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  1. Home
  3. Predicting Fault Slip Via Transfer Learning.
  1. Home
  3. Predicting Fault Slip Via Transfer Learning.

Related Experiment Videos

Predicting fault slip via transfer learning.

Kun Wang1,2, Christopher W Johnson1, Kane C Bennett1

  • 1Geophysics Group, Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.

Nature Communications
|December 17, 2021

View abstract on PubMed

Summary
This summary is machine-generated.

Transfer learning with simulations accurately predicts laboratory fault slip. Fine-tuning with limited earthquake data further improves predictions, showing potential for real-world fault behavior forecasting.

Related Experiment Videos

Area of Science:

  • Geophysics
  • Machine Learning
  • Computational Seismology

Background:

  • Machine learning models for predicting fault slip show promise but require extensive training data.
  • Earthquake data is sparse, posing a significant challenge for training predictive models for real-world fault behavior.

Purpose of the Study:

  • To develop a transfer learning approach for predicting fault slip using numerical simulations.
  • To investigate the efficacy of training a convolutional encoder-decoder model with simulated data and fine-tuning it with laboratory experimental data.

Main Methods:

  • A convolutional encoder-decoder model was trained using acoustic emission and fault friction data from numerical simulations.
  • The trained model was then applied to predict fault-slip behavior in laboratory experiments.
  • Further fine-tuning of the model's latent space was performed using limited data from a single laboratory earthquake cycle.

    • Main Results:

    • The transfer learning model accurately predicted fault friction in laboratory experiments.
    • Fine-tuning the model with a small subset of laboratory data significantly improved prediction accuracy.
    • The study demonstrates successful generalization from simulated to experimental data.

    Conclusions:

    • Transfer learning offers a viable solution for training machine learning models on sparse geophysical data.
    • Models trained on numerical simulations can be effectively adapted for predicting fault slip in laboratory settings.
    • This approach holds potential for improving earthquake forecasting and understanding fault behavior in Earth.