Micro-seismic signal identification method based on quasi-optimal Gaussian kernel multi-classification support vector machine

Abstract

The invention discloses a micro-seismic signal identification method based on a quasi-optimal Gaussian kernel multi-classification support vector machine, and belongs to the field of machine learningand data mining. The method comprises the following steps: firstly, dividing micro-seismic data according to channels, and performing data format conversion; secondly, performing feature extraction oneach piece of channel data according to a mean value and a variance, combining all channels of the same sample to form a new feature, and performing feature selection on the synthesized data by utilizing an optimal Gaussian kernel-like multi-classification support vector machine to generate a dimensionality-reduced unbalanced training sample set; thirdly, determining an under-sampling rate according to the non-equilibrium rate of the training sample, and carrying out under-sampling on the large class of samples; and finally, a multi-classification support vector machine is adopted to construct a microseism signal classifier after dimension reduction. According to the method, the influence of redundant features on classification can be effectively reduced; double dimensionality reduction is carried out on the channel characteristics and the combined characteristics, so that the microseismic signal dimensionality is effectively reduced, the accuracy and timeliness of a microseismic signal classifier are improved, and the accuracy of rock burst disaster early warning is improved.

Description

technical field [0001] The invention relates to a microseismic signal recognition method, in particular to a microseismic signal recognition method based on a class-optimal Gaussian kernel multi-classification support vector machine, which belongs to the field of machine learning and data mining. Background technique [0002] Rock burst is a typical coal mine dynamic disaster, which seriously threatens the efficient production and personnel safety of coal mines. Therefore, the early warning of rock burst disaster is very important. Rockburst refers to the phenomenon that under the action of high stress, the accumulated energy is released suddenly, causing impact on coal and rock mass, resulting in casualties and damage to buildings. Microseismic is a microseismic event induced by mining activities. It is a phenomenon in which the accumulated elastic strain energy of the coal-rock medium is suddenly released under the stress of the mining area, causing the rock mass around th...

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Problems solved by technology

[0006] Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a microseismic signal recognition method based on the class-optimal Gaussian kernel multi-classification support vector machine, which solves the problem of excessive data volume in the classification and recognition of microseismic signals. Redundant data features, low signal-to-noise ratio and unbalanced samples; realize fast and accurate identification of microseismic signal data, save computing resources, and improve response speed

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