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GroningenNet: Deep Learning for Low-Magnitude Earthquake Detection on a Multi-Level Sensor Network.

Ahmed Shaheen1, Umair Bin Waheed1, Michael Fehler2

  • 1Department of Geosciences, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.

Sensors (Basel, Switzerland)
|December 10, 2021
PubMed
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This summary is machine-generated.

A new convolutional neural network (CNN) effectively detects low-magnitude earthquakes using multi-level borehole sensors. This method improves seismic event detection and reduces noise, outperforming traditional algorithms.

Area of Science:

  • Seismology
  • Geophysics
  • Machine Learning

Background:

  • Induced seismicity is rising globally, necessitating robust detection of low-magnitude earthquakes.
  • Accurate micro-earthquake detection is vital for monitoring operations like hydraulic fracturing and understanding seismic mechanisms.
  • Existing detection algorithms struggle to reliably distinguish weak seismic events from local noise.

Purpose of the Study:

  • To develop and evaluate a convolutional neural network (CNN) for enhanced seismic event detection.
  • To leverage multi-level borehole sensor data for improved discrimination of subsurface events from surface noise.
  • To assess the CNN's performance against traditional seismic detection methods.

Main Methods:

  • A CNN model was trained using seismic records from multi-level geophones (50-200m depths) at Groningen borehole stations.
Keywords:
convolutional neural networksdeep learninginduced seismicitymicro-earthquakes

Related Experiment Videos

  • The CNN utilized energy moveout patterns across sensors as a key feature for event discrimination.
  • Performance was compared against Short-Term Average/Long-Term Average (STA/LTA) and template matching algorithms.

    • Main Results:

    • The CNN model demonstrated significantly superior performance in detecting previously uncatalogued events.
    • The proposed CNN approach substantially reduced false positive detections compared to STA/LTA and template matching.
    • Utilizing moveout features allowed for effective model training with reduced data requirements.

    Conclusions:

    • The developed CNN offers a robust and efficient solution for detecting low-magnitude seismic events.
    • The multi-level sensor approach and moveout feature analysis enhance discrimination capabilities.
    • This methodology is adaptable for microseismic monitoring in networks with similar multi-level sensor configurations.