Abstract 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.

The detonation of a well-recorded coupled explosion at the well-characterized Nevada National Security Site, as part of Physics Experiment One, has provided us with an opportunity to compare and contrast methods and results of yield estimation and other event characterization methods. Yield estimates are made using several methods, including local/regional magnitudes, waveform envelopes, acoustic amplitudes, joint seismoacoustic yield, and moment tensors. We include in our analysis a discussion and comparison of applicability, calibrations, assumptions, and uncertainties involved in each. With the exception of magnitude-based methods, all the techniques were more or less successful in recovering the known yield. It is, however, useful to be cognizant of requirements and calibrations, such as station coverage, emplacement conditions, propagation characterization, and uncertainties, to apply the methods to other areas with less complete information. We demonstrate that we can be successful at monitoring low-yield explosions using a variety of methods assuming that the signal-to-noise ratio is adequate at recording stations, and that the material properties are taken into account. Depth estimates are also important, particularly if one allows the possibility of a surface explosion.

Forensic analysis of man-made, non-nuclear events (such as industrial accidents, explosion experiments and mine collapses) has become more frequent and detailed owing to advancements in geophysical monitoring. In this Technical Review, we demonstrate how geophysical forensic monitoring using seismic, infrasound and hydroacoustic recordings provides insights on events in the solid earth, atmosphere and underwater. Advanced techniques, including machine-learning-based models, have been developed to detect, identify and investigate these events, providing information on location, subevents, sources and explosive yield. The increase in data availability, application of advanced methods and computation and the growth of multitechnology approaches have increased the accuracy of forensic event analysis and enabled more realistic characterization of uncertainties. For example, the 2020 Beirut explosion in Lebanon demonstrated that various seismic, acoustic and other methods could be used to estimate explosive yield (and yield uncertainties) of about 1 ktonne, providing confidence in the application of these methods to smaller events where data are available. However, forensic investigations remain largely limited to known events with identified sources. Increased access to data, sophisticated analysis methods and high-resolution earth models will improve forensic event analysis further, enabling civil and scientific applications, such as localization in the search for the lost ARA San Juan submarine. Forensic event analysis is used to investigate non-nuclear, man-made, explosion-like accidents and unanticipated events. This Technical Review outlines the techniques used to monitor and analyse the seismic, infrasound and hydroacoustic signals produced by such events in underground, near-surface, atmospheric and underwater domains.

The detonation of a well-recorded coupled explosion at the well-characterized Nevada National Security Site, as part of Physics Experiment One, has provided us with an opportunity to compare and contrast methods and results of yield estimation and other event characterization methods. Yield estimates are made using several methods, including local/regional magnitudes, waveform envelopes, acoustic amplitudes, joint seismoacoustic yield, and moment tensors. We include in our analysis a discussion and comparison of applicability, calibrations, assumptions, and uncertainties involved in each. With the exception of magnitude-based methods, all the techniques were more or less successful in recovering the known yield. It is, however, useful to be cognizant of requirements and calibrations, such as station coverage, emplacement conditions, propagation characterization, and uncertainties, to apply the methods to other areas with less complete information. We demonstrate that we can be successful at monitoring low-yield explosions using a variety of methods assuming that the signal-to-noise ratio is adequate at recording stations, and that the material properties are taken into account. Depth estimates are also important, particularly if one allows the possibility of a surface explosion.

The largest source of uncertainty in any source inversion is the velocity model used in the transfer function that relates observed ground motion to the seismic moment tensor. However, standard inverse procedure often does not quantify uncertainty in the seismic moment tensor due to error in the Green's functions from uncertain event location and Earth structure. We incorporate this uncertainty into an estimation of the seismic moment tensor using a data-derived distribution of velocity models based on complementary geophysical data sets, including thickness constraints, velocity profiles, gravity data, surface-wave group velocities, and regional body-wave travel times. The data-derived distribution of velocity models is then used as a prior distribution of Green's functions for use in Bayesian inference of an unknown seismic moment tensor using regional and teleseismic-P waveforms. The use of multiple data sets is important for gaining resolution to different components of the moment tensor. The combined likelihood is estimated using data-specific error models and the posterior of the seismic moment tensor is estimated and interpreted in terms of the most probable source type.

