Probabilistic source-type classification from seismic moment tensors

Clicks: 11
ID: 331831
2026
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Abstract
Summary Seismic source classification often relies on a single best-fitting moment tensor and deterministic rules on source-type diagrams. This study formulates classification directly in terms of the posterior distribution of the moment tensor on the fundamental lune. Moment tensors from waveform inversion are mapped into a uniform (v, w) source-type domain and used to build empirical source-type probability density functions p(v, w). We evaluate five hypotheses (±ISOCAL, DC, ±CLVD) plus a residual baseline, where ISOCAL denotes a calibrated isotropic kernel relative to the canonical ISO endmember. Classification probabilities P(Hi) are obtained by integrating p(v, w) against the hypothesis kernels; a residual probability Presidual is included as a sixth baseline (uniform) model that absorbs probability when the posterior is diffuse or poorly matched by the prototypes. The framework is applied to published regional moment tensor ensembles for 32 events, including 23 nuclear tests at the Nevada National Security Site and the DPRK Punggye-Ri test site, plus nearby earthquakes and collapses. Well-recorded underground nuclear tests yield tightly localized +ISOCAL probabilities with small Presidual, while events recorded by sparse, complex source-receiver paths show broader probability vectors and larger Presidual. Tectonic earthquakes map primarily to double-couple and CLVD hypotheses, while collapses map primarily to negative isotropic (–ISOCAL), with secondary CLVD in some events. The method is applicable wherever source-type posteriors are available, given a declared hypothesis family, and produces explicit hypothesis probabilities with uncertainty and a residual diagnostic that flags diffuse or weakly matched posteriors, and can feed directly into monitoring and decision pipelines.
Reference Key
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Authors Celso Alvizuri
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Journal geophysical journal international
Year 2026
DOI
10.1093/gji/ggag427
URL
https://doi.org/10.1093/gji/ggag427
Keywords Keywords not found

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