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Improved probabilistic seismic AVO inversion constrained by instantaneous phase using quadratic PP-reflectivity approximation and IA2RMS-Gibbs algorithm 认领 引用
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作者 Shuang-Shuang Zhou Xing-Yao Yin +1 位作者 Kun Li Ya-Ming Yang 《Petroleum Science》 SCIE EI CAS CSCD 2026年第1期127-142,共16页
Seismic amplitude variation with offset(AVO)inversion is a cornerstone of oil and gas reservoir prediction,enabling the estimation of subsurface elastic parameters and characterization of stratigraphic interfaces.Howe... Seismic amplitude variation with offset(AVO)inversion is a cornerstone of oil and gas reservoir prediction,enabling the estimation of subsurface elastic parameters and characterization of stratigraphic interfaces.However,balancing inversion accuracy and computational efficiency remains a critical challenge.To address this,we propose a novel probabilistic AVO inversion framework integrating three key innovations.First,we derive a high-precision quadratic approximation for compressional(P-wave)reflectivity by retaining first-and second-order terms from the exact Zoeppritz equations through a perturbation strategy.This approach significantly enhances accuracy compared to conventional linear approximations,particularly in reflecting the true amplitude variation at large angles.Subsequently,to improve lateral continuity and stratigraphic resolution,we introduce an instantaneous phase constraint derived via the Hilbert transform.This constraint leverages phase sensitivity to seismic waveform coherence,ensuring geologically consistent interface characterization during stochastic inversion.Furthermore,we develop a hybrid Markov Chain Monte Carlo(MCMC)algorithm combining adaptive Gibbs sampling with the independent doubly adaptive rejection Metropolis sampling(IA2RMS)method.This framework efficiently samples high-dimensional posterior probability density functions(PDFs)of elastic pa rameters:Gibbs sampling gene rates adaptive proposal distributions,while IA2RMS accele rates Markov chain convergence through location-and scale-adjustable proposals.Numerical experiments and field seismic data demonstrate the robustness and feasibility of the proposed probabilistic AVO inversion method. 展开更多
关键词 Quadratic approximation Instantaneous phase constraint Gibbs sampling Independent doubly adaptive rejection metropolis sampling(IA2RMS) Probability density functions(PDFs)
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