification in Walker Ridge block 313, Gulf of Mexico, from full-waveform inversion of ocean-bottom seismic data

The Gulf of Mexico (GOM) Joint Industry Project Leg 2 logging-while-drilling data in Walker Ridge lease block 313 (WR313) in the GOM detected gas hydrate in coarse- and fine-grained sediments at sites WR313-G and WR313-H. The coarse-grained units are thin ([Formula: see text]) and highly saturated, whereas the fine-grained unit is thick (approximately 200 m) with low saturation and fracture-filling gas hydrate. Unlike its coarse-grained counterpart, the seismic character of the fine-grained unit does not clearly indicate the presence of gas hydrate, which would likely have remained undiscovered in the absence of drilling. In this paper, through frequency-domain acoustic full-waveform inversion (FWI) of ocean-bottom seismometer data along a 2D multichannel seismic transect near sites WR313-G and WR313-H, we detect and quantify gas hydrate in the fine-grained unit. Key results are as follows: First, the base of the gas hydrate stability zone, which is not obvious in the reflection profile, can be discerned in the FWI results. Second, the gas hydrate in the fine-grained unit is mainly confined to the area between two sets of opposite-dipping normal faults implying that the fault architecture may be partially responsible for this gas hydrate accumulation and distribution.

Geochemistry & Geophysics↗
DOE OSTI2022

Seismic monitoring of well integrity

Seismic tube waves, produced by flexure of the well boundary, pressure changes in the fluid in the well, and deformation of the material immediately surrounding the well, are particularly sensitive to variations in the state of the well. In this work, we evaluate a direct approach for generating and observing tube waves as a means of detecting well damage. While we find that it can be difficult to reliably excite observable tube waves without a very strong surface source, time-frequency techniques can be employed to increase the detectability of tube wave reflections. New technologies, particularly distributed acoustic sensing, hold great promise for evaluating well integrity by monitoring tube waves, temperature changes, and seismic noise due to well deformation and fluid leakage.

58 GEOSCIENCES↗
DOE OSTI2023

Utah FORGE 5-2419: Seismicity-Permeability Relationships Probed via Nonlinear Acoustic Imaging - Workshop Presentation

This is a presentation on the Seismicity-Permeability Relationships Probed via Nonlinear Acoustic Imaging project by Pennsylvania State University, presented by Derek Elsworth. The project's objectives were to explore controls and acoustic signatures of aseismic through seismic evolution of friction-stability-permeability relationships on fractures in shear-reactivation, link this reactivation to key features of the pre-existing stress state, and upscale these indexes to reservoir scale to drive successful reservoir stimulation. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 8, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.

15 GEOTHERMAL ENERGY↗
DOE OSTI2011

Marine Corps Base Camp Lejeune Onslow Bay Seismic Sub-bottom

Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of development pressures in surrounding areas, impairments due to other anthropogenic disturbances, and increasing requirements for compliance with environmental regulations. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and to optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a minimum 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the first six years of the program (DCERP1) are presented here.The overarching objectives of DCERP are to: (1) understand the effects of military training activities, infrastructure development, and other coastal military installations; (2) develop models, tools, and indicators to evaluate ecosystem health; and (3) recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.This data package includes seismic sub-bottom profile data are from the shallow (-2 to -11.5m, NAVD88) nearshore region of Onslow Beach. Data were collected in 2007 using a high-resolution Edgetech Chirp 512i (average resolution of 10-20cm). The position of each data point was related to NAD83 using RTK-GPS. Data were collected at a frequency range of 0.5-8 kHz. Heave is apparent in the seismic profiles because no swell filter was applied during acquisition or post-processing. Post-processing was performed using Chesapeake Technology SonarWiz MAP5. Georeferenced .sd files (datums: UTM, Zone 18N, meters & NAVD88, meters) were generated using IVS Fledermaus Pro ver. 7.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Significantly Improving Regional Seismic Amplitude Tomography at Higher Frequencies by Determining S -Wave Bandwidth

