A High Resolution Multi-wave Matching Method Based on Singular Value Decomposition

Using multi-wave and multi-component seismic data for reservoir prediction, fracture detection and fluid identification has unique advantages. However, before the interpretation of joint seismic data of horizontal and horizontal waves, it is necessary to first solve the matching problem of horizontal and horizontal waves. This paper introduces a high precision multi-wave matching method based on singular value decomposition. By singular value decomposition (SVD), the method constructs the similarity function between longitudinal wave (PP) and converted wave (PS) maximization and imaging profile as the matching objective function to compensate the difference between reflection coefficients of longitudinal wave and converted wave, and matches through global optimization. This method overcomes the disadvantages caused by amplitude, phase, waveform, velocity and other big differences between longitudinal wave and shear wave when PP wave and PS wave are matched in time domain. An example test shows that the matching accuracy of this method is greatly improved, and it can provide high quality seismic data for joint inversion of vertical and horizontal waves and extraction of joint attributes of multiple waves, and good results are obtained.