Wavelet novel threshold function earthquake data denoising method

Abstract

The invention provides a wavelet novel threshold function earthquake data denoising method, and aims to solve the problems that the earthquake data quality in an oil gas seismic prospecting low signalto noise ratio region cannot reach processing and interpreting requirements; the method builds a novel threshold function so as to overcome the hard soft threshold disadvantages, uses the wavelet transformation as the core idea, and takes the earthquake data denoising as the target, thus determining the novel threshold function. Comparing the existing similar type methods, the denoising result ofthe method can more approximately reach the originality useful signals, thus largely improving the earthquake data quality.

Description

technical field [0001] The method of the invention belongs to the field of oil and gas seismic exploration, and relates to the design and application of seismic data denoising methods, and the method is effective for oil and natural gas seismic exploration in all regions. technical background [0002] The complex high and steep structural areas in piedmont rich in oil and gas resources have become the focus of current seismic exploration. With the refinement of exploration, noise interference is often very serious in areas with complex structures and low signal-to-noise ratio. If pre-stack records Without denoising processing, it will lead to unclear horizons or unreal structures on the final stacked section, making the quality of seismic data not meet the processing requirements, thus restricting the further improvement of seismic exploration in this area. [0003] The purpose of denoising seismic data is to suppress the noise on the original record as much as possible, but...

AI Technical Summary

Problems solved by technology

However, these two threshold functions still have defects: when using hard threshold denoising, because the function is discontinuous, the reconstructed signal will oscillate, and many artificial noise points will be generated at the edge of the image

However, when this method reduces the wavelet coefficient with a large absolute value, there is always a constant deviation, which directly affects the approximation between the reconstructed signal and the real signal, resulting in a certain loss of high-frequency information, which will lead to blurred edges of the image

In practical applications, using soft threshold to denoise the signal is relatively smooth, but there is a large signal distortion, and using hard threshold to denoise the effect is not ideal, and the denoising effect on time-varying signals is limited

Images