Characterizing regional seismic signals continues to be a difficult problem due to their variability. Calibration of these signals is very important to many aspects of monitoring underground nuclear explosions, including detecting seismic signals, discriminating explosions from earthquakes, and reliably estimating magnitude and yield. Amplitude tomography, which simultaneously inverts for source, propagation, and site effects, is a leading method of calibrating these signals. A major issue in amplitude tomography is the data quality of the input amplitude measurements. Pre-event and prephase signal-to-noise ratio (SNR) tests are typically used but can frequently include bad signals and exclude good signals. The deficiencies of SNR criteria, which are demonstrated here, lead to large calibration errors. To ameliorate these issues, we introduce a semi-automated approach to assess the bandwidth of a spectrum where it behaves physically. We determine the maximum frequency (denoted as F max ) where it deviates from this behavior due to inflections at which noise or spurious signals start to bias the spectra away from the expected decay. We compare two amplitude tomography runs using the SNR and new F max criteria and show significant improvements to the stability and accuracy of the tomography output for frequency bands higher than 2 Hz by using our assessments of valid S-wave bandwidth. We compare Q estimates, P/S residuals, and some detailed results to explain the improvements. Lastly, for frequency bands higher than 4 Hz, needed for effective P/S discrimination of explosions from earthquakes, the new bandwidth criteria sufficiently fix the instabilities and errors so that the residuals and calibration terms are useful for application.

58 GEOSCIENCES↗
DOE OSTI2018

Dynamic Tuning of Seismic Signal Detector Trigger Levels for Local Networks

The quality of automatic signal detections from sensor networks depends on individual detector trigger levels (TLs) from each sensor. The largely manual process of identifying effective TLs is painstaking and does not guarantee optimal configuration settings, yet achieving superior automatic detection of signals and ultimately, events, is closely related to these parameters. In this paper, we present a Dynamic Detector Tuning (DDT) system that automatically adjusts effective TL settings for signal detectors to the current state of the environment by leveraging cooperation within a local neighborhood of network sensors. After a stabilization period, the DDT algorithm can adapt in near-real time to changing conditions and automatically tune a signal detector to identify (detect) signals from only events of interest. Our current work focuses on reducing false signal detections early in the seismic signal processing pipeline, which leads to fewer false events and has a significant impact on reducing analyst time and effort. This system provides an important new method to automatically tune detector TLs for a network of sensors and is applicable to both existing sensor performance boosting and new sensor deployment. Finally, with ground truth on detections from a local neighborhood of seismic sensors within a network monitoring the Mount Erebus volcano in Antarctica, we show that DDT reduces the number of false detections by 18% and the number of missed detections by 11% when compared with optimal fixed TLs for all sensors.

58 GEOSCIENCES↗
DOE OSTI2018

Depth Discrimination Using Rg-to-Sg Spectral Amplitude Ratios for Seismic Events in Utah Recorded at Local Distances

Short-period fundamental-mode Rayleigh waves (Rg) are commonly observed on seismograms of anthropogenic seismic events and shallow, naturally occurring tectonic earthquakes (TEs) recorded at local distances. In the Utah region, strong Rg waves traveling with an average group velocity of about 1.8 km/s are observed at ~1 Hz on waveforms from shallow events ( depth<10 km ) recorded at distances up to about 150 km. At these distances, Sg waves, which are direct shear waves traveling in the upper crust, are generally the dominant signals for TEs. Here in this study, we leverage the well-known notion that Rg amplitude decreases dramatically with increasing event depth to propose a new depth discriminant based on Rg-to-Sg spectral amplitude ratios. The approach is successfully used to discriminate shallow events (both earthquakes and anthropogenic events) from deeper TEs in the Utah region recorded at local distances ( <150 km ) by the University of Utah Seismographic Stations (UUSS) regional seismic network. Using Mood’s median test, we obtained probabilities of nearly zero that the median Rg-to-Sg spectral amplitude ratios are the same between shallow events on the one hand (including both shallow TEs and anthropogenic events), and deeper earthquakes on the other, suggesting that there is a statistically significant difference in the estimated Rg-to-Sg ratios between the two populations. We also observed consistent disparities between the different types of shallow events (e.g., mining blasts vs. mining-induced earthquakes), implying that it may be possible to separate the subpopulations that make up this group. Lastly, this suggests that using local distance Rg-to-Sg spectral amplitude ratios one can not only discriminate shallow events from deeper events but may also be able to discriminate among different populations of shallow events.

