Publications

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Supplementing an existing high-quality seismic monitoring network with openly available station data could improve coverage and decrease magnitudes of completeness; however, this can present challenges when varying levels of data quality exist. Without discerning the quality of openly available data, using it poses significant data management, analysis, and interpretation issues. Incorporating additional stations without properly identifying and mitigating data quality problems can degrade overall monitoring capability. If openly available stations are to be used routinely, a robust, automated data quality assessment for a wide range of quality control (QC) issues is essential. To meet this need, we developed Pycheron, a Python-based library for QC of seismic waveform data. Pycheron was initially based on the Incorporated Research Institutions for Seismology's Modular Utility for STAtistical kNowledge Gathering but has been expanded to include more functionality. Pycheron can be implemented at the beginning of a data processing pipeline or can process stand-alone data sets. Its objectives are to (1) identify specific QC issues; (2) automatically assess data quality and instrumentation health; (3) serve as a basic service that all data processing builds on by alerting downstream processing algorithms to any quality degradation; and (4) improve our ability to process orders of magnitudes more data through performance optimizations. This article provides an overview of Pycheron, its features, basic workflow, and an example application using a synthetic QC data set.

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The Ecosystem for Open Science (eOS) initiative was established in 2019. Its objective is improving openness and sharing of data and information across Defense Nuclear Nonproliferation (DNN) Research and Development (R&D) activities. To support this initiative, the eOS team at Sandia National Laboratories (SNL) developed metadata and data standards and proposed a machine-readable metadata schema. The nuclear explosion monitoring field was selected as a focus area due to its the wide range of pertinent phenomenologies.We developed the DCAT-eOS-AP metadata schema extending the Data Catalog Vocabulary version 2 (DCATv2) standard using an application profile (AP), to fit the needs of multi-disciplinary NA-22 projects. The DCAT-eOS-AP metadata schema describes data at different levels of granularity ranging from general descriptions to more domain-specific granular metadata. Its implementation and serialization is flexible with the ability to include new file or data types. Thus, it will scale with the ever-increasing data management needs of government research. Due to the multitude of phenomenologies represented in the DCAT-eOS-AP schema, we anticipate that it will be easily extensible to various projects across many DOE mission areas. This document describes data management challenges faced within the DNN R&D portfolio and provides insight on how metadata and data standards/guidelines combined with a comprehensive metadata schema can add value to programs throughout the Department of Energy (DOE). It reviews the importance of metadata standards, FAIR (Findability, Accessibility, Interoperability, and Reusability) data principles, and metadata schemas. Additionally, it summarizes input from subject matter experts (SME) at SNL and other National Laboratories that resulted in metadata and data standards/guidelines encompassing domains relevant to NA-22 projects. Finally, we discuss the DCAT-eOS-AP metadata development. Implementation recommendations and future development directions are included for those keen on adopting the DCAT-eOS-AP metadata schema.

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During the development of new seismic data processing methods, the verification of potential events and associated signals can present a nontrivial obstacle to the assessment of algorithm performance, especially as detection thresholds are lowered, resulting in the inclusion of significantly more anthropogenic signals. Here, we present two 14 day seismic event catalogs, a local-scale catalog developed using data from the University of Utah Seismograph Stations network, and a global-scale catalog developed using data from the International Monitoring System. Each catalog was built manually to comprehensively identify events from all sources that were locatable using phase arrival timing and directional information from seismic network stations, resulting in significant increases compared to existing catalogs. The new catalogs additionally contain challenging event sequences (prolific aftershocks and small events at the detection and location threshold) and novel event types and sources (e.g., infrasound only events and long-wall mining events) that make them useful for algorithm testing and development, as well as valuable for the unique tectonic and anthropogenic event sequences they contain.

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We invert far-field infrasound data for the equivalent seismoacoustic timedomain moment tensor to assess the effects of variable atmospheric models and source phenomena. The infrasound data were produced by a series of underground chemical explosions that were conducted during the Source Physics Experiment (SPE), which was originally designed to study seismoacoustic signal phenomena. The first goal of this work is to investigate the sensitivity of the inversion to the variability of the estimated atmospheric model. The second goal is to determine the relative contribution of two presumed source mechanisms to the observed infrasonic wavefield. Rather than using actual atmospheric observations to estimate the necessary atmospheric Green’s functions, we build a series of atmospheric models that rely on publicly available, regional-scale atmospheric observations. The atmospheric observations are summarized and interpolated onto a 3D grid to produce a model of sound speed at the time of the experiment. For each of four SPE acoustic datasets that we invert, we produced a suite of three atmospheric models for each chemical explosion event, based on 10 yrs of meteorological data: an average model, which averages the atmospheric conditions for 10 yrs prior to each SPE event, as well as two extrema models. To parameterize the inversion, we assume that the source of infrasonic energy results from the linear combination of explosion-induced surface spall and linear seismic-to-elastic mode conversion at the Earth’s free surface. We find that the inversion yields relatively repeatable results for the estimated spall source. Conversely, the estimated isotropic explosion source is highly variable. This suggests that 1) the majority of the observed acoustic energy is produced by the spall and/or 2) our modeling of the elastic energy, and the subsequent conversion to acoustic energy, is too simplistic.

