Research Developments and Prospects on Microseismic Source Location in Mines

Microseismic source location is the essential factor in microseismic monitoring technology, and its loca- tion precision has a large impact on the performance of the technique. Here, we discuss the problem of low-precision location identification for microseismic events in a mine, as may be obtained using conven-tional location methods that are based on arrival time. In this paper, microseismic location characteristics in mining are analyzed according to the characteristics of the mine＇s microseismic wavefield. We review research progress in mine-related microseismic source location methods in recent years, including the combination of the Geiger method with the linear method, combined microseismic event location method, optimization of relative location method, location method without pre-measured velocity, and location method without arrival time picking. The advantages and disadvantages of these methods are discussed, along with their feasible conditions. The influences of geophone distribution, first arrival time picking, and the velocity model on microseismic source location are analyzed, and measures are proposed to influence these factors. Approaches to solve the problem under study include adopting information fusion, combining and optimizing existing methods, and creating new methods to realize high-precision microseismic source location. Optimization of the velocity structure, along with applications of the time-reversal imaging technique, passive time-reversal mirror, and relative interferometric imag-ing, are expected to greatly improve microseismic location precision in mines. This paper also discusses the potential application of information fusion and deep learning methods in microseismic source location in mines. These new and innovative location methods for microseismic source location have extensive prospects for development.

Microseismic source location using deep learning:A coal mine case study in China

Microseismic source location is crucial for the early warning of rockburst risks.However,the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy.Intelligent techniques,such as the full convolutional neural network(FCNN),can capture spatial information but struggle with complex microseismic sequence.Combining the FCNN with the long shortterm memory(LSTM)network enables better time-series signal classification by integrating multiscale information and is therefore suitable for waveform location.The LSTM-FCNN model does not require extensive data preprocessing and it simplifies the microseismic source location through feature extraction.In this study,we utilized the LSTM-FCNN as a regression learning model to locate the seismic focus.Initially,the method of short-time-average/long-time-average(STA/LTA)arrival time picking was employed to augment spatiotemporal information.Subsequently,oversampling the on-site data was performed to address the issue of data imbalance,and finally,the performance of LSTM-FCNN was tested.Meanwhile,we compared the LSTM-FCNN model with previous deep-learning models.Our results demonstrated remarkable location capabilities with a mean absolute error(MAE)of only 7.16 m.The model can realize swift training and high accuracy,thereby significantly improving risk warning of rockbursts.

Failure mechanism and coupled static-dynamic loading theory in deep hard rock mining: A review

Rock failure phenomena,such as rockburst,slabbing(or spalling) and zonal disintegration,related to deep underground excavation of hard rocks are frequently reported and pose a great threat to deep mining.Currently,the explanation for these failure phenomena using existing dynamic or static rock mechanics theory is not straightforward.In this study,new theory and testing method for deep underground rock mass under coupled static-dynamic loading are introduced.Two types of coupled loading modes,i.e.'critical static stress + slight disturbance' and 'elastic static stress + impact disturbance',are proposed,and associated test devices are developed.Rockburst phenomena of hard rocks under coupled static-dynamic loading are successfully reproduced in the laboratory,and the rockburst mechanism and related criteria are demonstrated.The results of true triaxial unloading compression tests on granite and red sandstone indicate that the unloading can induce slabbing when the confining pressure exceeds a certain threshold,and the slabbing failure strength is lower than the shear failure strength according to the conventional Mohr-Column criterion.Numerical results indicate that the rock unloading failure response under different in situ stresses and unloading rates can be characterized by an equivalent strain energy density.In addition,we present a new microseismic source location method without premeasuring the sound wave velocity in rock mass,which can efficiently and accurately locate the rock failure in hard rock mines.Also,a new idea for deep hard rock mining using a non-explosive continuous mining method is briefly introduced.