58 GEOSCIENCES↗
DOE OSTI2019

Classification of Local Seismic Events in the Utah Region: A Comparison of Amplitude Ratio Methods with a Spectrogram-Based Machine Learning Approach

Abstract The capability to discriminate low-magnitude earthquakes from low-yield anthropogenic sources, both detectable only at local distances, is of increasing interest to the event monitoring community. Here, we used a dataset of seismic events in Utah recorded during a 14-day period (1–14 January 2011) by the University of Utah Seismic Stations network to perform a comparative study of event classification at local scale using amplitude ratio (AR) methods and a machine learning (ML) approach. The event catalog consists of 7377 events with magnitudes M C ranging from –2 and lower up to 5.8. Events were subdivided into six populations based on location and source type: tectonic earthquakes (TEs), mining-induced events (MIEs), and mining blasts from four known mines (WMB, SMB, LMB, and CQB). The AR approach jointly exploits Pg-to-Sg phase ARs and Rg-to-Sg spectral ARs in multivariate quadratic discriminant functions and was able to classify 370 events with high signal quality from the three groups with sufficient size (TE, MIE, and SMB). For that subset of the events, the method achieved success rates between about 80% and 90%. The ML technique used trained convolutional neural network (CNN) models to classify the populations. The CNN approach was able to classify the subset of events with accuracies between about 91% and 98%. Because the neural network approach does not have a minimum signal quality requirement, we applied it to the entire event catalog, including the abundant extremely low-magnitude events, and achieved accuracies of about 94%–100%. Furthermore, we compare the AR and ML methodologies using a broad set of criteria and conclude that a major advantage to ML methods is their robustness to low signal-to-noise ratio data, allowing them to classify significantly smaller events.

58 GEOSCIENCES↗
DOE OSTI2021

Deep Learning Denoising Applied to Regional Distance Seismic Data in Utah

Seismic waveform data are generally contaminated by noise from various sources. Suppressing this noise effectively so that the remaining signal of interest can be successfully exploited remains a fundamental problem for the seismological community. To date, the most common noise suppression methods have been based on frequency filtering. These methods, however, are less effective when the signal of interest and noise share similar frequency bands. Inspired by source separation studies in the field of music information retrieval (Jansson et al., 2017) and a recent study in seismology (Zhu et al., 2019), we implemented a seismic denoising method that uses a trained deep convolutional neural network (CNN) model to decompose an input waveform into a signal of interest and noise. In our approach, the CNN provides a signal mask and a noise mask for an input signal. The short-time Fourier transform (STFT) of the estimated signal is obtained by multiplying the signal mask with the STFT of the input signal. To build and test the denoiser, we used carefully compiled signal and noise datasets of seismograms recorded by the University of Utah Seismograph Stations network. Results of test runs involving more than 9000 constructed waveforms suggest that on average the denoiser improves the signal-to-noise ratios (SNRs) by ~5 db, and that most of the recovered signal waveforms have high similarity with respect to the target waveforms (average correlation coefficient of ~0.80) and suffer little distortion. Application to real data suggests that our denoiser achieves on average a factor of up to ~2-5 improvement in SNR over band-pass filtering and can suppress many types of noise that band-pass filtering cannot. For individual waveforms, the improvement can be as high as ~15 db.

58 GEOSCIENCES↗
DOE OSTI2022

Seismic Tomographic Modeling of the Crust and Upper Mantle beneath Israel and the Middle East: Improved Resolution through Optimized Model Parameterization

Accurate regional seismic travel-time (RSTT) predictions rely on regional phases (e.g., Pg, Lg, Pn, Sn ) to account for 3D effects in the crust and upper mantle that are not captured by 1D models traditionally used for real-time location. The RSTT prediction model accounts for regional-scale crust and upper mantle structure globally by incorporating regional seismic phases into its travel-time calculations. Previous versions of the RSTT model have used a constant grid cell size of 1°. To improve the tomographic accuracy of recovering velocity structure at regional scales, we, in this study, perform data-driven grid refinement on the RSTT model down to a 0.125° grid (~14 km) in pursuit of two main goals: (1) to test the limits of RSTT capability and accuracy of determined velocity structure through variable grid refinement and (2) to image smaller structures in Israel and the Middle East and illuminate upper mantle dynamics operating in this complex tectonic area. We investigate the effects of model parameterization as grid cell size decreases and the trade-offs between recovered velocity structures. Our final dataset includes 4751 events and 499 stations that recorded 79,344 Pn and 7489 Pg . The variable grid refinement method allows recovery of finer-scale velocity structures and reduces travel-time residuals in areas with the highest data coverage. At smaller grid cell sizes, longer paths need to be upweighted to stabilize the inversion. Results illuminate tectonic features undefined in coarser grid-size models; in particular, we observe mantle perturbations related to the subduction zone around the Cyprian arc and crustal anomalies near the Dead Sea fault and throughout the Anatolian plate.

58 GEOSCIENCES↗