Explosion Discrimination Using Seismic Gradiometry and Spectral Filtering of Data
- Carnegie Mellon Univ., Pittsburgh, PA (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Here, we present a new method to discriminate between earthquakes and buried explosions using observed seismic data. The method is different from previous seismic discrimination algorithms in two main ways. First, we use seismic spatial gradients, as well as the wave attributes estimated from them (referred to as gradiometric attributes), rather than the conventional three-component seismograms recorded on a distributed array. The primary advantage of this is that a gradiometer is only a fraction of a wavelength in aperture compared with a conventional seismic array or network. Second, we use the gradiometric attributes as input data into a machine learning algorithm. The resulting discrimination algorithm uses the norms of truncated principal components obtained from the gradiometric data to distinguish the two classes of seismic events. Using high-fidelity synthetic data, we show that the data and gradiometric attributes recorded by a single seismic gradiometer performs as well as a conventional distributed array at the event type discrimination task.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
- Grant/Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1781540
- Report Number(s):
- SAND--2021-2054J; 693980
- Journal Information:
- Bulletin of the Seismological Society of America, Journal Name: Bulletin of the Seismological Society of America Journal Issue: 3 Vol. 111; ISSN 0037-1106
- Publisher:
- Seismological Society of AmericaCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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