摘要：The prestack amplitude variation with angle (AVA) inversion method utilising angle information to obtain the elastic parameters estimation of subsurface rock is vital to reservoir characterisation. Under the assumption of blocky layered media, an AVA inversion algorithm combining prestack spectral reflectivity inversion with sparse Bayesian learning (SBL) is presented. Prior information of the model parameters is involved in the inversion through the hierarchical Gaussian distribution where each parameter has a unique variance instead of sharing a common one. The frequency -domain prestack SBL inversion method retrieves sparse P- and S-wave impedance reflectivities by sequentially adding, deleting or re-estimating hyper-parameters without pre-setting the number of non-zero P- and S-wave reflectivity spikes. The selection of frequency components can help get rid of noise outside the selected frequency band. The precondition of the parameters helps to balance the weight of different parameters and incorporate the relationship between those parameters into the inversion process, thus improves the inversion result. Synthetic and real data examples illustrate the effectiveness of the method. (C) 2016 Elsevier B.V. All rights reserved.

关键字：AVA inversion Sparse Bayesian learning Precondition Hierarchical Gaussian distribution

ISSN号：0926-9851

卷、期、页：卷: 133 页: 1-8

发表日期：2016-10-01

期刊分区（SCI为中科院分区）：四区

收录情况：SCI（科学引文索引印刷版）,ESI（基本科学指标数据库）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：JOURNAL OF APPLIED GEOPHYSICS

通讯作者：纪永祯,邓力

第一作者：袁三一,王尚旭

论文类型：期刊论文

论文概要：纪永祯,袁三一,王尚旭,邓力，Frequency-domain sparse Bayesian learning inversion of AVA data for elastic parameters reflectivities，JOURNAL OF APPLIED GEOPHYSICS，2016，卷: 133 页: 1-8

论文题目：Frequency-domain sparse Bayesian learning inversion of AVA data for elastic parameters reflectivities