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DOE OSTI2020

Sandia National Laboratories Ecosystem for Open Science: Metadata Schema v0.2 Description.

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.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2022

Bayesian Seismoacoustic Source Location: Acoustic Approach

Seismic waves and infrasound are key technologies in the International Monitoring System (IMS) to monitor explosive events in the solid Earth and atmosphere. Energetic man-made or natural events (e.g., chemical/nuclear explosions, volcanic eruptions, and earthquakes) near the Earth’s surface produce both ground motion and atmospheric pressure disturbances which propagate as seismic waves and infrasound, respectively. Seismic waves have been generally used to detect and identify underground and near-surface events (Myers, et. al., 2007), and infrasound are sensitive to events near the surface or in the atmosphere (Modrak et. al., 2010). Due to their different sensitivities to events, they can complement to each other to improve the event detection and discrimination. The framework of joint seismoacoustic event location has recently reviewed by theoretical research (Koch and Arrowsmith, 2019). Although the early applications showed promising results to improve the accuracy of event location, their application were still limited to a small set of events selected to prove the concepts, and practical capability of the method for operational purpose is not fully evaluated with data. Our final goal is to apply the method of seismoacoustic event location to a larger set of events and evaluate its applicability for operational event location in practice. To that end, we focus on developing and verifying acoustic source location method in this study.

58 GEOSCIENCES↗

DOE OSTI2023

Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data

We present a new model of radially anisotropic seismic wavespeeds for the crust and upper mantle of a broad region of the Middle East and Southwest Asia (MESWA) derived from adjoint waveform tomography. We inverted waveforms from 192 Global Centroid Moment Tensor earthquakes (MW 5.5-7.0) recorded by over 1000 openly available broadband seismic stations from permanent and temporary networks in the region. Spatial coverage of the available data is highly uneven due to earthquakes clustered along plate boundaries and sparse coverage of open seismic networks in the region. We considered three possible starting models: the SPiRaL global model (Simmons et al., 2021); MEC-1 (Kaviani et al., 2020); and CSEM2.0 (Noe et al., 2023). Because the SPiRaL model provides good fits to the observed waveforms measured by the time-bandwidth product of selected windows in several period bands, provides all the necessary parameters and covers the entire domain we used it for the starting model with the period band 50-100 seconds. Inversion iterations proceeded using time-frequency phase misfits in six stages and 54 total iterations reducing the minimum period to 30 seconds. Our final model, MESWA, provides improved waveform fits compared to the starting model for both the data used in the inversion and an independent validation data set of 66 events. Two metrics of waveform fit (the time-frequency phase misfit used in the optimization and normalized L2 misfit) were both reduced by nearly 60% for both data sets and MESWA provides significantly larger misfit reductions relative to the SPiRaL model than the MEC-1 or CSEM models. We also find that MESWA provides a larger time-bandwidth product of selected windows indicating that more information content of the observed waveforms is explained by MESWA than the other models. Our new model reveals tectonic features imaged by other studies and methods but in a new holistic model of shear and compressional wavespeeds (v S and v P , respectively) with anisotropy covering the crust and uppermost mantle of a larger domain. MESWA has smaller scale-length features and tends to sharpen some features relative to the SPiRaL starting model. Examples include: low crustal v S in the TurkishIranian Plateau, Zagros Mountains, Afghan Central Blocks and Sulaiman Fold Belt; low mantle vSfollowing divergent (Gulf of Aden, Red Sea) and transform (Dead Sea Fault) margins of the Arabian Plate; low and high v S in the mantle beneath the Arabian Shield and Platform, respectively. Low vS is imaged below Cenozoic volcanic centers of the Arabian Peninsula, the so-called Mecca-Madina-Nafud (MMN) Line. Positive anisotropy (v SH > v SV ) is inferred for asthenospheric depths across the region except where up/downwelling may influence fabric alignment (e.g. Afar, Red Sea, Arabian Shield). Elevated vS tracks Makran subduction under southeast Iran. MESWA resembles the SPiRaL model in its long-wavelength structure, but enhances shorter wavelengths features on the order of 200 km and smaller. The resulting model could be used for as a starting model for further improvements, say using waveforms from in-country seismic networks that are not openly available or smaller-scale studies targeting shorter period waveforms. The model also could be used for source characterization and moment tensor inversion to improve earthquake hazard studies and nuclear explosion monitoring.

