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At least 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Modeled Performance of the ORNL Matched Filter Detector for Seismic Signatures of Multi-Axle Tractor Trailers

The MINOS program seeks methods to passively monitor the transfer of special materials, which are reproducible and quantitatively defensible. To date, we have demonstrated that short-period seismometers deployed near vehicle tracks can detect and monitor signatures of multi-axle tractor trailers impacting small road obstacles as means to detect and identify some transfer activities. In this report, we model the seismic velocity signal output from a tractor-trailer impacting a small obstacle (a template waveform) to parameterize a matched filter that identifies signals with similar shapes buried in noise, and that indicate repeating events (target waveforms). We then use this model to perform thousands of synthetic experiments, in which we apply our detector to identify noisy waveforms output from a similarly modeled vehicle data set; we process all data in the same manner as we process real data collected from the Oak Ridge National Laboratory (ORNL) seismic network. Our experiments, while limited, reveal that the matched filter shows a high discrimination capability, and at very low SNR values (at visibility thresholds). This means that template waveforms can match target waveforms that are sourced by models that share the same vehicle speed (target vehicles), and have a low probability of mis-identification. In particular, a matched filter designed to detect a five-axle tractor-trailer will reliably identify that target vehicle down to an SNR of 11.25 dB, and within ±0.1 km/hr, while successfully screening target vehicles with three axles.

42 ENGINEERING↗
DOE OSTI2021

3D orthorhombic earth model effects on seismic source characterization

Most earth materials are anisotropic with regard to seismic wave-speeds, especially materials such as shales, or where oriented fractures are present. However, the base assumption for many numerical simulations is to treat earth materials as isotropic media. This is done for simplicity, the apparent weakness of anisotropy in the far field, and the lack of well-characterized anisotropic material properties for input into numerical simulations. One approach for addressing the higher complexity of actual geologic regions is to model the material as an orthorhombic medium. We have developed an explicit time-domain, finite-difference (FD) algorithm for simulating three-dimensional (3D) elastic wave propagation in a heterogeneous orthorhombic medium. The objective of this research is to investigate the errors and biases that result from modeling a non-isotropic medium as an isotropic medium. This is done by computing “observed data” by using synthetic, anisotropic simulations with the assumption of an orthorhombic, anisotropic earth model. Green’s functions for an assumed isotropic earth model are computed and then used an inversion designed to estimate moment tensors with the “observed” data. One specific area of interest is how shear waves, which are introduced in an anisotropic model even for an isotropic explosion, affect the characterization of seismic sources when isotropic earth assumptions are made. This work is done in support of the modeling component of the Source Physics Experiment (SPE), a series of underground chemical explosions at the Nevada National Security Site (NNSS).

58 GEOSCIENCES↗
DOE OSTI2014

Bell Creek Test Site: 3-D Seismic and Characterization Report

The Plains CO 2 Reduction (PCOR) Partnership, led by the Energy & Environmental Research Center (EERC), is working with Denbury Onshore (Denbury) to evaluate the effectiveness of large-scale injection of carbon dioxide (CO 2 ) into the Bell Creek oil field for CO 2 enhanced oil recovery (EOR) and to study long-term incidental CO 2 storage. A technical team that includes Denbury, the EERC, and others are conducting activities to determine the baseline reservoir characteristics for development of a geologic model for predictive simulations and to serve as a comparison to time-lapse data as they are acquired. One of the activities was the acquisition and interpretation of a baseline three-dimensional (3-D) surface seismic survey acquired over a major portion of the field which is the subject of this report. The interpretation will be used to advance the field characterization effort. The geophysical data will be integrated with the EERC’s geologic model to improve its accuracy. In the future, when paired with at least one subsequent 3-D surface seismic survey, the data difference will provide a direct indication of where the CO 2 has migrated within the reservoir and aid in monitoring, verification, and accounting goals.

