发表机构
Université Paris Cité, LIPADE; Institut de Physique du Globe de Paris; CEA（巴黎西岱大学，LIPADE; 巴黎地球物理研究所; 法国原子能和替代能源委员会）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大规模地震数据集中模板匹配耗时严重的问题，提出分布式处理框架TREMOR，在保持结果精确的前提下，比最优方法快最多12倍。
AI 中文摘要
地震台网以波形数据序列的形式连续记录地球多个位置的地面速度，地震学家通过分析这些数据来检测包括地震在内的各种地球物理事件。其中一些事件特别受关注：它们被称为模板，用于在地震数据集中搜索匹配的相似事件。这被称为模板匹配，是地震学中的一项基本任务，也是各种地震分析的基础。然而，模板匹配需要大量的处理时间，尤其是当数据集超过单台机器的内存容量时。这给地震学家带来了重大挑战，并且随着地震数据集规模的持续增长，这一问题日益严重。在本文中，我们提出了TREMOR，一个用于模板匹配的分布式数据序列处理框架，旨在高效处理大规模波形数据集。我们将TREMOR应用于两个具有代表性的真实世界地震模板匹配用例，并通过广泛的实验评估证明了其效率，TREMOR比最佳对比方法快最多12倍，同时返回相同且精确的结果。
英文摘要
Seismic station networks continuously record the ground velocity at several locations on earth in the form of waveform data series, which seismologists analyze to detect various kinds of geophysical events, including earthquakes. Some of these events are of particular interest: they are called templates and are used to search the seismic data collections for matching, similar events. This is known as template matching, and is a fundamental task in seismology, serving as the backbone for various seismic analyses. However, template matching requires extensive processing times, especially for seismic collections that exceed the memory capacity of a single machine. This poses a significant challenge to seismologists and is becoming worse as the seismological datasets continue to grow in size. In this paper, we introduce TREMOR, a distributed data series processing framework for template matching, designed to efficiently handle large waveform collections. We apply TREMOR to two representative real-world seismic use cases for template matching and, through an extensive experimental evaluation, we demonstrate its efficiency, with TREMOR being up to 12x faster than the best competing method, while returning the same, exact results.
