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An AI-Assisted Real-Time Earthquake Forecasting Case Study in China

Earthquake forecasting aims to save human lives and reduce economic loss by providing early alerts before the occurrence of large destructive earthquakes. It was long considered challenging due to the various uncertain factors, including data processing artifacts, unknown physical mechanisms, geological complexity, anthropogenic interventions, etc. With the advent of artificial intelligence (AI) and gigantic datasets from multiple sources, earthquake forecasting has become more hopeful. In this study, we trained an earthquake forecasting model using a classic random forest algorithm and a large-scale dataset from West China, where earthquake activities are prevalent. We obtained encouraging real-time testing results on an independent dataset from the same area. The training data comprises the geo-acoustic (GA) and electromagnetic (EM) data of more than 120 M>3.5 earthquake events recorded by 150 stations from 10/01/2016 to 12/31/2020. Instead of using the continuous waveforms directly, we extract physically meaningful features to lower the freedom and uncertainty involved in the training process. The real-time forecasting performance reaches above 70% accuracy with a distance error close to 200 miles and a magnitude error below 0.5 Ml. This research sheds light on more widely tackling the enigmatic earthquake forecasting problems using AI-assisted data-harnessing technologies.

Session: Towards Advancing Earthquake Forecasting and Nowcasting: Recent Progress Using Ai-Enhanced Methods - I

Type: Oral

Room: Tubughnenq’ 3

Date: 5/1/2024

Presentation Time: 02:30 PM (local time)

Presenting Author: Yangkang Chen

Student Presenter: No

Additional Authors