02 PETROLEUM↗
DOE OSTI2022

GraphAlign: Graph-Enabled Machine Learning for Seismic Event Filtering

This report summarizes results from a 2 year effort to improve the current automated seismic event processing system by leveraging machine learning models that can operated over the inherent graph data structure of a seismic sensor network. Specifically, the GraphAlign project seeks to utilize prior information on which stations are more likely to detect signals originating from particular geographic regions to inform event filtering. To date, the GraphAlign team has developed a Graphical Neural Network (GNN) model to filter out false events generated by the Global Associator (GA) algorithm. The algorithm operates directly on waveform data that has been associated to an event by building a variable sized graph of station waveforms nodes with edge relations to an event location node. This builds off of previous work where random forest models were used to do the same task using hand crafted features. The GNN model performance was analyzed using an 8 week IMS/IDC dataset, and it was demonstrated that the GNN outperforms the random forest baseline. We provide additional error analysis of which events the GNN model performs well and poorly against concluded by future directions for improvements.

58 GEOSCIENCES↗
DOE OSTI2022

BayesMT: A Probabilistic Bayesian Framework for the Seismic Moment Tensor

Moment tensors (MTs) have long been used in earthquake and explosion source analysis, and there has been a renewed interest in how they can inform us about the seismic source, particularly in the geophysical monitoring community due to its application in event identification and yield analysis. However, parameter uncertainties in seismic MT inversion are rarely available. The inverse procedure often does not quantify MT model errors such as event location, data noise and Earth model that are essential for estimating solution robustness. To address this need, we propose to adopt the Bayesian probabilistic framework to incorporate uncertainties in MT inversions. In this study, we present the theoretical background of a probabilistic Bayesian framework for MT inversion accounting for model and measurements errors and illustrate the implementation of the method using a synthetic example.

58 GEOSCIENCES↗
DOE OSTI2023

Adjoint Waveform Tomography for Next Generation Seismic Analyses and Monitoring

The development of methods and capabilities to compute complete waveform simulations in three-dimensional (3D) Earth models along with adjoint methods for computing the fully 3D sensitivity kernels in the 2000's set the stage for new advances in seismic imaging. I believe that the full benefits of adjoint waveform tomography (AWT) are not yet fully realized and this will be an important direction for the future of seismic tomography.

58 GEOSCIENCES↗
DOE OSTI2024

5-2428: Coupled Investigation of Fracture Permeability Impact on Reservoir Stress and Seismic Slip Behavior [Slides]

Our goal is to develop, apply and validate a holistic thermal, hydrologic, mechanical, and chemical (THMC) workflow that also includes evaluation of induced seismic slip in EGS reservoirs. We will integrate experimental and modelling approaches to reduce parameter uncertainty and better integrate friction constitutive laws for faults to improve our ability to predict and mitigate seismic hazard at Utah FORGE and future EGS sites. We propose a novel approach that incorporates 3D physics-based Earthquake simulations in THMC models, using lab measurements of friction parameters, herein referred to as “THMC+E” models. These simulations will enable exploration of various circumstances that may hinder EGS success and develop mitigation strategies.

58 GEOSCIENCES↗
DOE OSTI2024

Predicting seismic amplitudes with machine learning

The accurate estimation of seismic wave amplitude is vital to precisely determine the yield, magnitude, and event discrimination possible for a given network – a critical element in nuclear explosion monitoring. This task is complicated by several factors, including but not limited to radiation pattern, scattering effects, and crustal variations, which can lead to the attenuation or amplification of amplitude along a given raypath. In this report, we explore the novel application of machine learning to the task of seismic amplitude estimation by training a simple Artificial Neural Network (ANN) on an S-wave amplitude dataset from Lai et al. (2019). Attributes from this dataset used as input to the ANN included event-station distances, station locations (latitude, longitude), event locations (latitude, longitude), event depths, event magnitudes, radiation patterns, signal-to noise ratio (SNR) measurements (average-amplitude, peak-to-trough, maximum peak), and signal periods. We find that the trained ANN predicts S-wave amplitudes with a modest tendency toward underestimating the actual values, as indicated by a linear regression between predicted and actual data (slope: 0.892, intercept: -0.651). These results suggest that an ANN can perform this task, with potential for significant improvements through improved datasets, architectures, and parameter tuning.

