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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Spatially Dependent Seismic Wavefield Scattering from an Underground Chemical Explosion: Analysis of the Source Physics Experiment Dry Alluvium Geology Large- N Array

Explosion sources have been observed to generate significant shear‐wave energy despite their isotropic nature. To investigate this phenomenon, we conduct an analysis of the seismic data collected as part of the Source Physics Experiment (SPE): Dry Alluvium Geology (DAG) and investigate the generation of shear‐wave energy via scattering. The data were produced by three underground chemical explosions and consist of three‐component seismograms, which were recorded by the DAG Large‐N array. Synthetic tests suggest that for the DAG experiments, small‐scale stochastic heterogeneities, defined as features with correlation lengths of 10–100s of meters, are more effective than large‐scale geologic structure (scales >1–10 km) at reproducing the scattering of explosion generated wavefields observed at DAG. We analyze the seismic data for spatially variable ratios between transversely and radially polarized seismic energy, and then estimate the mean free path of P and S waves. All analyses are conducted within a frequency band of 5–50 Hz. The ratio of transversely to radially polarized energy is the highest in the east and west portion of the Large‐N array. In addition, the magnitude of the estimated S‐wave mean free path is shorter in the eastern portion of the Large‐N array. This variation indicates that the eastern area of the DAG array is where more scattering is occurring, suggesting azimuthal dependence of P‐to‐P and P‐to‐S scattering. Furthermore, this azimuthal dependence of P‐to‐S scattering can have implications for explosion discrimination based on spectral ratios of seismic wave types, because the general assumption is that explosions do not generate shear‐wave energy. Synthetic tests modeling only larger‐scale geologic structure had lower transversely polarized energy (only four stations showing a transversely to radially polarized energy ratio greater than 1) and fewer stations (<10) displaying shorter (<300 m) mean free paths than what was observed in the DAG data results.

Darrh, Andréa Nicole↗
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 OSTI2019

Locating Surface Explosions by Combining Seismic and Infrasound Data

An outline of a Bayesian source location framework for using seismic and acoustic observations is developed and tested on synthetic and real data. Seismic and acoustic phenomena are both commonly used in detection and location of a variety of natural or man–made events, such as volcanic eruptions, quarry blasts, and military exercises. Typically, seismic and acoustic observations have been utilized independently of each other. Here, we outline a Bayesian formulation for combining the two observations in a single estimate of the location and origin time. Using realistic estimates of uncertainty, we subsequently explore how combining the different observation types can benefit event location at local to near–regional distances. We apply the method to synthetic data and to real observations from a mining blast in Bingham Mine in Utah. Our findings suggest that, for relatively sparse or azimuthally limited observations, the relative strengths of the two different phenomenologies enable more precise joint–event localization than either seismic or infrasonic measurements alone.

58 GEOSCIENCES↗
DOE OSTI2020

Semisupervised Learning for Seismic Monitoring Applications

The impressive performance that deep neural networks demonstrate on a range of seismic monitoring tasks depends largely on the availability of event catalogs that have been manually curated over many years or decades. However, the quality, duration, and availability of seismic event catalogs vary significantly across the range of monitoring operations, regions, and objectives. Semisupervised learning (SSL) enables learning from both labeled and unlabeled data and provides a framework to leverage the abundance of unreviewed seismic data for training deep neural networks on a variety of target tasks. We apply two SSL algorithms (mean-teacher and virtual adversarial training) as well as a novel hybrid technique (exponential average adversarial training) to seismic event classification to examine how unlabeled data with SSL can enhance model performance. In general, we find that SSL can perform as well as supervised learning with fewer labels. We also observe in some scenarios that almost half of the benefits of SSL are the result of the meaningful regularization enforced through SSL techniques and may not be attributable to unlabeled data directly. Lastly, the benefits from unlabeled data scale with the difficulty of the predictive task when we evaluate the use of unlabeled data to characterize sources in new geographic regions. Finally, in geographic areas where supervised model performance is low, SSL significantly increases the accuracy of source-type classification using unlabeled data.