58 GEOSCIENCES↗

DOE OSTI2024

MultiPEM Toolbox: User Manual [Rev. 2]

This document explains use of the Multi-Phenomenology Explosion Monitoring (Multi PEM) Toolbox, a collection of R scripts for estimating the unknown device parameters of a new event with uncertainty quantification. The methodology and application used for illustration in this user manual are fully documented in a Los Alamos National Laboratory technical report hereafter designated “WPA” for reference. Additional details on the application are found in a recent journal article. Two assessment types are available: rapid and complete. Rapid assessments are conducted in two stages, as described in Section 2. In the first stage, calibration data are used to estimate forward and error model parameters (WPA, §5.1) and (if relevant) errors-in-variables yield values for calibration sources (WPA, §3, Equation (3)). In the second stage, new event data are used to estimate the unknown new event device parameters (WPA, §5.2) with uncertainty quantification. Two options for treating the inferred first stage parameters in second stage Bayesian analysis are available: fixing them at their maximum likelihood estimate (default), or multiple imputation. Multiple imputation involves utilizing several posterior samples (imputations) of the first stage parameters as fixed values in the second stage posterior sampling of the new event device parameters. Second stage sampling is conducted across imputations in parallel to improve computational efficiency. This method produces improved uncertainty quantification of the new event device parameters compared with the default treatment of the first stage parameters, at the expense of additional computation. Complete assessments are conducted in a single stage, as described in Section 3. Calibration and (if relevant) new event data are used simultaneously to estimate all forward model, error model, and (if relevant) new event device parameters with uncertainty quantification on the latter. As the name suggests, rapid assessments generally run substantially faster than complete assessments (even with multiple imputation), because the results of first stage analysis can be stored and incorporated into estimating a relatively low-dimensional space of new event device parameters whenever relevant new event data becomes available. On the other hand, complete assessments must be run on the full set of model and device parameters with calibration and new event data every time the latter becomes available.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Advanced Error Modeling for Prompt Yield Estimation [Slides]

Multi-Phenomenology Explosion Monitoring (MultiPEM) Toolbox provides a capability for post-detonation characterization focusing on yield estimation; Synthesize information from multiple sensor types; Leverage historical databases to inform statistical models; Allows for rapid or comprehensive analysis; Implemented in R code.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2023

An Analysis of Published DAS Studies for Application to SPE Phase III

Distributed Acoustic Sensing (DAS) is an emerging technology capable of recording the acoustic wavefield at unprecedented spatial resolution. However, this new tool requires significant refinements before it becomes operational for explosion monitoring objectives. Recent studies have shown significant development of array processing with DAS data. In this report we explore three such array processing methods including DAS strain-rate data versus geophone measured ground motion, beamforming for event parameters, and machine learning based denoising. Prior to applying these algorithms to the Source Physics Experiment Phase II and Phase III data, we validate these methods through replication analysis.

47 OTHER INSTRUMENTATION↗

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 OSTI2023

A Metadata Exchange Model for Distributed Acoustic Sensing (DAS) by the DAS-RCN Data Management Team

Co-Author Voon Hui Lai, Australian National University, will be attending IUGG and presenting the work. The session of interested is "S05 - Advances in Earthquake and Explosion Monitoring Using Distributed Acoustic Sensing"

Hodgkinson, Kathleen Marian↗

DOE OSTI2026

MultiPEM Toolbox: User Manual

58 GEOSCIENCES↗

DOE OSTI2021

Geologic Framework Modeling (GFM) Integration

To be presented at the 2021 NEM. The March 15-19, 2021 virtual Nuclear Explosion Monitoring (NEM) Program Review combines reviews of several related programs within the Office of Defense Nuclear Nonproliferation Research and Development (DNN R&D) of the National Nuclear Security Administration.

58 GEOSCIENCES↗

DOE OSTI2025

Application of Indirect Quantification of 133mXe to Calibration of HPGe Detector

The INL Noble Gas Laboratory provides intercomparison samples for the noble gas analysis laboratories as part of the CTBTO PrepCom IMS. Xe-133m is one of the four relevant radionuclides in nuclear explosion monitoring. Without commercially available Xe-133m calibration standards laboratories must create and improve calibration methods. Improvements in calibration methods at the INL NGL benefit the CTBTO PrepCom through better certified values for Xe-133m intercomparison samples. Calibration of High Purity Germanium detectors for Xe-133m quantification is complicated by the coexistence of Xe-133 in samples under analysis. Xe-133 is typically produced in larger quantities, has higher gamma emission probabilities, and its gammas are detected more efficiently than Xe-133m. Xe-133m activity of samples can be indirectly inferred through the 133:133m activity ratio of a batch of material, and the Xe-133 counts in the assay of a small aliquot of the same material. This indirect quantification method can be leveraged to perform detector calibrations for quantification of Xe-133m. Calibrations can be performed by inferring the Xe-133m to certify the sample, and direct counting to determine detector efficiency. A comparison of method results will be shown.

