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Related Experiment Video

Updated: Dec 12, 2025

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt
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Earthquake Probability Assessment for the Indian Subcontinent Using Deep Learning.

Ratiranjan Jena1, Biswajeet Pradhan1,2, Abdullah Al-Amri3

  • 1Center for Advanced Modeling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia.

Sensors (Basel, Switzerland)
|August 9, 2020
PubMed
Summary

This study introduces a deep learning model using convolutional neural networks (CNNs) for accurate earthquake probability mapping. The advanced model effectively processes seismic data, improving predictions for disaster management.

Keywords:
GISIndian subcontinentdeep learningearthquake probability

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Area of Science:

  • Earth Science
  • Geophysics
  • Machine Learning

Background:

  • Earthquake prediction is complex, requiring robust probability assessment due to inherent uncertainties.
  • Exponential growth in seismic data presents scalability challenges for traditional probability models.
  • Deep learning, while successful in other fields, has been underexplored for earthquake probability assessment.

Purpose of the Study:

  • To develop a scalable earthquake probability mapping model using deep learning techniques.
  • To leverage convolutional neural networks (CNNs) for enhanced seismic data analysis.
  • To improve the accuracy and efficiency of earthquake probability assessments.

Main Methods:

  • Utilized a convolutional neural network (CNN) for classification.
  • Input features included nine seismic indicators: proximity to faults, fault density, lithology, slope angle, elevation, magnitude density, epicenter density, distance from epicenter, and peak ground acceleration (PGA) density.
  • Trained and tested the model on country-level datasets for the Indian subcontinent.

Main Results:

  • Achieved high accuracy with overall accuracy (OA) of 96% (training) and 92% (testing).
  • Demonstrated strong performance for the earthquake class with precision (0.88), recall (0.99), and F1 score (0.93) on the testing dataset.
  • Identified significant areas of very-high (712,375 km²) and high (591,240.5 km²) earthquake probability.

Conclusions:

  • The proposed deep learning model significantly outperforms traditional methods in earthquake probability assessment accuracy.
  • The model provides a scalable solution for generating detailed earthquake probability maps.
  • Findings support urban planners and disaster managers in making informed decisions for earthquake risk mitigation and preparedness.