Micro seismic data denoising method based on wavelet transformation

Abstract

The present invention provides a micro seismic data denoising method based on wavelet transformation. The method comprises the concrete steps of (1) carrying out the wavelet decomposition on the micro seismic data to obtain the finest scale of high frequency and low frequency wavelet coefficients; (2) ranking each scale of high frequency and low frequency wavelet coefficients again from large to small; (3) estimating the noise variance of the high frequency and low frequency wavelet coefficients at different scales, and calculating the mid-value and the minimum value in the new sequence; (4) calculating the threshold value of each scale of high frequency and low frequency wavelet coefficients according to constraint factors; (5) retaining various scales of coefficients greater than the absolute values of the threshold values, and calculating the new threshold values according to the step (4) for the coefficients less than the absolute values of the threshold values; (6) repeating the steps (2) to (5), and saving various scales of new high frequency and low frequency wavelet coefficients after processing; (7) obtaining the micro seismic data after denoising by the wavelet inverse transformation. The micro seismic data denoising method based on wavelet transformation of the present invention solves the problem that the micro seismic data is high in signal to noise ratio, and has the waveform distortion after denoising.

Description

technical field [0001] The invention relates to a microseismic monitoring data processing technology, in particular to a wavelet transform-based microseismic data noise reduction method. Background technique [0002] Microseismic monitoring technology is currently mainly used in natural earthquake prediction, conventional and unconventional oil and gas hydraulic fracturing fracture monitoring, coal mine safety monitoring, bridge / dam deformation, geological disaster prevention and other fields. At present, by processing and interpreting the microseismic records generated by fracturing of shale gas reservoirs, the trend, spatial distribution and geometric characteristics (fracture length, width and height) of fractures can be determined, and the complexity of fractures can be judged. The three-dimensional description of fault structure characteristics provides data on changes in underground stress field, formation deformation and subsidence in the upper part of the reservoir, ...

AI Technical Summary

Problems solved by technology

[0003] However, due to the weak energy of microseismic events (Level -3 to +1 on the Richter scale) and wide frequency range (100 to 1500 Hz), they are easily affected by noise from complex sources. Weak signal extraction and noise suppression have always been difficult points in microseismic data processing.

At present, most microseismic processing commercial software generally use FIR filter or IIR filter to denoise microseismic data. Due to the influence of filter structure, the filtered microseismic data is easy to cause phase distortion.

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