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：近期对自然资源的开采及相关的废水地下注入活动，在美国中部构造平静地区引发了许多中小型地震。地震活动的增加使地震数据记录呈指数级增长，这要求开发高效算法，以便从大量含噪数据中可靠地检测地震事件。当前多数地震检测方法主要针对中大型地震设计，因此往往遗漏许多被地震噪声掩盖的低震级地震。Perol等人（2018）专注于利用深度学习方法解决地震检测问题：他们提出了一种卷积神经网络（ConvNetQuake），用于从地震记录中检测和定位地震事件。本报告旨在复现作者提出的部分方法，即实现一个用于从地震记录中分类事件（地震vs.噪声）的卷积神经网络。