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Results for “controlled source seismology”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A method to fuse multiphysics waveforms and improve predictive explosion detection: theory, experiment and performance

Natural and human-made sources of transient energy often emit multiple geophysical signatures that include mechanical and electromagnetic waveforms. We present a constructive method to fuse and evaluate statistics that we derive from such multiphysics waveforms that improves our capability to detect small, near-ground explosions over similar methods that consume single signature waveforms. Our method advances Fisher's Combined Probability Test (Fisher's Method) to operate under both hypotheses of a binary test on noisy data and provide researchers with the density functions required to forecast the ability of Fisher's Method to screen fused explosion signatures from noise. We apply this method against 12 d, multisignature explosion and noise records to show (1) that a fused multiphysics waveform statistic that combines radio, acoustic and seismic waveform data can identify explosions roughly 0.8 magnitude units lower than an acoustic emission, STA/LTA detector for the same detection probability and (2) that we can quantitatively predict how this fused, multiphysics statistic performs with Fisher's Method. Our work thereby offers a baseline method for predictive waveform fusion that supports multiphenomenological explosion monitoring (multiPEM) and is applicable to any binary testing problem in observational geophysics.

58 GEOSCIENCES↗
DOE OSTI2023

Examining Infrasound Propagation at High Spatial Resolution Using a Nodal Seismic Array

Infrasound—acoustic waves in the atmosphere below 20 Hz—is a useful monitoring tool. Topography and atmospheric structure strongly control infrasound propagation, and at common source–receiver distances neither of these effects can be ignored when quantitative source constraints are sought. Detailed spatial measurements of the infrasound wavefield would inform propagation models and improve source estimates. However, the “large-N” deployment strategy now well-known in seismology has not yet been realized for infrasound studies. Here, we use the 900-node seismic array from the 2014 Imaging Magma Under St. Helens (iMUSH) experiment as a proxy for a large-N infrasound network, by leveraging acoustic–seismic coupled arrivals. The active-source component of iMUSH consisted of 23 shallowly buried explosions around Mount Saint Helens volcano; these explosions produced epicentral infrasound recorded on the nodes. We find that the bulk presence of ground-coupled infrasound on the nodes is controlled by wind noise and source–receiver distance, with observed arrivals for eight explosions. Explosions with the most extensive coupling produce complex spatial waveform patterns across the array. These patterns are related to both topographic and atmospheric propagation effects, as well as spatially variable site (coupling) effects. We compare our observations to simple topographic diffraction and high-resolution wind advection models, and full-wave numerical simulations. We find strong spatial correlations between (a) coupled arrival strength and modeled topographic obstruction and (b) coupled arrival time and along-path winds. Our seismoacoustic analyses and results are applicable to other existing and future nodal seismic data sets and can expand the utility of such deployments.

58 GEOSCIENCES↗
DOE OSTI2023

3-D Simulations of earthquakes rupture jumps: 1. Homogeneous pre-stress conditions

SUMMARY Observational and modelling studies indicate that earthquake ruptures can jump between fault sections as large as ∼3 and ∼5 km for compressional and extensional offsets, respectively. Here, we compare characteristics of the rupture jump process on parallel but offset fault sections from traditional 3-D dynamic rupture simulations governed by slip weakening friction using the finite element code, FaultMod, to those from quasi-dynamic simulations governed by rate- and state-dependent friction (rate-state friction) using the code RSQSim. These simulations use spatially uniform initial stresses. For a variety of measures the rupture renucleation position on the offset fault, the rate-state friction and slip weakening friction models produce very similar results. The principal difference is the additional occurrence of delayed rupture jumps that arise from the time- and stress-dependent nucleation that is characteristic of rate-state friction. For immediate rupture jumps, models with slip weakening friction span greater offsets than those with rate-state friction. However the jump distances are nearly identical when delayed rupture jumps are included in the comparisons. We propose that delayed rupture jumps are the likely mechanism for adjacent large-earthquake pairs and clusters. Based on the similarity of renucleation positions with both dynamic and quasi-dynamic models, we conclude that the renucleation positions for rupture initiation on the receiver fault (separated by less than ∼3 km from the source fault) are primarily controlled by static stress changes induced by slip on the initiating fault. However, in light of the slightly greater maximum jump distances (>3 km) seen with the dynamic slip weakening friction model, dynamic stress changes from seismic waves play an increasingly important role as offset distances increase.

, RSQSim↗
DOE OSTI2023

A Catalog of Temporally Localized Systematic Deviations in Global Body Wave Travel-Time Measurements

Accurate measurements of the arrival times of seismic waves are crucial for seismological analyses such as robust locations of earthquakes, characterization of seismic sources, and high-fidelity imaging of the Earth’s interior. However, these travel-time measurements can sometimes be contaminated by timing errors at the stations which record this data. In this study, we apply a classical approach, based on identifying time-dependence in measured body wave arrival times, to identify these timing errors in a dataset on the order of 107 individual measurements. We find timing deviations at a subset of the stations in our dataset and document the temporal location, extent, and severity of these errors, finding errors at 83 stations, and impacting ~100,000 measurements. This catalog of deviations may enable future investigators to obtain a more accurate dataset through the implementation of quality control measures to eliminate the contaminated data we have identified.

