Seismic Events Prediction Using Deep Temporal Convolution Networks

Yue Geng,Ce Han,Lingling Su,Yunhong Jia

doi:10.1155/2019/7343784

Abstract

Seismic events prediction is a crucial task for preventing coal mine rock burst hazards. Currently, this task attracts increasing research enthusiasms from many mining experts. Considering the temporal characteristics of monitoring data, seismic events prediction can be abstracted as a time series prediction task. This paper contributes to address the problem of long-term historical dependence on seismic time series prediction with deep temporal convolution neural networks (CNN). We propose a dilated causal temporal convolution network (DCTCNN) and a CNN long short-term memory hybrid model (CNN-LSTM) to forecast seismic events. In particular, DCTCNN is designed with dilated CNN kernels, causal strategy, and residual connections; CNN-LSTM is established in a hybrid modeling way by utilizing advantage of CNN and LSTM. Based on these manners, both of DCTCNN and CNN-LSTM can extract long-term historical features from the monitoring seismic data. The proposed models are experimentally tested on two real-life coal mine seismic datasets. Furthermore, they are also compared with one traditional time series prediction method, two classic machine learning algorithms, and two standard deep learning networks. Results show that DCTCNN and CNN-LSTM are superior than the other five algorithms, and they successfully complete the seismic prediction task.

Highlights

Underground coal mines are different from general permanent tunnel engineering; like subways, their stopes are constantly moving with the mining activities ongoing
Seismic events prediction can directly reflect the safety conditions of underground coal mine, and it helps preventing rock burst accidents and hazards effectively. erefore, seismic forecasting is a crucial guarantee for coal mine safety production
Results and Discussion e proposed networks were compared with the abovementioned ve methods. eir performance on underground coal mine seismic events prediction is shown in Tables 2 and 3

Summary

Introduction

Underground coal mines are different from general permanent tunnel engineering; like subways, their stopes are constantly moving with the mining activities ongoing. The force equilibrium status of coal is destroyed and the internal stress of coal and rock is redistributed. Due to the above situation, rock burst disasters occur frequently. Seismic events prediction can directly reflect the safety conditions of underground coal mine, and it helps preventing rock burst accidents and hazards effectively. Erefore, seismic forecasting is a crucial guarantee for coal mine safety production. Conventional predictors are mostly based on classic geomechanics, applying unified indexes to evaluate all coal mine roofs [1, 2]. The mechanism of roof disaster has not been thoroughly studied; it is difficult to accurately establish geomechanical models for simulating the occurrence processes of roof disasters

Objectives

Methods

Results

Conclusion