Micro-seismic event detection and location in underground mines by using Convolutional Neural Networks (CNN) and deep learning

Recent years have witnessed a clear trend to develop deeper and longer tunnels to meet the growing needs of mining. Micro-seismic events location is vital for predicting and avoiding the traditional mine disasters induced by high stress concentration, such as rock burst, roof caving, water inrush and slope landslide. Deep learning has become a research hotspot within the field of artificial intelligence in recent years, which has achieved significant progresses and applications in the areas of image recognition, speech recognition, language processing and computer vision. The biggest difference between the deep learning and the traditional back propagation training method is that the deep learning can automatically and independently learn the characteristics of a large amount of data without human intervention. This paper uses Convolutional Neural Network (CNN) and deep learning techniques to develop a method for identifying the Time Delay of Arrival (TDOA) and subsequently the source location of micro-seismic events in underground mines. The power spectrum and phase spectrum of cross wavelet transform calculated from the recorded seismic waves due to micro-seismic events are used as inputs to CNN. The amplitude and phase information of the cross wavelet transform power spectrum are parameters that are used without manual manipulation to build the complex mapping to predict TDOA by deep learning network. Experimental data from the in-field blast tests and simulation tests show that the proposed approach can well identify TDOA and hence detect the event source locations of the field blasting tests. It is demonstrated that the proposed approach with the CNN and deep learning techniques gives more accurate micro seismic source identifications with the recorded noisy waveforms from in-situ blast tests, as compared to several typical existing methods.