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

Probabilistic Seismic Hazard Analysis for Iraq Based on the Updated Earthquake Catalog (1900-2021) and Ground Motion Characteristics

Onur et al. (2017) compiled the first comprehensive earthquake catalog for Iraq, covering 1900 to 2009 within 26°–40°N latitude and 36°–51°E longitude. This catalog was utilized in a probabilistic seismic hazard assessment (PSHA) by Abdulnaby et al. (2020) to aid in updating Iraq’s building code seismic provisions. Recently, we have updated the earthquake catalog for Iraq by adding earthquakes recorded from 2010 to 2021 and directly calculating moment magnitude (Mw) for about 2,800 earthquakes using the coda envelope methodology and waveform data from the Mesopotamian Seismological Network (MPSN) in Iraq.

58 GEOSCIENCES↗
DOE OSTI2025

Operational Forecasting of Induced Seismicity (CRADA Final Report)

This was a collaborative effort between Lawrence Livermore National Security, LLC ("LLNS"), as manager and operator of Lawrence Livermore National Laboratory ("LLNL"), The Regents of the University of California, as manager and operator of Lawrence Berkeley National Laboratory (Collectively, Contractors) and Nanometrics, Inc. ("Participant"), to develop a toolkit called "Operational Forecasting of Induced Seismicity (ORION)" that includes a decision tree method for operational forecasting of induced seismicity rates related to fluid disposal operations.

58 GEOSCIENCES↗
DOE OSTI2025

FY 2025 End of Year Report: Seismic Monitoring of Underground Vibration Sources using Distributed Acoustic Sensing (DAS) and Seismometers

This end-of-year report summarizes progress on using seismic monitoring to detect, associate, and locate anomalous vibration signals that may indicate potential containment breaches. The work focused on four key tasks: 1. Developing a database of continuous waveforms and ground-truth event data from multiple sensing modalities. 2. Refining and implementing detection and association algorithms to generate a catalog of anomalous underground activities. 3. Testing and improving distributed acoustic sensing amplitude-based geolocation methods to build an event location catalog. 4. Testing and refining seismic array polarization-based geolocation methods to build an event location catalog. This report provides a brief recap of results from the FY25 midyear report (Tasks 1 and 2) and presents new findings from geolocation methods (Tasks 3 and 4).

58 GEOSCIENCES↗
DOE OSTI2025

The propagation of seismic waves, misinformation, and disinformation from the 2024-10-05 M 4.5 Iran earthquake

The 2024-10-05 Iran M 4.5 earthquake took place at a time of heightened tensions in the Middle East. We perform a discrimination and moment tensor analysis and identify a shallow-dipping, reverse fault source commensurate with the compressional setting of the Iranian interior. Nonetheless, the event's aftermath saw widespread dissemination of misinformation, and potentially active disinformation, concluding that it was in fact a test of an Iranian nuclear weapon. The 'evidence' for many of these claims was based on inaccurate interpretation of seismic data. In this paper, we analyze how geophysical 'fake news' propagated through social media (mainly Twitter/X) following this event, eventually gaining traction in mainstream, earned media. This event is an illustrative warning of how seismic data can be misinterpreted and/or manipulated in public discourse.