58 GEOSCIENCES↗
DOE OSTI2026

Limited Dynamic Earthquake Triggering in Nevada

Dynamic triggering occurs when seismic waves from distant large earthquakes temporarily alter stress conditions along faults, potentially triggering new earthquakes hundreds to thousands of kilometers away from the source. Previous studies have linked triggered seismicity to anthropogenic activities such as geothermal, oil, and gas production. Although these activities are present in Nevada, little work has been conducted to explore dynamically triggered seismicity in Nevada. Here, we analyze a newly published, high-resolution earthquake catalog for Nevada to identify local seismicity dynamically triggered by teleseismic events (Mw≥7) from 2008 to 2023. We identify 94 dynamically triggered earthquakes concentrated in four distinct regions, which qualitatively show a modest positive correlation with geothermal well locations. Triggered seismicity in Nevada is predominantly delayed, with some instantaneously triggered by Rayleigh waves. The prevalence of delayed triggering indicates that pore fluid interactions may play a critical role in controlling dynamic triggering susceptibility in Nevada. Our results demonstrate that dynamic triggering can provide valuable insight to help identify critically stressed regions.

58 GEOSCIENCES↗
DOE OSTI2023

The influence of physical and algorithmic factors on simulated far-field waveforms and source–time functions of underground explosions using unsupervised machine learning

SUMMARY Characterizing explosion sources and differentiating between earthquake and underground explosions using distributed seismic networks becomes non-trivial when explosions are detonated in cavities or heterogeneous ground material. Moreover, there is little understanding of how changes in subsurface physical properties affect the far-field waveforms we record and use to infer information about the source. Simulations of underground explosions and the resultant ground motions can be a powerful tool to systematically explore how different subsurface properties affect far-field waveform features, but there are added variables that arise from how we choose to model the explosions that can confound interpretation. To assess how both subsurface properties and algorithmic choices affect the seismic wavefield and the estimated source functions, we ran a series of 2-D axisymmetric non-linear numerical explosion experiments and wave propagation simulations that explore a wide array of parameters. We then inverted the synthetic far-field waveform data using a linear inversion scheme to estimate source–time functions (STFs) for each simulation case. We applied principal component analysis (PCA), an unsupervised machine learning method, to both the far-field waveforms and STFs to identify the most important factors that control variance in the waveform data and differences between cases. For the far-field waveforms, the largest variance occurs in the shallower radial receiver channels in the 0–50 Hz frequency band. For the STFs, both peak amplitude and rise times across different frequencies contribute to the variance. We find that the ground equation of state (i.e. lithology and rheology) and the explosion emplacement conditions (i.e. tamped versus cavity) have the greatest effect on the variance of the far-field waveforms and STFs, with the ground yield strength and fracture pressure being secondary factors. Differences in the PCA results between the far-field waveforms and STFs could possibly be due to near-field non-linearities of the source that are not accounted for in the estimation of STFs and could be associated with yield strength, fracture pressure, cavity radius and cavity shape parameters. Other algorithmic parameters are found to be less important and cause less variance in both the far-field waveforms and STFs, meaning algorithmic choices in how we model explosions are less important, which is encouraging for the further use of explosion simulations to study how physical Earth properties affect seismic waveform features and estimated STFs.

58 GEOSCIENCES↗
DOE OSTI2025

The Dynamic Networks Experiments: Virtual Experiments to Quantify Gains in Nuclear Explosion Monitoring

We describe an ongoing series of virtual experiments conducted collaboratively by four United States National Laboratories: Sandia National Laboratories, Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Pacific Northwest National Laboratory. These Dynamic Network Experiments (DNEs) provide an experimental framework to evaluate the potential impact of new research tools on nuclear explosion monitoring. The second DNE (DNE2), completed in 2024, exploited waveform data (seismic, infrasound, and electromagnetic) that was recorded by multi-modal sensors within and near the Nevada National Security Site and synthetic radionuclide signatures over multiple time periods. During the execution of DNE2, we processed and analyzed data through a multi-stage event processing pipeline that ingested raw data, performed quality control, detected signals, built events from these signals, located these events, and characterized the events’ source types and sizes. For each stage and over the entire event processing pipeline, we evaluated performance changes by comparing the performance of new data processing methods, models, and algorithms against a baseline. We also performed an additional execution phase to assess event processing pipeline function, speed, and efficiency against that of an expert analyst, including computational and manual efforts. Finally, we assessed the impact and effort of modern computing infrastructure on the monitoring pipeline. This paper describes key elements of the DNEs, from formulation through execution, as demonstrated in DNE2. The DNEs introduce several novel concepts to quantitatively measure the potential impact of new methods on explosion monitoring, including the collaborative design of multi-modal datasets, performance and logistical metrics, and integrated analyses.

42 ENGINEERING↗