58 GEOSCIENCES↗
DOE OSTI2021

The Silencing of U.S. Campuses Following the COVID-19 Response: Evaluating Root Mean Square Seismic Amplitudes Using Power Spectral Density Data

In response to the COVID-19 global pandemic, many populated and active regions have become deserted and show significant reductions in their background seismicity, especially campuses across the United States (U.S.). Seismic sensors located in the vicinity of or within U.S. campuses show that anthropogenic seismic noise remains elevated during the ordinary, nonpandemic, academic year, only subduing during periods of recess (e.g., winter break). Here, we use power spectral density (PSD) data computed by the Incorporated Research Institutions for Seismology Data Management Center for quality assessment to calculate root mean square (rms) amplitude and analyze the effects of the COVID-19 school closures. We processed and analyzed PSD data for 46 seismic stations located within 50 m of a U.S. university or college. Results show that 42 campus stations show an overall rms drop following a statewide school closure.

58 GEOSCIENCES↗
DOE OSTI2023

Modeling-Based Assessment of Deep Seismic Potential Induced by Geologic Carbon Storage

Induced seismicity is an inherent risk associated with geologic carbon storage (GCS) in deep rock formations that could contain undetected faults prone to failure. Modeling-based risk assessment has been implemented to quantify the potential of injection-induced seismicity, but typically simplified multiscale geologic features or neglected multiphysics coupled mechanisms because of the uncertainty in field data and computational cost of field-scale simulations, which may limit the reliable prediction of seismic hazard caused by industrial-scale CO 2 storage. The degree of lateral continuity of the stratigraphic interbedding below the reservoir and depth-dependent fault permeability can enhance or inhibit pore-pressure diffusion and corresponding poroelastic stressing along a basement fault. In conclusion, this study presents a rigorous modeling scheme with optimal geological and operational parameters needed to be considered in seismic monitoring and mitigation strategies for safe GCS.

58 GEOSCIENCES↗
DOE OSTI2025

Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques

Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine‐learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine‐learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density‐based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image‐derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle‐related labels compared to the camera‐derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short‐term average/long‐term average method and k‐means clustering. Our results suggest that seismic data, when analyzed with machine‐learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.

Chai, Chengping [Oak Ridge National Laboratory (OR↗
DOE OSTI2026

Seismic Event Characterization Using Full Moment Tensors on the Hypersphere

Moment tensor solutions provide insights into the deformation that has occurred in the source region of a seismic event and are therefore of great value in identifying different types of seismic sources, such as when monitoring for underground nuclear tests. Despite this utility, inversion of waveforms recorded by seismometers for their full seismic moment tensor is not yet routine, and development of robust methods to classify events based on this information is in its infancy. Here, we assemble an inventory of 1405 full moment tensor solutions that include explosive, earthquake, and collapse events, and investigate the use of anisotropic probability distribution functions on the 5D hypersphere to discriminate between these sources. Using a Bayesian classifier, we obtain optimal success rates of 98.4% across all events and demonstrate that modification of the prior probabilities provides a natural way to alter the balance between not missing desirable events (such as explosions) versus misclassifying large numbers of undesired events (such as earthquakes). The approach is specifically designed to progress from traditional, bipolar event screening metrics to more generalized event identification across multiple types of seismic sources. Despite current databases containing insufficient numbers of events to definitively demonstrate at present, we also find intriguing evidence of subgroupings within individual source populations on the hypersphere, for example, between chemical and nuclear explosions, raising the potential possibility of discriminating between these event types in the future.

