两种反褶积方法处理效果对比
Comparison of the Processing Effects of Two Deconvolution Methods

近几年，伴随着勘探采集工作由浅入深，地质条件变得越来越复杂，地震波反射被减弱、反射波同相轴不连续，地震响应弱，能量也衰减损耗大，导致获取到的地震数据分辨率很低。对于地球物理勘探采集得到的地震数据，提高其分辨率是地震信号处理过程中的关键一步。反褶积处理是提高地震数据时间分辨率最有效的技术方法之一。该方法通过压缩地震子波使得相互干涉地层反射系数分离开，因而更好地识别地层，了解地层信息。传统的反褶积方法主要包括最小二乘反褶积方法和稀疏脉冲反褶积方法。最小二乘反褶积是一种线性反褶积方法。但是地震数据并不都是线性信号，对于非线性信号，最小二乘反褶积方法难以处理。而稀疏脉冲反褶积是一种非线性反褶积方法，将压缩子波的传统反褶积核心转换到反射系数和幅值上面来，拓宽了数据处理思路。稀疏脉冲反褶积加入L1范数稀疏约束项，将反褶积问题转换成非线性目标泛函，从而更好地研究其求解的优化算法。本文首先介绍了反褶积、维纳滤波、最小平方反褶积、脉冲反褶积和稀疏脉冲反褶积，并模拟了反射系数序列，利用最小相位子波合成地震记录，最后用脉冲反褶积与稀疏脉冲反褶积作用于地震数据来观察反褶积方法的作用效果。考查两种反褶积方法的作用效果，挑选出某些道出来观察作用效果，利用均方误差来考察反褶积的效果。
In recent years, with the exploration and acquisition work progressing from shallow to deep, geological conditions have become increasingly complex. Seismic wave reflections have been weakened, the reflection wave phase axis is discontinuous, the seismic response is weak, and the energy attenuation loss is large, resulting in low resolution of obtained seismic data. Improving the resolution of seismic data obtained from geophysical exploration is a crucial step in the seismic signal processing process. Deconvolution processing is one of the most effective techniques for improving the temporal resolution of seismic data. This method compresses seismic wavelets to separate the reflection coefficients of interfering formations, thus better identifying formations and understanding formation information. The traditional deconvolution methods mainly include the least squares deconvolution method and the sparse pulse deconvolution method. Least squares deconvolution is a linear deconvolution method. However, not all earthquake data are nonlinear signals, and for nonlinear signals, the least squares deconvolution method is difficult to process. Sparse pulse deconvolution is a nonlinear deconvolution method that transforms the traditional deconvolution core of compressed wavelets into reflection coefficients and amplitudes, broadening the data processing approach. Sparse pulse deconvolution incorporates L1 norm sparse constraint term to transform the deconvolution problem into a nonlinear objective functional, thereby better studying the optimization algorithm for its solution. This article first introduces deconvolution, Wiener filtering, least squares deconvolution, pulse deconvolution, and sparse pulse deconvolution, and simulates the reflection coefficient sequence. It synthesizes seismic records using the minimum phase wavelet, and finally applies pulse deconvolution and sparse pulse