Abstract
<p>In seismic exploration, random noise deteriorates the quality of acquired data. This study analyzed existing denoising methods used in seismic exploration from the perspective of random noise. Wavelet thresholding offers a new approach to reducing random noise in simulation results, synthetic data, and real data. A modified wavelet threshold function was developed by considering the merits and demerits of conventional soft and hard thresholding schemes. A MATLAB (matrix laboratory) simulation model was used to compare the signal-to-noise ratios (SNRs) and mean square errors (MSEs) of the soft, hard, and modified threshold functions. The results demonstrated that the modified threshold function can avoid the pseudo-Gibbs phenomenon and produce a higher SNR than the soft and hard threshold functions. A seismic convolution model was built using seismic wavelets to verify the effectiveness of different denoising methods. The model was used to demonstrate that the modified thresholding scheme can effectively reduce random noise in seismic data and retain the desired signal. The application of the proposed tool to a real raw seismogram recorded during a land seismic exploration experiment located in north China clearly demonstrated its efficiency for random noise attenuation.</p>
Highlights
Seismic exploration is the only way for petroleum companies to obtain firsthand geological data on oil and gas reservoirs
Seismic field data contain information about underground geological structures, but the data often cannot be directly used for geological interpretation because they are susceptible to interference from environmental noise
Because seismic signal processing is a critical task in seismic exploration, the processing techniques have a direct bearing on progress in the field of seismic exploration
Summary
Seismic exploration is the only way for petroleum companies to obtain firsthand geological data on oil and gas reservoirs. The soft threshold function is bound to cause a deviation in the estimate because it indiscriminately subtracts m from all wavelet coefficients whose absolute values are higher than the threshold This is reflected by the fact that the signal reconstructed from wavelet coefficients using the soft threshold function is too smooth, which reduces the effectiveness of denoising. For this reason, it is necessary to create a new threshold function that overcomes the drawbacks of the soft and hard threshold functions. As the wavelet coefficient’s absolute value diverges from m, the coefficient should approach the value estimated by the hard threshold function Under these conditions can the pseudo-Gibbs phenomenon be avoided for better denoising effectiveness.
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