Geosciences↗
DOE OSTI2024

Determining Stress Orientation in Rock Valley, Nevada, Using Ambient Seismic Noise

The stress field and the mechanical properties of rocks are important to consider for nuclear explosion monitoring due to their effect on seismic wave radiation from earthquakes and explosions. At the Rock Valley Direct Comparison site, the regional orientation of the maximum horizontal compressive stress (SH max ) is well constrained, but it is unknown whether there are local heterogeneities. Here, I show that stress-induced anisotropy in nonlinear elasticity can be used to estimate the orientation of SH max . Rocks have compliant internal contacts, such as fractures and mineral grain boundaries, that respond to applied strains more strongly than individual mineral crystals. This strain response is asymmetric between compressive and dilatational strains and is affected by anisotropy in the ambient stress field. Traditional seismic velocity measurements are of strain-averaged velocity, which is less sensitive than nonlinear elasticity to the behavior of compliant internal contacts and therefore the stress field and fracture behavior. My results show that the orientation of SH max measured using ambient seismic noise aligns with regional estimates, with some heterogeneity that can also be explained by the limitations of the seismic array. I demonstrate the potential of a passive technique for monitoring the stress field in places that currently lack local measurements.

58 GEOSCIENCES↗
DOE OSTI2022

Rock Physics-Based Data Assimilation of Integrated Continuous Active-Source Seismic and Pressure Monitoring Data during Geological Carbon Storage

Summary There has been substantial controversy concerning the role of geological carbon storage (GCS) in sequestering anthropogenic carbon emissions to mitigate climate change and global warming. Arguments center on the inability to monitor a geological storage site precisely and continuously, especially highlighting the associated costs and spatiotemporal trade-offs when using conventional subsurface monitoring techniques (well logs, core samples, chemical tracers, and 4D seismics). Active surveillance of GCS sites is essential for managing and mitigating potential leaks but is also required by regulation. With the goal of enhancing the monitoring capability at GCS sites, we present a rock physics-based joint data assimilation model to study a popular GCS site at Cranfield, Mississippi, USA. Synthetic continuous active-source seismic monitoring (CASSM) data (in the form of Vp and Qp measurements) and wellbore pressure monitoring data are assimilated with an ensemble of reservoir realizations to monitor gas saturation and reservoir pressure changes over a period of 100 years. Synthetic seismic attributes are generated using rock physics models (RPMs) and wellbore pressure monitoring data are extracted from the ground truth. Two assimilation methods, ensemble Kalman filter (EnKF) and ensemble Kalman smoother (EnKS), are tested in an observation system simulation experiment (OSSE) environment to assess the prediction accuracy of the individual and composite observation systems. The joint monitoring system achieves more accurate estimates of gas saturation and pressure, across the time span from start of injection to end of forecast, as compared to a single type of monitoring tool and irrespective of data assimilation algorithm choice. These results indicate that jointly assimilated data from two types of sensors (in this case, crosswell seismic and downhole pressure) may lead to a more risk-reducing monitoring design. One would expect that more data, vis-à-vis inclusion of a new sensor type, will improve the accuracy of any GCS monitoring system. However, from a practical standpoint, one important question is whether such a gain in accuracy is worth the additional cost associated with the new sensor. This paper focuses on quantifying the gain in accuracy, such that a practitioner can answer this question.

Engineering↗
DOE OSTI2012

Improving the Monitoring, Verification, and Accounting of CO 2 Sequestered in Geologic Systems with Multicomponent Seismic Technology and Rock Physics Modeling

Research done in this study showed that P-SV seismic data provide better spatial resolution of geologic targets at our Appalachian Basin study area than do P-P data. This finding is important because the latter data (P-P) are the principal seismic data used to evaluate rock systems considered for CO 2 sequestration. The increase in P-SV 1 resolution over P-P resolution was particularly significant, with P-SV 1 wavelengths being approximately 40-percent shorter than P-P wavelengths. CO 2 sequestration projects across the Appalachian Basin should take advantage of the increased resolution provided by converted-shear seismic modes relative to P-wave seismic data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Infrasound Generation from the HH Seismic Hammer.

