EPBench: A Benchmark for Short-term Earthquake Prediction with Neural Networks

TL;DR

EPBench is the first comprehensive global benchmark for short-term earthquake prediction using neural networks, providing extensive data, evaluation tools, and models to advance research in this critical area.

Contribution

This paper introduces EPBench, a novel benchmark dataset and evaluation framework for short-term earthquake prediction with neural networks, filling a major gap in standardized testing.

Findings

Benchmark includes 924,472 earthquake records and 2959 multimodal records.

Provides data partitions and evaluation methods tailored for the task.

Includes various neural network and statistical models for comparison.

Abstract

Since the beginning of this century, the significant advancements in artificial intelligence and neural networks have offered the potential to bring new transformations to short-term earthquake prediction research. However, currently, there is no widely used benchmark for this task. To address this, we have built a new benchmark (EPBench), which is, to our knowledge, the first global regional-scale short-term earthquake prediction benchmark. Our benchmark comprises 924,472 earthquake records and 2959 multimodal earthquake records collected from seismic networks around the world. Each record includes basic information such as time, longitude and latitude, magnitude, while each multimodal record includes waveform and moment tensor information additionally, covering a time span from 1970 to 2021. To evaluate performance of models on this task, we have established a series of data partitions and evaluation methods tailored to the short-term earthquake prediction task. We also provide a variety of tools to assist future researchers in partitioning the data according to their geographical understanding. Our benchmark includes a variety of neural network models widely used for time series forecasting, as well as a statistical-based model currently employed by seismological bureaus in several countries. We hope this benchmark will serve as a guide to attract more researchers to explore new methods for addressing this task, which holds great significance for human existence. Code is available at https://github.com/CoderZY-X/EPBench