We present a constructive method to fuse and evaluate multiple waveform statistics that improves our capability to detect small, aboveground explosions over similar methods that use single waveform statistics. Our method advances Fisher's Combined Probability Test (Fisher's Method) to operate under both null and alternative hypotheses of a binary test on noisy data, which allows researchers to forecast their ability to screen fused explosion signatures from noise, against source size. Our data show that (1) a fused multi-physics waveform statistic that combines radio, acoustic and seismic waveform data can identify explosions ~ 0.4 magnitude units lower than the most capable, constituent radio emission detector for the same detection probability and (2) that we can quantitively predict how this fused, multi-physics statistic performs with Fisher's Method. This work offers a baseline, quantitative foundation for event detection that supports multi-phenomenological explosion monitoring (multiPEM) that and applicable to any binary hypothesis testing problem in observational geophysics.
Presenting Author: Joshua D. Carmichael
Authors
Joshua D Carmichael
Presenting Author Corresponding Author
joshuac@lanl.gov
Los Alamos National Laboratory, Los Alamos, New Mexico, United States
Presenting Author
Corresponding Author
Neill P Symons
symons@lanl.gov
Los Alamos National Laboratory, Los Alamos, New Mexico, United States
Michael L Begnaud
mbegnaud@lanl.gov
Los Alamos National Laboratory, Los Alamos, New Mexico, United States
A Method to Fuse Multi-Physics Waveforms and Improve Predictive Explosion Detection: Theory, Experiment and Performance