Aftershock Detection with Multiscale Description Based Neural Network Aftershocks refer to the smaller earthquakes that occur following large earthquakes, in the same area of the main shock. The task of aftershocks detection, as a crucial and challenging issue in disaster monitoring, has attracted wide research attention in relevant fields. Compared with the traditional detection methods like STALTA algorithms or heuristic matching, neural network techniques are regarded as an advanced choice with better pattern recognition ability. However, current neural networkbased solutions mainly formulate the seismic wave as ordinary time series, where existing techniques are directly deployed without adaption, and thus fail to obtain competitive performance on the intensive and highlynoise waveforms of aftershocks. To that end, in this paper, we propose a novel framework named MultiScale Description based Neural Network (MSDNN) for enhancing aftershock detection. Specifically, MSDNN contains a delicatelydesigned network structure for capturing both shortterm scale and longterm scale seismic features. Therefore, the unique characteristics of seismic waveforms can be fullyexploited for aftershock detection. Furthermore, a multitask learning strategy is introduced to model the seismic waveforms of multiple monitoring stations simultaneously, which can not only refine the detection performance but also provide additionally quantitative clues for discovering homologous earthquakes. Finally, comprehensive experiments on the data set from aftershocks of the Wenchuan M8.0 Earthquake have clearly validated the effectiveness of our framework compared with several stateoftheart baselines.

Identifying microseismic events in a mining scenario using a convolutional neural network Abstract   Microseismic monitoring in mining can potentially forecast catastrophic disasters, and help in the optimization of daytoday operational and safety issues. However, it is difficult to fully utilize microseismic data due to the large quantity of small microseismic events and because automatically detecting events is frustrated by the pollution of the microseismic signal by instrument noise and mining activities. Recent research has demonstrated that convolutional neural networks (CNNs) trained on vast seismic data sets can accurately detect seismic events. We train a CNN on handlabeled, modestly sized, multichannel microseismic data from a coal mine. We make the labeled data available as part of this paper. Using a    k   fold cross validation approach, we demonstrate that the CNN surpasses the accuracy of a human microseismic expert, both in picking more true events and in eliminating more spurious (false) events. This demonstrates the feasibility of including a CNN in an automated system to detect, classify and locate microseismic events, for use in mine safety and operation.