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skills/benchflow-ai/skillsbench/tasks-earthquake-phase-association-environment-skills-seismic-picker-selection

tasks-earthquake-phase-association-environment-skills-seismic-picker-selection

1
benchflow-ai/skillsbench·Research·Audit pending·Snapshot 60720abe8370

Summary

This source did not publish a separate summary. Review SKILL.md before using the skill.

SKILL.md

Seismic Event Detection & Phase Picking Method Selection Guide

Overview: Method Tradeoffs

When choosing an event detection and phase picking method, consider these key tradeoffs:

MethodGeneralizabilitySensitivitySpeed, Ease-of-UseFalse Positives
STA/LTAHighLowFast, EasyMany
ManualHighHighSlow, DifficultFew
Deep LearningHighHighFast, EasyMedium
Template MatchingLowHighSlow, DifficultFew
  • Generalizability: Ability to find arbitrary earthquake signals
  • Sensitivity: Ability to find small earthquakes

Key insight: Each method has strengths and weaknesses. Purpose and resources should guide your choice.

STA/LTA (Short-Term Average / Long-Term Average)

Advantages

  • Runs very fast: Automatically operates in real-time
  • Easy to understand & implement: Can optimize for different window lengths and ratios
  • No prior knowledge needed: Does not require information about earthquake sources or waveforms
  • Amplitude-based detector: Reliably detects large earthquake signals

Limitations

  • High rate of false detections during active sequences
  • Automatic picks not as precise
  • Requires manual review and refinement of picks for a quality catalog

Template Matching

Advantages

  • Optimally sensitive detector (more sensitive than deep-learning): Can find smallest earthquakes buried in noise, if similar enough to template waveform
  • Excellent for improving temporal resolution of earthquake sequences
  • False detections are not as concerning when using high detection threshold
  • Limitations

    • Requires prior knowledge about earthquake sources: Need template waveforms with good picks from a preexisting catalog
    • Does not improve spatial resolution: Unknown earthquake sources that are not similar enough to templates cannot be found
    • Setup effort required: Must extract template waveforms and configure processing
    • Computationally intensive

    Deep Learning Pickers

    When to Use

    • Adds most value when existing seismic networks are sparse or nonexistent
    • Automatically and rapidly create more complete catalog during active sequences
    • Requires continuous seismic data
    • Best on broadband stations, but also produces usable picks on accelerometers, nodals, and Raspberry Shakes
    • Use case: Temporary deployment of broadband or nodal stations where you want an automatically generated local earthquake catalog

    Advantages

    • No prior knowledge needed about earthquake sources or waveforms
    • Finds lots of small local earthquakes (lower magnitude of completeness, Mc) with fewer false detections than STA/LTA
    • Relatively easy to set up and run: Reasonable runtime with parallel processing. SeisBench provides easy-to-use model APIs and pretrained models.

    Limitations

    • Out-of-distribution data issues: For datasets not represented in training data, expect larger automated pick errors (0.1-0.5 s) and missed picks
    • Cannot pick phases completely buried in noise - Not quite as sensitive as template-matching
    • Sometimes misses picks from larger earthquakes that are obvious to humans, for unexplained reason

    References

    • This skill is a derivative of Beauce, Eric and Tepp, Gabrielle and Yoon, Clara and Yu, Ellen and Zhu, Weiqiang. Building a High Resolution Earthquake Catalog from Raw Waveforms: A Step-by-Step Guide Seismological Society of America (SSA) Annual Meeting, 2025. https://ai4eps.github.io/Earthquake_Catalog_Workshop/
    • Allen (1978) - STA/LTA method
    • Perol et al. (2018) - Deep learning for seismic detection
    • Huang & Beroza (2015) - Template matching methods
    • Yoon and Shelly (2024), TSR - Deep learning vs template matching comparison

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