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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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DOE OSTI2021

High-Quality Revision of the Israeli Seismic Bulletin

Seismic bulletins, with trustworthy phase picks, origin times, and source locations are key for regional seismic studies, such as travel-time (TT) tomography, attenuation tomography, and anisotropy studies. To lay the groundwork for such studies in Israel, we revised the seismic bulletin of Israel and the surrounding area and obtained a trustworthy TT data set. From the earthquake and explosion bulletins of the Geophysical Institute of Israel, we compiled a starting data set of about 123,000 earthquakes and explosions that occurred during the past 40 yr. After screening out the poorly recorded events, we were left with a data set of ~38,000 well-recorded events. We then revised the remaining data set in two consecutive steps. In the first, we reviewed and updated station metadata, including changes in station metadata parameters over time. In the second step, we jointly relocated a list of selected seismic events, using the Bayesian hierarchical location software package (BayesLoc) of Myers et al. (2007) that performs joint relocation of multiple events. We observed striking dissimilarities between the spatial distributions of the newly relocated catalog and the initial locations. Although the depth distribution of the starting catalog is trimodal with peaks at 0, 5, and 10 km, the distribution in this study is unimodal, with a broad peak between 7.5 and 12.5 km. By differencing the observed arrival times and the origin times obtained through relocation with BayesLoc, we obtained a revised TT database that consists of 261,336 Pg, 132,876 Pn, 114,816 Sg, and 60,394 Sn arrivals, from a set of 30,458 jointly relocated seismic sources. In this work, we compared prerevision and postrevision TTs as a function of epicentral distance and concluded that the revised data set contains far fewer outliers and inconsistencies than the original data set. The revised TT data set may be used for seismic studies, such as TT tomography, attenuation tomography, and anisotropy studies.

58 GEOSCIENCES↗
DOE OSTI2021

Exploring the Effects of Emplacement Conditions on Explosion P/S Ratios across Local to Regional Distances

High-frequency (~> 2 Hz) seismic P/S amplitude ratios are well-established as a discriminant to distinguish between natural earthquakes and underground explosions at regional distances (~200–1500 km). As research shifts toward identifying lower-yield events, work has begun to investigate the potential of this discriminant for use at local distances (<200 km), in which initial results raise questions about its effectiveness. Here, we utilize data from several chemical explosion experiment series at the Nevada National Security Site in southern Nevada in the United States to study explosion Pg/Lg ratios across the range of local to regional distances. Additionall, the experiments are conducted over differing emplacement conditions, with contrasting geologies and a variety of yields and depths of burial, including surface explosions. We first establish the similarities of Pg/Lg ratios from chemical explosions to those from historic nuclear tests and conclude that, as previous data have suggested, chemical explosion ratios are good proxies for nuclear tests. We then examine Pg/Lg ratios from the new experiment series as functions of distance, yield, depth of burial, and scaled depth of burial (SDOB). At far-local and regional distances, we observe consistently higher ratios from hard-rock explosions compared to ones in a weaker dry alluvium medium, consistent with prior regional distance results. No other trends with yield, depth of burial, or SDOB are strongly evident. Scatter in the observed ratios is very high, particularly at the shortest event-to-station distances, suggesting that small-scale path effects play a significant role. On average, the local distance explosion Pg/Lg ratios show remarkable consistency across all the variations in emplacement. Explosion source models will need to reproduce these results.

58 GEOSCIENCES↗
DOE OSTI2022

Estimating Explosion Yields Using Moment Tensor Solutions and Seismic Moment

We report 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 m b -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.

58 GEOSCIENCES↗
DOE OSTI2024

Evaluating Physics-Informed Neural Network Performance for Seismic Discrimination between Earthquakes and Explosions

In this article, we evaluate adding a weak physics constraint, that is, a physics‐based empirical relationship, to the loss function with a physics‐informed manner in local distance explosion discrimination in the hope of improving the generalization capability of the machine learning (ML) model. We compare the proposed model with the two‐branch model we previously developed, as well as with a pure data‐driven model. Unexpectedly, the proposed model did not consistently outperform the pure data‐driven model. By varying the level of inconsistency in the training data, we find this approach is modulated by the strength of the physics relationship. In conclusion, this result has important implications for how to best incorporate physical constraints in ML models.

58 GEOSCIENCES↗