133mXe↗

DOE OSTI2023

Radioxenon Detection for Monitoring Subsurface Nuclear Explosion

The Comprehensive Nuclear-Test-Ban Treaty (CTBT) bans the testing of nuclear weapons anywhere on the earth (atmospheric, surface, underwater and subsurface). Identification of nuclear explosions in the atmosphere, surface, and underwater is relatively straightforward considering a wide range of signatures resulting from such an event. However, for a subsurface explosion, most of the signatures traditionally associated with a nuclear explosion are not readily available. Therefore, the international community has increasingly relied on the atmospheric measurement of noble gases to identify subsurface nuclear weapon explosions. This chapter initially covers the basic principles of subsurface nuclear explosion identification and the importance of detecting radioxenon. This is followed by reviewing some of the early radioxenon detection systems that were developed by research groups around the world in the late 1990s and early 2000s. The detection media employed, results from laboratory and field testing, and some challenges/drawbacks for these systems are detailed. The next section of the chapter is dedicated to innovative detector concepts that have emerged in the past ten to fifteen years using novel detection material, algorithms, and signal readout techniques. The advances achieved in terms of energy resolution, coincidence detection efficiencies, system performance, and the minimum detectable concentration are covered. The final section goes over some of the potential improvements that can be incorporated in the design to enhance detector sensitivity and new detection material that can be explored in the field of radioxenon detection.

Gadey, Harish Reddy↗

DOE OSTI2022

Using STAX data to predict IMS radioxenon concentrations

The noble gas collection and measurement stations in the International Monitoring System (IMS) are heavily influenced by releases from medical isotope production facilities. The ability to reliably model the movement of radioxenon from the points of release to these IMS samplers has improved enough that a routine aspect of the analysis of IMS radioxenon data should be the prediction of the effect of releases from civilian nuclear facilities on the sample concentrations. In this work, predicted concentrations at IMS noble gas systems in Germany and Sweden based on measured releases from Institute for Radioelements (IRE) in Belgium and atmospheric transport modeling for a four-month period are presented and discussed.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2025

PySolate : A Python‐Based Thresholding Tool to Denoise or Designal Seismic Waveforms Based on the Continuous Wavelet Transform

PySolate is a Python‐based toolset that implements the continuous wavelet transform and nonlinear thresholding operations to denoise or designal seismic data, following Langston and Mousavi (2019). This filtering approach can remove microseismic noise to isolate intermediate‐period seismic signals that are key to enabling full‐waveform modeling and analysis of smaller‐magnitude regional events. This approach is best for the application to signals with frequency or time separation of signal and noise, in contrast to Fourier analysis, which is effective when signal and noise are separated in frequency. We demonstrate the Python toolset using the six announced Democratic People’s Republic of Korea declared nuclear tests, showing the effectiveness of isolating the seismic signal compared to standard bandpass filtering. In conclusion, we also demonstrate the ease of using the toolset with any Python processing tools.

Asia↗

DOE OSTI2025

A statistical approach to screening isotopic signatures in monitoring for underground nuclear explosions

The ability to differentiate between atmospheric radionuclide signatures from underground nuclear explosions (UNEs) and signals from other sources, such as medical isotope-production facilities and nuclear reactors, can be critical to the detection and monitoring of unannounced, low-yield nuclear events. Signatures having anomalously high amplitudes, compared to background levels, remain the best indicator in screening for a UNE. However, isotopic composition can further validate a suspected UNE signature, but separation from any atmospheric background composition is first necessary. To date, evaluating the challenges of performing this separation has typically involved comparing an observed background with a highly idealized deterministic model of radioxenon signature production by a UNE that does not consider the influence of post-detonation chemical/physical processes in the detonation cavity or the subsequent gas transport mechanisms that can also affect the isotopic composition of the detected gas signature. In addition, purely deterministic models, as previously employed, overlook the uncertainty inherent in estimating critical parameters characterizing the UNE and its detonation environment. In this paper, we create detailed, multi-parameter models of radionuclide evolution using the widely accepted England and Rider post-detonation radionuclide decay-chain network coupled to detailed models simulating physical production and transport processes affecting the gas signature. Because these models are governed by uncertain parameters including barometric fluctuations, realistic ranges of variation for each of the parameters influencing isotopic composition are then defined. A Latin-Hypercube sampling approach is used to obtain a random distribution of isotopic production and gas transport results associated with a given value of each parameter. We apply these results to background histories of two stations, one providing 4-isotope background measurements and the other providing two-isotope measurements associated with the 2013 DPRK announced UNE.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2025