The HH Seismic hammer is a large, "weight-drop" source for active source seismic experiments. This system provides a repetitive source that can be stacked for subsurface imaging and exploration studies. Although the seismic hammer was designed for seismological studies it was surmised that it might produce energy in the infrasonic frequency range due to the ground motion generated by the 13 metric ton drop mass. This study demonstrates that the seismic hammer generates a consistent acoustic source that could be used for in-situ sensor characterization, array evaluation and surface-air coupling studies for source characterization.

Jones, Kyle Richard↗
DOE OSTI2016

Probabilistic Seismic Hazards Update for LLNL

Fugro Consultants, Inc. (FCL) completed the Probabilistic Seismic Hazard Analysis (PSHA) performed for Building 332 at the Lawrence Livermore National Laboratory (LLNL), near Livermore, CA. The study performed for the LLNL site includes a comprehensive review of recent information relevant to the LLNL regional tectonic setting and regional seismic sources in the vicinity of the site and development of seismic wave transmission characteristics. The Seismic Source Characterization (SSC), documented in Project Report No. 2259-PR-02 (FCL, 2015b), and Ground Motion Characterization (GMC), documented in Project Report No. 2259-PR-06 (FCL, 2015a) were developed in accordance with ANS/ANSI 2.29- 2008 Level 2 PSHA guidelines. The ANS/ANSI 2.29-2008 Level 2 PSHA framework is documented in Project Report No. 2259-PR-05 (FCL, 2016a). The Hazard Input Document (HID) for input into the PSHA developed from the SSC and GMC is presented in Project Report No. 2259-PR-04 (FCL, 2016b). The site characterization used as input for development of the idealized site profiles including epistemic uncertainty and aleatory variability is presented in Project Report No. 2259-PR-03 (FCL, 2015c). The PSHA results are documented in Project Report No. 2259-PR-07 (FCL, 2016c).

58 GEOSCIENCES↗
DOE OSTI2016

Development of a low cost method to estimate the seismic signature of a geothermal field form ambient noise analysis.

A new, cost effective and non-invasive exploration method using ambient seismic noise has been tested at Soda Lake, NV, with promising results. The material included in this report demonstrates that, with the advantage of initial S-velocity models estimated from ambient noise surface waves, the seismic reflection survey, although with lower resolution, reproduces the results of the active survey when the ambient seismic noise is not contaminated by strong cultural noise. Ambient noise resolution is less at depth (below 1000m) compared to the active survey. In general, the results are promising and useful information can be recovered from ambient seismic noise, including dipping features and fault locations.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Probabilistic Seismic Hazards Update for LLNL: PSHA Results Report

This report presents the Probabilistic Seismic Hazard Analysis (PSHA) performed for Building 332 at the Lawrence Livermore National Laboratory (LLNL), near Livermore, CA by Fugro Consultants, Inc. (FCL). This report is specific to Building 332 only and not to other portions of the Laboratory. The study performed for the LLNL site includes a comprehensive review of recent information relevant to the LLNL regional tectonic setting and regional seismic sources in the vicinity of the site and development of seismic wave transmission characteristics. The Seismic Source Characterization (SSC), documented in Project Report No. 2259-PR-02 (FCL, 2015a), and Ground Motion Characterization (GMC), documented in Project Report No. 2259-PR-06 (FCL, 2015c) were developed in accordance with ANS/ANSI 2.29-2008 Level 2 PSHA guidelines. The ANS/ANSI 2.29-2008 Level 2 PSHA framework is documented in Project Report No. 2259-PR-05 (FCL, 2016a). The Hazard Input Document (HID) for input into the PSHA developed from the SSC is presented in Project Report No. 2259-PR-04 (FCL, 2016b). The site characterization used as input for development of the idealized site profiles including epistemic uncertainty and aleatory variability is presented in Project Report No. 2259-PR-03 (FCL, 2015b).

