Microseismic Weak Signal Detection Method Research

Surface microseismic monitoring technology,compared with wellbore microseismic monitoring,will be widely technology in the field of micro-seismic fracturing monitoring, its application prospects will be great. Due to surface microseismic source and the particularity of receiving mode, causing the surface microseismic data with low signal noise ratio (SNR), and so on the surface of microseismic data processing, improve the data signal to noise ratio, detection of effective signal, on the surface of microseismic monitoring is of great significance, for the next step in the inversion positioning to provide accurate first arrival.The direct wave is the effective signal in surface microseismic data.We should analyse the characteristics of effective signal’s waveform, spectrum and energy in time-space domain and frequency domain.We aslo discuss and analyse the types and reasons of the noise on the basis of the actual surface microseismic data. And the key of the surface microseismic signal detection is how to deal with the kind of irregular the random noise with wide distribution, strong energy, and dealing with noise as far as possible don’t damage the effective signals is another key. According to the characteristics of the surface microseismic data, we do the pre-processing and get rid of some regular noise, such as single frequency disturbance and strong energy, to some extent improve the signal-to-noise ratio of data.Combining with the actual surface microseismic signal, the stochastic resonance, multi-channel subsection cross-correlation filter method, kalman filtering method, independent component analysis (ICA) method is used in the detection of weak signals. Stochastic resonance is mainly based on to join a certain amount of noise in effective signal, stochastic resonance phenomenon will appear in large amplitude of the signal for weak signal detection. Multichannel subsection cross-correlation algorithm is based on the principle of signal and noise of the cross-correlation is zero, choosing the appropriate related step length and trace number. Kalman filtering algorithm is putting forward a wider and more flexible the state variables and the state space than the signal, according to the microseismic data to get initial state noise and observation noise forecast of kalman filter, improves the signal-to-noise ratio of data. The basic idea of ICA algorithm is taking random noise as the source signal, take the records of the adjacent two tracs as mixed signal, then use the ICA algorithm, signal and noise then will be separated.We mainly studied the surface microseismic data processing methods based on Curvelet transform. After Curvelet transform the signal and noise’s Curvelet coefficient is different, by adopting the method of threshold and the thresholding function, keep the Curvelet coefficient greater than the threshold value, set to zero less than the threshold value, to retain effective signal and to achieve the purpose of weak signal detection. By setting up different suface microseismic models, and do the different actual data processing, it shows that the method is applicable.Compared with the results of wavelet transform and Curvelet transform conventional threshold function, the three methods all have certain effect,but the improved threshold function compensation Curvelet transform’s effect is the best, while removing a lot of noise as far as possible don’t damage the effective signals, finally improves the signal-to-noise ratio of microseismic data, achieve the purpose of the micro seismic weak signal detection.