Regional Source-Type Discrimination Using Nonlinear Alignment Algorithms

The discrimination problem in seismology aims to accurately classify different underground source types based on local, regional, and/or teleseismic observations of ground motion. Typical discriminant approaches are rooted in fundamental, physics-based differences in radiation pattern or wave excita...

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Main Authors: Marlon D. Ramos, Rigobert Tibi, Christopher J. Young, Erica L. Emry
Format: Article
Language:English
Published: Seismological Society of America 2025-02-01
Series:The Seismic Record
Online Access:https://doi.org/10.1785/0320240038
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author Marlon D. Ramos
Rigobert Tibi
Christopher J. Young
Erica L. Emry
author_facet Marlon D. Ramos
Rigobert Tibi
Christopher J. Young
Erica L. Emry
author_sort Marlon D. Ramos
collection DOAJ
description The discrimination problem in seismology aims to accurately classify different underground source types based on local, regional, and/or teleseismic observations of ground motion. Typical discriminant approaches are rooted in fundamental, physics-based differences in radiation pattern or wave excitation, which can be frequency-dependent and may not make use of the full waveform. In this article, we explore whether phase and amplitude distances derived from dynamic time warping (DTW) and elastic shape analysis (ESA) can inform event discrimination. We demonstrate the ability to distinguish underground point sources using synthetic waveforms calculated for a 1D Earth model and various source mechanisms. We then apply the method to recorded data from events in the Korean Peninsula, which includes declared nuclear explosions, a collapse event, and naturally occurring earthquakes. Phase and amplitude distances derived from DTW and ESA are then used to classify the event types via dendrogram and k-nearest-neighbor clustering analyses. Using information from the full waveform, we show how different underground sources can be distinguished at regional distances. We highlight the potential of these nonlinear alignment algorithms for discrimination and comment on ways we can extend the framework presented here.
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publisher Seismological Society of America
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spelling doaj-art-484d707e8d014568a2c9778bbde4c4392025-08-20T03:00:04ZengSeismological Society of AmericaThe Seismic Record2694-40062025-02-01519710610.1785/0320240038tsr2024038Regional Source-Type Discrimination Using Nonlinear Alignment AlgorithmsMarlon D. Ramos0https://orcid.org/0000-0003-4449-8624Rigobert Tibi1https://orcid.org/0000-0002-4784-3940Christopher J. Young2https://orcid.org/0000-0001-5027-3216Erica L. Emry3https://orcid.org/0000-0001-5515-1568Sandia National Laboratories, Albuquerque, New Mexico, U.S.A.Sandia National Laboratories, Albuquerque, New Mexico, U.S.A.Sandia National Laboratories, Albuquerque, New Mexico, U.S.A.Sandia National Laboratories, Albuquerque, New Mexico, U.S.A.The discrimination problem in seismology aims to accurately classify different underground source types based on local, regional, and/or teleseismic observations of ground motion. Typical discriminant approaches are rooted in fundamental, physics-based differences in radiation pattern or wave excitation, which can be frequency-dependent and may not make use of the full waveform. In this article, we explore whether phase and amplitude distances derived from dynamic time warping (DTW) and elastic shape analysis (ESA) can inform event discrimination. We demonstrate the ability to distinguish underground point sources using synthetic waveforms calculated for a 1D Earth model and various source mechanisms. We then apply the method to recorded data from events in the Korean Peninsula, which includes declared nuclear explosions, a collapse event, and naturally occurring earthquakes. Phase and amplitude distances derived from DTW and ESA are then used to classify the event types via dendrogram and k-nearest-neighbor clustering analyses. Using information from the full waveform, we show how different underground sources can be distinguished at regional distances. We highlight the potential of these nonlinear alignment algorithms for discrimination and comment on ways we can extend the framework presented here.https://doi.org/10.1785/0320240038
spellingShingle Marlon D. Ramos
Rigobert Tibi
Christopher J. Young
Erica L. Emry
Regional Source-Type Discrimination Using Nonlinear Alignment Algorithms
The Seismic Record
title Regional Source-Type Discrimination Using Nonlinear Alignment Algorithms
title_full Regional Source-Type Discrimination Using Nonlinear Alignment Algorithms
title_fullStr Regional Source-Type Discrimination Using Nonlinear Alignment Algorithms
title_full_unstemmed Regional Source-Type Discrimination Using Nonlinear Alignment Algorithms
title_short Regional Source-Type Discrimination Using Nonlinear Alignment Algorithms
title_sort regional source type discrimination using nonlinear alignment algorithms
url https://doi.org/10.1785/0320240038
work_keys_str_mv AT marlondramos regionalsourcetypediscriminationusingnonlinearalignmentalgorithms
AT rigoberttibi regionalsourcetypediscriminationusingnonlinearalignmentalgorithms
AT christopherjyoung regionalsourcetypediscriminationusingnonlinearalignmentalgorithms
AT ericalemry regionalsourcetypediscriminationusingnonlinearalignmentalgorithms