ABSTRACT We use the Pasyanos and Chiang (2022) data set to calculate the seismic moment M0 for each explosion and use the measured explosive yield W to validate the W∼M0 relationship in Denny and Johnson (1991; hereafter, DJ91). The M0 is corrected by transforming to a potency tensor and applying more appropriate near-source geophysical parameter values in the moment estimate. The mean residual between observed and predicted yield is near zero; however, the standard deviation of the residuals results in an F-value (a 95% confidence factor) of about 5. We re-estimate the coefficients in the DJ91 model and find similar values and only a slight improvement in the F-value. Next, we embark on a similar model selection process as DJ91, allowing for non-cube-root yield scaling and other plausible near-source elastic moduli. As was found by DJ91, the yield dependence is not significantly different from unity, and a cube root assumption is valid. Therefore, we yield scale the seismic moment and test the significance of all plausible explanatory variables. Isotropic moment performs better in the response variable than total moment. The preference for isotropic moment could be due to its relationship to volume change, which would be more directly affected by explosive yield. Surprisingly, we find that the overburden pressure, which is a function of depth, is not a significant parameter in the model. We hypothesize that this is due to the competing depth effects on source asymmetry and the incorporation of depth in the Green’s functions used to calculate the seismic moment tensors. Importantly, this emphasizes that only seismic moment tensor-derived moments should be used in these models. After removing insignificant model parameters, we are left with a simple model to predict explosive yield W^ in kt from isotropic moment MI in N·m, W^=κ−1.4132 100.035626GPMI, in which κ and GP are the near-source bulk modulus and gas porosity in Pa and %, respectively. The F-value for this model is approximately 3.

SUMMARY We introduce a seismic identification method for collapse events using moment tensors (MTs). We start by computing full (six-element) MT solutions for 43 identified collapse events from around the world, and statistically characterizing the population on the MT hypersphere. We then test a large data set of over 1000 full MTs for the western U.S. against the distribution of collapses using a MT-based identification method similarly as used for testing explosions. Known collapses and explosions are readily identified, along with other anomalous events in the Geysers and central California coast. Misidentification rates are determined for various screening angles with optimal misidentification rates between earthquakes and collapses on the order of 3 per cent. The method is demonstrated to be very effective at identifying non-earthquake sources with a 97–98 per cent accuracy. It is likely to be transportable to other regions, and can be used for event identification anywhere full MT solutions are routinely calculated.

ABSTRACT The Large Surface Explosion Coupling Experiment (LSECE) is a chemical explosion experiment conducted in Yucca Flat at the Nevada National Security Site in 2020. The experiment included two surface detonations of ∼1000 kg trinitrotoluene equivalent. The main goal of this experiment was to provide the ground-truth data for seismoacoustic wave excitation by large chemical explosions near the ground surface. The seismic and acoustic energy partitioning between the surface is strongly governed by the depth or height of explosions, and either seismic or acoustic-only analysis may have inherent ambiguity in determining explosion yield and depth simultaneously. Previous studies suggested that joint seismoacoustic analysis can resolve the trade-off and reduce the uncertainty of yield and depth estimation dramatically. We demonstrate the capability of seismoacoustic analysis to improve the accuracy of explosion yield and depth estimation with the LSECE data. Local acoustic wave propagation in the atmosphere can be substantially affected by constantly varying weather conditions. Consisting of two detonations before dawn and in the afternoon, LSECE provides unique data to evaluate the model accuracy of acoustic wave propagation and seismoacoustic energy partitioning depending on local atmospheric conditions. We quantitatively evaluate the accuracy of yield and depth estimation depending on atmospheric variability and the improvement achieved by the joint seismoacoustic approach.

Seismic moment, a measurable and well-understood quantity of seismic sources, is used to estimate the yield of explosions. Application of such a method in the past, as in the manner of mb-derived yields, has been complicated by the effect of variations in the explosion working point, depth, and secondary source effects (such as spalling and tectonic release) on the observed moment. We start using the full (six-element) moment tensor solution, which can capture the relevant source physics and, at least in theory, better isolate the primary explosion source. The moment-to-yield ratio is then estimated using an explosion source model which, provided with emplacement conditions, can relate the two parameters. We discuss the major sources of uncertainty associated with the method, and calibrate it with chemical and nuclear explosions at the Nevada National Security Site. We then apply the method to published moment tensor solutions for the six declared North Korean nuclear explosions that occurred between 2006 and 2017. The results are mostly consistent with other yield estimates made using a variety of high-frequency methods. This technique is a new approach to estimating explosive yield and simple to implement, as much of the complexity is captured by the source models.