58 GEOSCIENCES↗
DOE OSTI2016

A NEW ANALYSIS OF THE TWO CLASSICAL ZZ CETI WHITE DWARFS GD 165 AND ROSS 548. II. SEISMIC MODELING

We present the second of a two-part seismic analysis of the bright, hot ZZ Ceti stars GD 165 and Ross 548. In this second part, we report the results of detailed searches in parameter space for identifying an optimal model for each star that can account well for the observed periods, while being consistent with the spectroscopic constraints derived in our first paper. We find optimal models for each target that reproduce the six observed periods well within ∼0.3% on the average. We also find that there is a sensitivity on the core composition for Ross 548, while there is practically none for GD 165. Our optimal model of Ross 548, with its thin envelope, indeed shows weight functions for some confined modes that extend relatively deep into the interior, thus explaining the sensitivity of the period spectrum on the core composition in that star. In contrast, our optimal seismic model of its spectroscopic sibling, GD 165 with its thick envelope, does not trap/confine modes very efficiently, and we find weight functions for all six observed modes that do not extend into the deep core, hence accounting for the lack of sensitivity in that case. Furthermore, we exploit after the fact the observed multiplet structure that we ascribe to rotation. We are able to map the rotation profile in GD 165 (Ross 548) over the outermost ∼20% (∼5%) of its radius, and we find that the profile is consistent with solid-body rotation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Absolute Magnitudes of Seismic Red Clumps in the Kepler Field and SAGA: The Age Dependency of the Distance Scale

Red clump stars are fundamental distance indicators in astrophysics, although theoretical stellar models predict a dependence of absolute magnitudes with age. This effect is particularly strong below ∼2 Gyr, but even above this limit a mild age dependence is still expected. We use seismically identified red clump stars in the Kepler field for which we have reliable distances, masses, and ages from the SAGA survey, to first explore this effect. By excluding red clump stars with masses larger than 1.6 M{sub ⊙} (corresponding to ages younger than 2 Gyr), we derive robust calibrations linking intrinsic colors to absolute magnitudes in the following photometric systems: Strömgren by, Johnson BV, Sloan griz, 2MASS JHK {sub s}, and WISE W1W2W3. With the precision achieved we also detect a slope of absolute magnitudes ∼0.020±0.003 mag Gyr{sup −1} in the infrared, implying that distance calibrations of clump stars can be off by up to ∼0.2 mag in the infrared (over the range from 2 to 12 Gyr) if their ages are unknown. Even larger uncertainties affect optical bands, because of the stronger interdependency of absolute magnitudes on colors and age. Our distance calibrations are ultimately based on asteroseismology, and we show how the distance scale can be used to test the accuracy of seismic scaling relations. Within the uncertainties our calibrations are in agreement with those built upon local red clumps with Hipparcos parallaxes, although we find a tension, which, if confirmed, would imply that scaling relations overestimate the radii of red clump stars by 2 ± 2%. Data releases post Gaia DR1 will provide an important testbed for our results.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Data for Monitoring Fracture Saturation with Internal Transportable Seismic Sources and Twin Neural Networks

Seismic coda-wave analysis is a well-developed method for detecting subtle physical changes in complex media by measuring arrival times in the late-arriving energy from multiply-scattered or reflected waves. However, a challenge arises when multiply-scattered waves are not sufficiently separated in time from the direct arrivals to provide a clear coda wave train. Additional complications for monitoring changes in fracture systems arise when the signals originate from unsynchronized internal sources, such as natural induced seismicity, from acoustic emission, or from transportable intra-fracture sources (chattering dust), that generate uncontrolled signals that vary in arrival time, amplitude and frequency content. Here, we use a twin neural network (also known as a Siamese neural network) for dimensionality reduction to analyze signals from chattering dust to classify the fluid saturation state of a synthetic fracture system. The twin neural network with shared weights generates a low-dimensional representation of the data input by minimizing contrastive loss, serving as the input to a multiclass classifier that accurately classifies whether multiple fractures in a fracture system are fully saturated or partially saturated, or whether a change in saturation has occurred in different fractures in the system. These results show that information buried in unresolved codas from uncontrolled sources can be extracted using machine learning to monitor the evolution of fracture systems caused by physical and chemical processes even when the scattered and direct wave fields overlap.

58 GEOSCIENCES↗