A framework for the prediction of earthquake using federated learning

Area of Science:

• Geophysics and Artificial Intelligence
• Machine Learning for Natural Disaster Prediction

Background:

• Earthquakes pose significant risks to life and infrastructure.
• Existing AI-based earthquake prediction methods struggle with data challenges like size, privacy, and transmission latency.
• Federated learning (FL) offers a decentralized approach to process data locally, preserving privacy and security.

Purpose of the Study:

• To propose a novel earthquake prediction framework utilizing federated learning (FL).
• To enhance the efficiency, reliability, and precision of earthquake prediction models.
• To address data privacy and heterogeneity issues inherent in large-scale seismic datasets.

Main Methods:

• Developed a federated learning framework incorporating the FedQuake algorithm.
• Analyzed three distinct local seismic datasets to create local machine learning models.
• Aggregated local models on a central FL server to form a global data model.
• Trained a meta-classifier on the global model for refined earthquake predictions.
• Tested the framework using seismic data from the Western Himalayas region.

Main Results:

• The proposed FL framework demonstrated superior performance compared to traditional ML models.
• Achieved an 88.87% prediction accuracy when validated against 35 years of regional seismic data.
• The framework effectively handled multidimensional seismic data within a 100 km radius.

Conclusions:

• The novel federated learning framework offers a promising solution for accurate and private earthquake prediction.
• The high prediction accuracy suggests its potential as a key component in future earthquake early warning systems.
• FL's ability to manage heterogeneous data and ensure privacy is crucial for advancing seismic research.