标题： EPBench：基于神经网络的短临地震预测基准平台 显示英文标题

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

摘要： 自本世纪初以来，人工智能和神经网络的重大进展为短期地震预测研究带来了新的变革潜力。然而，目前尚无广泛使用的相关基准。为了解决这一问题，我们构建了一个新的基准（EPBench），据我们所知，这是首个全球区域规模的短期地震预测基准。我们的基准包含了从全球各地地震监测网络收集的 924,472 条地震记录以及 2959 条多模态地震记录。每条记录包括时间、经度、纬度、震级等基本信息，而每条多模态记录还额外包括波形和矩张量信息，覆盖的时间范围从 1970 年到 2021 年。为了评估模型在这个任务上的性能，我们建立了一系列专门针对短期地震预测任务的数据划分和评估方法。我们还提供了多种工具，帮助未来的研究人员根据他们的地理理解对数据进行划分。我们的基准包含了广泛用于时间序列预测的各种神经网络模型，以及一些国家地震局当前使用的基于统计的方法。我们希望这个基准能够作为指南，吸引更多研究人员探索解决该任务的新方法，这对人类生存具有重要意义。 显示英文摘要

摘要： 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.