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PhaseNet workflow boosts seismic wave detection accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new workflow using the PhaseNet machine learning model to improve seismic wave detection on teleseismic data. This workflow, implemented with MsPASS, significantly enhances the recall of P-wave picks by over 700% compared to models trained on regional data. While increasing model size improved accuracy, it drastically reduced inference speed, suggesting GPUs are more suitable than CPUs for scaling this application. AI

IMPACT Improves seismic data analysis accuracy, potentially aiding in earthquake detection and research.

RANK_REASON The cluster contains an academic paper detailing a new methodology and benchmark results for a machine learning model. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

PhaseNet workflow boosts seismic wave detection accuracy

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The cluster contains an academic paper detailing a new methodology and benchmark results for a machine learning model. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jinxin Ma, Yinzhi Wang, Gary L. Pavlis, Chenbo Yin ·

    Evaluating PhaseNet on Teleseismic Data with MsPASS

    arXiv:2605.22837v1 Announce Type: cross Abstract: Numerous studies have shown that the machine-learning picker PhaseNet produces accurate P and S picks on local earthquake signals, but its performance can degrade sharply on teleseismic signals. To address this limitation, we pres…

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