Analysis of measurements from an array of radioxenon samplers near to Hartlepool Nuclear Power Station

As part of a scientific research and development project, the radionuclide fingerprint of an operating advanced gas-cooled reactor 25 (AGR) has been studied across several facets (Goodwin et al., 2024). One part of this project was to deploy an array of 26 radioxenon samplers to the region for a period of around 1 year, to measure any radioxenon emissions from the reactors of the 27 Hartlepool nuclear power station at a range of tens of kilometres away. The array of 3 sensors was operational for around 12 28 months from March 2022 and detected many occurrences of isotopes of radioxenon. Here we provide a detailed analysis and 29 interpretation of the data and where possible, attribution of detections to a source or region. Whilst a key part of this work is to 30 measure any emissions of radioxenon from Hartlepool, this work presents one of the most comprehensive efforts to determine the 31 source of a great number of (mostly) 133Xe detections. A combination of different types of atmospheric dispersion modelling 32 techniques, including the use of stack monitoring data from nearby civil radioxenon-emitting nuclear facilities has enabled the 33 majority of detections to be attributed to one or more possible sources. Whilst emissions from Hartlepool have been detected on 34 the systems, the majority of detections are associated with a medical isotope production facility in Fleurus, Belgium (IRE).

Advanced gas-cooled reactor↗

DOE OSTI2025

Explosive Byproduct Gas Transport Through Sorptive Geomedia

Current underground nuclear explosion (UNE) detection strategies rely heavily on atmospheric noble gas sampling of radioxenon. However, discriminating nuclear weapons testing programs from civilian sources is difficult due to highly variable atmospheric radioxenon backgrounds and processes affecting subsurface transport of parent radionuclides. Here, we aim to study the transport of gases produced by subsurface explosions as novel stable signatures for underground nuclear explosion (UNE) monitoring. These gases may be produced in large quantities with distinct molecular ratios, which will be impacted by subsurface transport processes. To demonstrate how ratios of gases produced by explosions can change during transport in geomaterials, we conducted laboratory benchtop experiments on the transport of carbon dioxide (CO 2 ) and hydrogen (H 2 ) gases through variably saturated zeolitic tuff, which is abundant at the historic US testing site. We observed that zeolitic tuff sorbs substantial quantities of CO 2 while allowing H 2 to transport more freely, leading to changes in the molecular ratios of the two gases along the transport pathway. Gas uptake in the dry zeolitic tuff core was 72.3% for CO 2 , compared with 53.4% for xenon and 7.6% for H 2 . The presence of 20% water saturation disrupted the CO 2 sorption process, though to a lesser extent than observed for noble gases, with a 36.7% drop in xenon sorption compared with a 21.9% drop for CO 2 . These results represent the first observations of zeolite sorption altering explosive gas ratios during transport through geomedia relevant to nuclear proliferation monitoring.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

A PIPS + SrI 2 (Eu) detector for atmospheric radioxenon monitoring

The PIPS–SrI 2 (Eu) is a prototype atmospheric radioxenon detection system designed at Oregon State University in support of international efforts towards monitoring clandestine nuclear weapon testing activities. This detector aims to address some shortcomings found in currently deployed beta–gamma atmospheric radioxenon detection systems, such as lackluster energy resolution and memory effect, by employing modern detection materials and readout. The system uses a PIPSBox, a silicon-based gas cell, for electron detection, and a pair of ultrabright, D-shaped SrI 2 (Eu) scintillators coupled to silicon photomultipliers for photon detection. A custom eight-channel digital pulse processor equipped with a field programmable gate-array (FPGA) identifies electron–photon coincidences between the volumes in near real-time. Gas samples of the four radioxenon isotopes of interest were independently measured with the PIPS–SrI 2 (Eu) detection system to determine energy resolution and efficiency. Application of FPGA-based coincidence discrimination in near real-time reduced the ambient background count rate by 95.85 ± 0.04%. Using parameters from the Xenon International gas processing unit and assuming a blank sample and zero memory effect the minimum detectable concentrations (MDCs) for the isotopes were calculated to be 0.12 ± 0.03, 0.27 ± 0.05, 0.15 ± 0.02, and 1.00 ± 0.08 mBq/m 3 air for 131m Xe, 133 Xe, 133m Xe, and 135 Xe, respectively. These MDC estimates compare well with other radioxenon detection systems employed in the International Monitoring System (IMS) and indicate that the PIPS–SrI 2 (Eu) is in compliance with the Comprehensive Nuclear Test-Ban-Treaty Organization (CTBTO) sensitivity requirement of ≤ 1 mBq/m 3 for 133 Xe.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