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Crowdsourcing earthquake prediction using machine learning (ML) on laboratory data yielded new insights. Competitors developed novel analysis methods, advancing seismic data understanding and ML applications in geophysics.

Keywords:
earthquake predictionlaboratory earthquakesmachine learning competitionphysics of faulting

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Area of Science:

  • Earthquake science
  • Geophysics
  • Machine learning applications

Background:

  • Earthquake prediction remains a significant challenge in seismology.
  • Machine learning (ML) offers potential for novel data analysis approaches.

Purpose of the Study:

  • To explore crowdsourcing via ML competitions for earthquake prediction.
  • To analyze laboratory earthquake data for forecasting future events.

Main Methods:

  • Utilized Kaggle, a machine learning competition platform.
  • Engaged over 4,500 teams to develop data analysis approaches.
  • Competitors predicted time to next laboratory earthquake from seismic data.

Main Results:

  • Over 400 computer programs were created and shared.
  • Winning teams used rescaling of failure times and data distribution comparisons.
  • Identified new insights into laboratory fault processes and seismic data evolution.

Conclusions:

  • Crowdsourcing ML competitions can advance geosciences.
  • This approach provides a model for engaging the ML community in scientific problems.
  • The competition served as a valuable pedagogical tool for ML in geophysics.