The August 2020 Beirut explosion is the largest single-fired ammonium nitrate explosion documented in history. The massive explosion excited loud infrasound in the atmosphere, and clear waveforms were recorded by a regional infrasound array at an epicentral distance of 100 km, allowing for accurate measurements of explosion energy. We estimate the explosion size based on the infrasound waveform inversion. Unlike conventional inversions using empirical models, we perform full 3-D finite-difference simulations to obtain a physics-based propagation model for the inversion. Accurate numerical modeling of infrasound is challenging as the propagation is substantially affected by the turbulent atmosphere. Instead of a single deterministic prediction, we provide a range of waveform predictions by running multiple simulations with stochastic weather forecast models, which allows for comprehensive uncertainty analysis of numerical modeling and estimated yields. Finally, we expand the yield estimation technique for seismoacoustic analysis and demonstrate the substantial advantage of the joint approach.

We characterize the attenuation structure of the Arabian Peninsula through the measurement of regional phase amplitudes. High-resolution is achieved by combining stations from global networks with national network data through the cooperative effort of several countries in the region, including Saudi Arabia, Oman, Iraq, and Kuwait. The result is an improved attenuation model of the crust and upper mantle for a broad frequency band that extends from 0.5 to 10 Hz. The observed attenuation is in accordance with various elements of earth structure, including plate boundary type, style of tectonism, thermo-tectonic age, and temperature. Emerging features from the model include details in the structure along the Red Sea, and improved imaging of the southern Arabian Peninsula extending north from the Gulf of Aden. The resulting attenuation model can be employed for better magnitude estimates, in isolating tectonic and structural features, and in characterizing strong ground motion in the Arabian Peninsula.

Moment tensor (MT) solutions are proving increasingly valuable in explosion monitoring, especially now that they are more routinely calculated for the unconstrained, full (six component) MT. In this study, we have calculated MTs for U.S. underground nuclear tests conducted at the Nevada National Security Site using seismic recordings primarily from the Livermore Nevada Network. We are able to determine them for 130 nuclear explosions from 1970 to 1996 for a range of yields and under a variety of material conditions, which we have supplemented with 10 additional chemical explosions at the test site. The result is an extensive database of MTs that can be used to assess the performance of important monitoring tasks such as event identification and yield determination. We test the explosion event screening on the fundamental lune of the MT eigensphere and find MT screening to be a robust discriminant between earthquakes and explosions. We then explore the estimation of moment-derived yield, in which we find that material properties are the largest contributor to differences in the estimated moment-to-yield ratio. Further research conducted on this dataset can be used to develop, test, and improve various explosion monitoring methodologies.

The goal of this study was to use U.S. nuclear explosions with known source parameters (yield, depth, shot point material and/or parameters) to determine moment-derived yield estimates. This work has been accomplished by performing full moment tensor solutions using regional network data from the LLNL network, and other regional broadband stations. As part of this study, we have calculated solutions for 130 U.S. nuclear explosions and 12 additional chemical explosions. We then take several approaches to using moment tensors to estimate yield, considering both the full and isotropic moment tensors, doing straight regression analysis on the whole dataset, then successively refining the calibration with additional information about material and overburden. We have also tried a completely new approach of using the seismic moment to help estimate the radiated seismic energy and tying this to yield through a seismic efficiency. Results appear to be promising, but more work might be required to make this more useful in an operational sense.

Seismic and acoustic recordings have long been used for the forensic analysis of various natural and anthropogenic events, especially in the realm of nuclear treaty monitoring. More recently, multi-phenomenological analysis has been applied to these signals with great success, providing unique constraints for studying a broad range of source events, including man-made noise, earthquakes and explosions. In particular, the fusion of seismic and infrasonic data has proven valuable for the analysis of explosive yield, significantly improving on the yield estimates obtained from either seismic or acoustic analysis alone. Unfortunately, the seismo-acoustic analysis of local explosions is complicated by the fact that the two phenomena are potentially co-dependent. Large seismic waves displace the earth like a piston, potentially inducing acoustic waves into the atmosphere as they pass. Similarly, large acoustic waves can couple into the earth, inducing ground motion along their path. This co-dependence can be problematic, particularly when the passing acoustic shockwave couples into the earth coincident with a seismic phase arrival, thereby corrupting the signal. To address this problem, we present a method for isolating the shockwave response of a seismic sensor, such that any underlying seismic phase arrivals can be recovered. This is accomplished by employing the adaptive noise cancellation model, where a co-located infrasound sensor is used as a reference measurement for the shockwave. In this model, the adaptive filter learns the transform between the relative atmospheric pressure (as recorded by the infrasound sensor), and the resulting ground motion (as recorded by the seismometer). In this way, the filtered infrasound recording approximates the seismic shockwave response, and can be subtracted from the seismograph to recover the phase arrivals. The experimental data comes from a set of three low-yield near-surface chemical explosions conducted by LLNL as part of a field experiment, known as FE2. The explosions were recorded at eight stations, located at varying distances from the source (between 64m and 2km), with each station consisting of a co-located three-component seismic velocity transducer and differential infrasound sensor. The adaptive technique is demonstrated for recovering seismic arrivals in both the vertical and horizontal channels across all eight stations, and evaluated using leave-one-out cross-validation across the three explosions.