We invert far-field infrasound data for the equivalent seismoacoustic time-domain moment tensor to assess the effects of variable atmospheric models and source phenomena. The infrasound data were produced by a series of underground chemical explosions that were conducted during the Source Physics Experiment (SPE), which was originally designed to study seismoacoustic signal phenomena. The first goal is to investigate the sensitivity of the inversion to the variability of the estimated atmospheric model. The second goal is to determine the relative contribution of two presumed source mechanisms to the observed infrasonic wavefield. Rather than using actual atmospheric observations to estimate the necessary atmospheric Green’s functions, we build a series of atmospheric models that rely on publicly available, regional-scale atmospheric observations. The atmospheric observations are summarized and interpolated onto a 3D grid to produce a model of sound speed at the time of the experiment. For each of four SPE acoustic datasets that we invert, we produced a suite of three atmospheric models for each chemical explosion event, based on 10 yrs of meteorological data: an average model, which averages the atmospheric conditions for 10 yrs prior to each SPE event, as well as two extrema models. To parameterize the inversion, we assume that the source of infrasonic energy results from the linear combination of explosion-induced surface spall and linear seismic-to-elastic mode conversion at the Earth’s free surface. We find that the inversion yields relatively repeatable results for the estimated spall source. Conversely, the estimated isotropic explosion source is highly variable. This suggests that 1) the majority of the observed acoustic energy is produced by the spall and/or 2) our modeling of the elastic energy, and the subsequent conversion to acoustic energy, is too simplistic.

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This report shows the results of constructing predictive atmospheric models for the Source Physics Experiments 1-6. Historic atmospheric data are combined with topography to construct an atmospheric model that corresponds to the predicted (or actual) time of a given SPE event. The models are ultimately used to construct atmospheric Green's functions to be used for subsequent analysis. We present three atmospheric models for each SPE event: an average model based on ten one-hour snap shots of the atmosphere and two extrema models corresponding to the warmest, coolest, windiest, etc. atmospheric snap shots. The atmospheric snap shots consist of wind, temperature, and pressure profiles of the atmosphere for a one-hour time window centered at the time of the predicted SPE event, as well as nine additional snap shots for each of the nine preceding years, centered at the time and day of the SPE event.

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This document serves to guide a researcher through the process of predicting atmospheric conditions in a region of interest utilizing the Weather Research and Forecasting (WRF) model. This documentation is specific to WRF and WRF Preprocessing System (WPS) version 3.8.1. WRF is an atmospheric prediction system designed for meteorological research and numerical atmospheric prediction. In WRF, simulations may be generated utilizing real data or idealized atmospheric conditions. Output from WRF serves as input into the Time-Domain Atmospheric Acoustic Propagation Suite (TDAAPS) which performs staggered-grid finite difference modeling of the acoustic velocity pressure system to produce Green's functions through these atmospheric models.

This document serves to guide a researcher through the process of running the Weather Research and Forecasting (WRF) model and incorporating observations into coarse resolution reanalysis products to model atmospheric conditions at high (50 m) resolution. This documentation is specific to WRF and the WRF Preprocessing System (WPS) version 3.8.1 and the Objective Analysis (OBSGRID) code released on April 8, 2016. Output from WRF serves as an input into the Time-Domain Atmospheric Acoustic Propagation Suite (TDAAPS) which performs staggered-grid finite difference modeling of the acoustic velocity pressure system to produce Green's functions through these atmospheric models.

We invert far field infrasound data for the equivalent seismo-acoustic time domain moment tensor to assess the effects of variable atmospheric models as well as to quantify the relative contributions of two presumed source phenomena. The infrasound data was produced by a series of underground chemical explosions that were conducted during the Source Physics Experiment, (SPE) which was originally designed to study explosion-generated seismo-acoustic signal phenomena. The goal of the work presented herein is two-fold: the first goal is to investigate the sensitivity of the estimated time domain moment tensors to variability of the estimated atmospheric model. The second goal is to determine the relative contribution of two possible source mechanisms to the observed infrasonic wave field. Rather than using actual atmospheric observations to estimate the necessary atmospheric Green's functions, we build a series of atmospheric models that rely on publicly available, regional atmospheric observations and the assumption that the acoustic energy results from a linear combination of an underground isotropic explosion and surface spall. The atmospheric observations are summarized and interpolated onto a 3D grid to produce a model of sound speed at the time of the experiment. For each of four SPE acoustic datasets that we invert, we produced a suite of three atmospheric models, based on ten years of regional meteorological observations: an average model, which averages the atmospheric conditions for ten years prior to each SPE event, as well as two extrema models. We find that the inversion yields relatively repeatable results for the estimated spall source. Conversely, the estimated isotropic explosion source is highly variable. This suggests that the majority of the observed acoustic energy is produced by the spall source and/or our modeling of the elastic energy propagation, and it's subsequent conversion to acoustic energy via linear elastic-to-acoustic coupling at the free surface, is too simplistic.

This report shows the results of constructing predictive atmospheric models for the Source Physics Experiments 1-6. Historic atmospheric data are combined with topography to construct an atmo- spheric model that corresponds to the predicted (or actual) time of a given SPE event. The models are ultimately used to construct atmospheric Green's functions to be used for subsequent analysis. We present three atmospheric models for each SPE event: an average model based on ten one- hour snap shots of the atmosphere and two extrema models corresponding to the warmest, coolest, windiest, etc. atmospheric snap shots. The atmospheric snap shots consist of wind, temperature, and pressure profiles of the atmosphere for a one-hour time window centered at the time of the predicted SPE event, as well as nine additional snap shots for each of the nine preceding years, centered at the time and day of the SPE event.

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