基于ARMA模型的剩余子波反褶积方法

ARMA MODEL-BASED DECONVOLUTION

Dcconvoluton is one of the main tools in enhancing resolution for seismic records. The factors that affect the dcconvolution performance arc: 1) whether the wavelet model is appropriate, 2) whether the wavelet's power spectrum is accurately estimated, and 3) whether the wavelet's phase charctcr is correctly specified. In this paper, we present an ARMA model-based dcconvolution method for solving the above three problems. In prcstack dcconvolution, we assume that the wavelet is of minimum phase, While in post-stack dcconvolution, we have shown that the AR part of wavelets must be of minimum phase and the MA part of wavelets can be approximated by a zero-phase sequence. We have tested the method on both prcstack and post-stack data and achieved good results in either case especially the latter one.