Machine learning (ML) is a collection of methods used to develop understanding and predictive capability by learning relationships embedded in data. ML methods are becoming the dominant approaches for many tasks in seismology. ML and data mining techniques can significantly improve our capability for seismic data processing. In this review we provide a comprehensive overview of ML applications in earthquake seismology, discuss progress and challenges, and offer suggestions for future work. ▪ Conceptual, algorithmic, and computational advances have enabled rapid progress in the development of machine learning approaches to earthquake seismology. ▪ The impact of that progress is most clearly evident in earthquake monitoring and is leading to a new generation of much more comprehensive earthquake catalogs. ▪ Application of unsupervised approaches for exploratory analysis of these high-dimensional catalogs may reveal new understanding of seismicity. ▪ Machine learning methods are proving to be effective across a broad range of other seismological tasks, but systematic benchmarking through open source frameworks and benchmark data sets are important to ensure continuing progress.

中文翻译：

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地震学中的机器学习


机器学习 (ML) 是通过学习数据中嵌入的关系来开发理解和预测能力的方法的集合。机器学习方法正在成为地震学许多任务的主要方法。机器学习和数据挖掘技术可以显着提高我们的地震数据处理能力。在这篇综述中，我们全面概述了机器学习在地震学中的应用，讨论了进展和挑战，并为未来的工作提供了建议。 ▪ 概念、算法和计算的进步使得地震学机器学习方法的发展取得了快速进展。 ▪ 这一进展的影响在地震监测中最为明显，并正在催生新一代更加全面的地震目录。 ▪ 应用无监督方法对这些高维目录进行探索性分析可能会揭示对地震活动性的新认识。 ▪ 事实证明，机器学习方法在广泛的其他地震学任务中是有效的，但通过开源框架和基准数据集进行系统基准测试对于确保持续进展非常重要。