The recent exploitation of natural resources and associated waste water injection in the subsurface have induced many small and moderate earthquakes in the tectonically quiet Central United States. This increase in seismic activity has produced an exponential growth of seismic data recording, which brings the necessity for efficient algorithms to reliably detect earthquakes among this large amount of noisy data. Most current earthquake detection methods are designed for moderate and large events and, consequently, they tend to miss many of the low-magnitude earthquake that are masked by the seismic noise. Perol et. al (2018) has focused on the problem of earthquake detection by using a deep-learning approach: the authors proposed a convolutional neural network (ConvNetQuake) to detect and locate earthquake events from seismic records. This reports aims at reproducing part of the methodology proposed by the author, which is the implementation of a convolutional neural network for classification of events (i.e., earthquake vs. noise) from seismic records.
翻译:近期,在地质上平静的美国中部地区,由于对自然资源的开发和伴随而来的注水通量,引发了许多小规模和中等规模的地震。这种地震活动的增加使得地震数据记录出现了指数式增长,这就需要有效的算法来可靠地检测噪声数据中的地震。目前大部分地震检测方法是为中等和较大规模的事件设计的,因此往往会错过很多被地震噪声掩盖的低震级的地震事件。Perol等人(2018)通过使用深度学习方法,提出了一种卷积神经网络(ConvNetQuake)来检测并定位地震事件。这篇报告旨在复现作者提出的方法中的一部分,即使用卷积神经网络从地震记录中进行事件分类(即地震 vs. 噪声)的实现。