Research On Compressed Sensing Method Of Microseismic Data

Microseismic signal refers to the elastic wave or stress wave released by the accumulation of internal energy under the action of external stress.Microseismic signal is a typical non-stationary random signal with characteristics of quick mutation and short duration.Microseismic signal contains the state information of rock in coal mine,so real-time and efficient transmission and detection of microseismic signal is of great significance to mine safety.When to collect signal samples,the traditional sampling method to follow Nyquist Nyquist sampling theorem,but with the development of the society,people need is gradually increasing the amount of information,the frequency of the message carried by these signals is becoming more and more high,the required sampling rate and processing speed is also constantly improve,this leads to the traditional sampling method on the bandwidth of signal processing more difficult.Moreover,the traditional way of collecting microseismic signals is sampling first,then compression,and then transmission,which reduces the real time of the legend of microseismic signals.In recent years,compressive sensing theory,which has attracted much attention,has solved this problem well.Compressive sensing does not accept the limitation of sampling frequency.It can use a small number of linear observations to represent the signal with sparse nature,and then use nonlinear method to reconstruct the original signal.In addition,compressive sensing processes signals by sampling and compression,which greatly improves the real-time performance of microseismic signal transmission.In this paper,based on the detailed introduction of the compressed sensing algorithm theory,based on the traditional compressed sensing algorithm,an adaptive microseismic data compressed sensing method based on dictionary learning is proposed.The microseismic signal is processed by adaptive sampling strategy and adaptive redundant dictionary.Firstly,the time-frequency transform method is used to analyze the microseismic signal in the frequency domain,and the energy characteristics of the microseismic signal are obtained.We further combine the energy index of the microseismic signal with the sparsity index to propose an adaptive sampling strategy.Then,fractal box dimension indexes of microseismic signals were obtained through FracLab2.2 toolbox,and self-similarity characteristics of microseismic signals were obtained.Then,microseismic signal segments were taken as training samples to construct self-adaptive redundant dictionaries.An adaptive compression sensing algorithm based on dictionary learning is used to compress microseismic signals.The validity of this algorithm in microseismic compression observation is verified by analyzing experimental results.