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May 14, 2026Scientific Reports0 citationsOpen Access

Precursory seismicity unveiled before the Mw7.1 Dingri earthquake in Tibet

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FPF. PettenatiAVAlessandro VuanDSDenis Sandron

Key Points

  • The aim is to identify seismic patterns before ruptures using AI-enhanced monitoring.
  • Analysis of seismic events with a focus on the 2.4 magnitude event at the Himalayan front.
  • Utilization of AI techniques to evaluate single-station monitoring data.
  • Evaluation of the relationship between seismicity and regional tectonics.
  • Progressive increase in seismicity indicates a late-stage nucleation process.
  • Findings support that tectonic loading plays a role in seismic patterns.
  • AI-enhanced methods show promise for identifying seismicity patterns in data-sparse regions.

Abstract

4 event at the Himalayan front. We interpret this evolution as a slow, progressive increase in seismicity, consistent with a late-stage nucleation process modulated by regional tectonics. Our results are therefore compatible with tectonic loading, and suggest that single-station seismic monitoring, when enhanced by AI techniques, can help identify retrospective seismicity patterns prior to rupture, even in data-sparse and tectonically complex regions.

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Cite This Study

Pettenati et al. (2026) studied this question.

synapsesocial.com/papers/6a05659da550a87e60a1dfc7https://doi.org/10.1038/s41598-026-49197-5
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