The overall goal of this project is to build a high-resolution velocity and attenuation model for a region spanning from the eastern Mediterranean Sea and extending eastward into the Middle East. Phase I of this project, covered in this report, focuses on the analysis of the SHDAS array, updated dispersion and amplitude measurements, development of surface wave group velocity models, and construction of a preliminary 1-degree, velocity-only model for the study region. These will be discussed in turn.

In recent years, two sets of chemical explosions have been conducted at the Nevada National Security Site: six explosions from Phase I of the Source Physics Experiment and four explosions from the Forensics Surface Events. We have estimated the explosive yield and depth‐of‐burial/height‐of‐burst of both from the synthesis of seismic and low‐frequency acoustic data. Analysis of the seismic signal is performed using a waveform envelope technique. Using seismic data only, however, there is a trade‐off between the yield and depth or height of the explosion, which becomes especially acute for near‐surface events. This trade‐off between yield and depth/height can be broken by including complementary analysis of the acoustic signal using the acoustic waveform inversion method. The acoustic signal also has a strong trade‐off between a yield and depth/height, but the slope is opposite that of the seismic for near‐surface events. We use a likelihood function to combine the seismic and acoustic analysis and improve our estimates of yield and depth/height for these sets of explosions. The results are generally consistent with the known parameters, but can be improved upon by higher‐frequency seismic analysis and improved acoustic impulse scaling for large‐scaled depths‐of‐burial. These events serve as prime examples of data fusion from multiple data streams. The challenge will be to apply the method to events at longer regional distances where the seismic signals will include phases traveling in the upper mantle, and the acoustic signals will be recorded as infrasound signals, which are subject to atmospheric variability.

The Source Physics Experiment (SPE) series is a long-term NNSA research and development effort designed to improve U.S. arms control and nuclear nonproliferation verification and monitoring capabilities. The findings from the SPE will advance the United States’ nuclear explosion monitoring capabilities, particularly with respect to detection, discrimination and determination of yields associated with small nuclear explosions that can be lost amid the noisy seismo-acoustic background from other sources. The data generated from the SPE, a series of well-designed and recorded chemical explosions, will contribute to the development and validation of first-principles explosive source generated seismo-acoustic modeling codes. These codes will then facilitate the update of semi-empirical methods, currently based on historic test site data, such that key explosion observables can be reproduced, thus improving confidence in nuclear test monitoring in new areas and/or under novel emplacement conditions. The overall SPE project is comprised of both the development of the new explosion simulation codes and the chemical explosion test series. The chemical explosion test series will generate the empirical data required to both develop and validate the new simulation codes.

On 3 September 2017, the Democratic People's Republic of Korea (DPRK) conducted its sixth and largest declared nuclear test at the Punggye-ri test site. Recently, we have been using regional waveform envelopes to estimate the explosive yield and overburden of chemical and nuclear explosions by coupling explosion source models to propagation parameters. Similar to most yield determination methods, there can be trade-offs between yield and depth, leading to uncertainties in both parameters. The relative locations are well constrained by small timing differences in seismic phase arrivals at stations that recorded multiple events, but there are potential uncertainties on the absolute locations. Depths are poorly constrained by the relative arrival times. In this study, we performed a coupled location and yield analysis of the DPRK nuclear tests. We obtain highly accurate travel times using correlation methods and relative locations using a Bayesian location method. Then, while keeping the relative locations of the six tests constant, we consider the consequences of shifts to the absolute locations on the resulting overburden for each event. Given that overburden, we determine the yield that minimizes the waveform misfit. We also test and compare a number of explosion source models. By considering the coupled location/depth/yield problem, we reduce uncertainties in the absolute locations, and determine yield and depth estimates of the events. Based on statistical analysis, we estimate that the 2017 test has a yield of 125 kt (equivalent trinitrotoluene [TNT]) with a 1 sigma uncertainty range of 103-150 kt at about 600 m of overburden.