DeepDetect: A Cascaded Region-Based Densely Connected Network for Seismic Event Detection

Automatic event detection from time series signals has broad applications. Traditional detection methods detect events primarily by the use of similarity and correlation in data. Those methods can be inefficient and yield low accuracy. In recent years, machine learning techniques have revolutionized many sciences and engineering domains. In particular, the performance of object detection in a 2-D image data has significantly improved due to deep neural networks. In this paper, we develop a deep-learning-based detection method, called "DeepDetect," to detect events from seismic signals. We find that the direct adaptation of similar ideas from 2-D object detection to our problem faces two challenges. The first challenge is that the duration of earthquake event varies significantly; the other is that the proposals generated are temporally correlated. To address these challenges, we propose a novel cascaded region-based convolutional neural network to capture earthquake events in different sizes while incorporating contextual information to enrich features for each proposal. To achieve a better generalization performance, we use densely connected blocks as the backbone of our network. Because some positive events are not correctly annotated, we further formulate the detection problem as a learning-from-noise problem. To verify the performance, we employ the seismic data generated from the Pennsylvania State University Rock and Sediment Mechanics Laboratory, and we acquire labels with the help of experts. We show that our techniques yield high accuracy. Therefore, our novel deep-learning-based detection methods can potentially be powerful tools for identifying events from the time series data in various applications.