Method for online early warning of coal mine rock burst disaster based on characteristic drifting

Abstract

The invention discloses a method for online early warning of a coal mine rock burst disaster based on characteristic drifting, and belongs to the technical field of information processing. The method of the invention includes the steps of processing the microseismic data flow in a time window using non-linear signal analysis theory, and extracting 8 pieces of time-frequency domain data from each microseismic event to form eigenvectors so as to more accurately describe drifting patterns of the microseismic data before and after a rock burst disaster; and learning and training sample data by introducing a least squares support vector machine (LS-SVM) to obtain a LS-SVM classifier, classifying the microseismic monitoring data flow using the LS-SVM classifier, further finding a precursor of the rock burst catastrophe and performing online early warning. The method is more robust than a conventional threshold early warning method.

Description

technical field [0001] The invention belongs to the technical field of information processing, and in particular relates to an online early warning method for coal mine rock burst disasters based on feature drift. Background technique [0002] Rock burst is a typical coal-rock dynamic disaster, which has the characteristics of long disaster-pregnant process, sudden occurrence, and strong destructive power, and poses a huge threat to coal mine safety production. At present, microseismic monitoring has become one of the important means of monitoring and early warning of rock burst in coal mines. Research in this area has been carried out for many years at home and abroad, and fruitful results have been achieved. However, due to the complexity of the mechanism of rock burst, the existing Most of the microseismic monitoring and early warning methods are based on the static statistics of the number and energy of microseismic events, and the monitoring and early warning is carried...

AI Technical Summary

Problems solved by technology

At present, microseismic monitoring has become one of the important means of monitoring and early warning of rock burst in coal mines. Research in this area has been carried out for many years at home and abroad, and fruitful results have been achieved. However, due to the complexity of the mechanism of rock burst, the existing Most of the microseismic monitoring and early warning methods are based on the static statistics of the number and energy of microseismic events, and the monitoring and early warning is carried out by setting the threshold, and the threshold setting needs to comprehensively consider various factors, and sometimes even need to continuously adjust relevant parameters, which greatly limits the microseismic monitoring and early warning. The popularization and application of the system also greatly reduces the accuracy of the early warning

In other words, these early warning methods do not fully consider the process characteristics of rock burst disaster formation, especially the drift characteristics of microseismic monitoring data streams, which makes their popularization and application difficult and the early warning accuracy is low

[0003] In the process of microseismic monitoring, a large amount of microseismic monitoring data is generated. These microseismic monitoring data exist in the form of data streams and are constantly expanding. However, the use of these data is currently only at the level of microseismic event counting and energy calculation. There is no in-depth analysis and research on the characteristics and laws contained in the data flow, and there is an embarrassing situation where the data is rich but the available information and knowledge are poor.

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