发表机构
GFZ Helmholtz Centre for Geosciences; Institute for Geological Sciences, Freie Universität Berlin（亥姆霍兹地学研究中心（GFZ）; 柏林自由大学地质科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对DAS数据处理中缺乏标准化导致模型可比性和互操作性差的问题，提出SeisBench DAS框架，定义标准数据格式和模型接口，基于xdas和PyTorch构建，高效应用深度学习模型，弥合开发者与实践者鸿沟。
AI 中文摘要
光纤传感技术，如分布式声学传感（DAS），已成为地球物理研究中广泛应用的技术。为了处理DAS产生的大规模数据集，研究者已提出了多种机器学习方法。然而，由于缺乏数据和模型的标准化，这些方法在可比性和互操作性方面存在不足。这导致了模型开发者与分析DAS数据的实践者之间的鸿沟，并阻碍了深度学习在DAS领域的应用。为解决这些限制，我们在此提出SeisBench DAS，这是面向地震学机器学习的SeisBench库的一个扩展。SeisBench DAS为DAS基准数据集定义了标准格式，包括标准化的元数据和标签，以及DAS模型。它基于xdas框架进行数据摄取和虚拟阵列处理，并利用PyTorch读取和应用机器学习模型。重要的是，SeisBench提供了一个引擎，能够高效地将深度学习模型应用于各种格式的DAS数据，从而弥合了模型开发者与实践者之间的差距。SeisBench DAS被设计为一个开放且可扩展的框架，便于纳入未来深度学习在DAS领域的发展成果。
英文摘要
Fibre optic sensing, such as distributed acoustic sensing (DAS), has become a widespread technology for geophysical studies. To process the large-scale datasets produced by DAS, several machine learning methods have been proposed. However, without standardization of data and models, these methods lack comparability and interoperability. This introduces a gap between model developers and practitioners analyzing DAS data and inhibits adoption of deep learning for DAS. To address these limitations, here we present SeisBench DAS, an extension to the SeisBench library for machine learning in seismology. SeisBench DAS defines standard formats for DAS benchmark datasets, including standardised metadata and labels, and DAS models. It builds on the xdas framework for data ingestion and virtual array handling, and on PyTorch for reading and applying the machine learning models. Importantly, SeisBench provides an engine to efficiently apply deep learning models to diverse formats of DAS data, bridging the gap between model developers and practitioners. SeisBench DAS is designed as an open and extensible framework, allowing to easily incorporate future developments in deep learning for DAS.
Comments14 pages, 5 figures
