.png){kind=icon}
返回
Bayesian linearized AVO inversion
.png){kind=icon}
PRE
AI
DOI：10.1190/1.1543206.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A new linearized AVO inversion technique is developed in a Bayesian framework. The objective is to obtain posterior distributions for P-wave velocity, S-wave velocity, and density. Distributions for other elastic parameters can also be assessed-for example, acoustic impedance, shear impedance, and P-wave to S-wave velocity ratio. The inversion algorithm is based on the convolutional model and a linearized weak contrast approximation of the Zoeppritz equation. The solution is represented by a Gaussian posterior distribution with explicit expressions for the posterior expectation and covariance; hence, exact prediction intervals for the inverted parameters can be computed under the specified model. The explicit analytical form of the posterior distribution provides a computationally fast inversion method. Tests on synthetic data show that all inverted parameters were almost perfectly retrieved when the noise approached zero, With realistic noise levels, acoustic impedance was the best determined parameter, while the inversion provided practically no information about the density. The inversion algorithm has also been tested on a real 3-D data set from the Sleipner field. The results show good agreement with well logs, but the uncertainty is high.
Keyword:
WAVE-FORM INVERSION
PARAMETER-ESTIMATION
UNCERTAINTY ANALYSIS
SEISMIC INVERSION
INFORMATION
RESOLUTION
期刊
IF:
3.2
论文数:
8.4K
被引数:
3.3W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏Clinical impact of KRAS mutations in metastatic pancreatic ductal adenocarcinoma (PDAC).KRAS基因突变在转移性胰腺导管腺癌（PDAC）中的临床影响。
PREAI
分享收藏
查看更多内容