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Identifying and Characterizing Local Seismicity With a Dynamic Correlation Processor

Description: 

Detecting and classifying underground explosions are essential tasks for global security, but, particularly for small explosions that may only be recorded at local distances, they can be challenging ones. We seek to further our understanding of the capability to detect and characterize small, local, seismic events utilizing data from the Nevada National Security Site (NNSS) and surrounding area. As a first step to investigate the background seismicity in the region, we apply a Dynamic Correlation Processor (DCP) to the data. The DCP utilizes a bank of power and subspace detectors that grows in time as new waveform patterns are encountered. Correlation detection thresholds are determined dynamically in order to adjust to changes in background noise levels. We focus initially on a time period in June of 2019 which includes a large number of catalog events, including a known chemical explosion from the Source Physics Experiment, that can be compared to the results from the DCP. Future work will involve more detailed exploration of the resulting groups of detections, including relative magnitudes and discrimination analysis.

Prepared by LLNL under Contract DE-AC52-07NA27344.

Session: Exploiting Explosion Sources: Advancements in Seismic Source Physics [Poster]

Type: Poster

Date: 4/19/2023

Presentation Time: 08:00 AM (local time)

Presenting Author: Ana C. Aguiar

Student Presenter: No

Invited Presentation: 

Authors