42 ENGINEERING↗
DOE OSTI2018

Computation of the Seismic Stability of Earth Retaining Structures Final Report CRADA No. TSB-954-94

The purpose of this CRADA project was to evaluate the seismic stability of block retaining wall systems. Retaining wall systems are used extensively in private and commercial developments. This study was designed to develop and demonstrate a computer modeling technology to be used to predict the seismic stability of any block wall system design. The nonlinear finite element computer programs developed at LLNL and employed in the Computational Earthquake Initiative were utilized in this small business CRADA to analyze the seismic stability of the block retaining walls. The unique capability of the LLNL programs to rigorously model frictional contact in a dynamic analysis problem were used in a computer simulation of the dynamic interaction the block waiVsoil systems under seismic excitation.

58 GEOSCIENCES↗
DOE OSTI2019

Washington State Highway Bridge Seismic Screening Tool (BSST) (Technical Report)

The Regional Resiliency Assessment Program (RRAP) is a cooperative assessment of specific critical infrastructure within a designated geographic area and a regional analysis of the surrounding infrastructure that addresses a range of infrastructure resilience issues that could have regionally and nationally significant consequences. In 2017, the U.S. Department of Homeland Security’s Cybersecurity and Infrastructure Security Agency (CISA) sponsored the Washington State Transportation Systems RRAP project in coordination with the Washington Emergency Management Division (EMD), Washington State Department of Transportation (WSDOT), and other regional stakeholders. This project is focused on assessing the impacts of a Cascadia Subduction Zone (CSZ) earthquake on state transportation systems and, in particular, how those impacts may affect the ability of emergency response efforts to move supplies into the region. The intended outcome of this analysis is the prioritization of transportation routes and modes for additional planning, investment, hardening, or other activities to enhance their resilience - and therefore, to enhance their ability to support response and recovery efforts following a CSZ earthquake. An important part of this transportation system-level assessment has been to assess the seismic vulnerability of the state highway system. In doing so, the RRAP project team developed a Bridge Seismic Screening Tool (BSST) to assess, at a system-level, the potential impacts that a CSZ earthquake could have on state highway bridges. The first step in the BSST is to assess the seismic vulnerability of highway bridge infrastructure in Washington State following a CSZ earthquake to determine a projected or potential damage state. Damage states are then used to determine approximate reopening times for bridge crossings.2 This document provides details on the methodological development of the BSST, the implementation of that tool to analyze the projected damage incurred in a CSZ earthquake scenario, and the determination of corresponding reopening times of interstate and state highway bridges following such an event. The BSST was implemented programmatically using a Microsoft Excel macro-enabled workbook; instructions on use of this tool are provided in the accompanying report, Washington State Highway Bridge Seismic Screening Tool (BSST) - User Manual.

42 ENGINEERING↗
DOE OSTI2021

Seismic Array Analysis Using Fiber-Optic Distributed Acoustic Sensing on Small Local and Regional Earthquakes

We examined the feasibility of using fiber-optic Distributed Acoustic Sensing (DAS) as an alternative to a traditional seismic array. Seismic arrays are used routinely globally to measure waveform propagation parameters and signal features including back-azimuth (BAZ) and apparent horizontal velocity through the process of beamforming or frequency-wavenumber (f-k) array analysis with the assumption that the signals travel across the array as a plane-wave. These measurements are useful for identifying signal detections as teleseismic, regional, or local distance seismic phases based on their velocities traveling across the array (e.g., Rost and Thomas, 2001). Signal enhancement is the main benefit of array processesing through the stacking of multiple channels as a phased array. This enhancement increases as the square root of the number of channels relative to a single seismic station (e.g., Rost and Thomas, 2001), which is a factor of 3 from a typical nine-channel array. DAS provides the potential of stacking waveforms from 100’s to 1000’s of channels. However, DAS is a novel technology designed for geophysical exploration and therefore has some limitations which we will explore. We started with the dataset from the PoroTomo project because of its unique experimental layout of colocated geophone array and DAS deployment so the two recording technologies can be compared side-by-side.

58 GEOSCIENCES↗