DE-MCMC Based Stochastic Seismic Inversion Incorporating Gaussian Mixture Model

2019 ◽  
Author(s):  
K. Li ◽  
X. Yin ◽  
Z. Zong
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Seismic Inversion ◽  
Stochastic Seismic Inversion

Poststack Seismic Inversion Using A Patch-based Gaussian Mixture Model

Geophysics ◽  
2021 ◽  
pp. 1-102
Author(s):  
Lingqian Wang ◽  
Hui Zhou ◽  
Hengchang Dai ◽  
Bo Yu ◽  
Wenling Liu ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Acoustic Impedance ◽  
Structural Information ◽  
Gaussian Mixture ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Borehole Data ◽  
Inversion Result ◽  
Band Limited

Seismic inversion is a severely ill-posed problem, because of noise in the observed record, band-limited seismic wavelets, and the discretization of a continuous medium. Regularization techniques can impose certain characteristics on inversion results based on prior information in order to obtain a stable and unique solution. However, it is difficult to find an appropriate regularization to describe the actual subsurface geology. We propose a new acoustic impedance inversion method via a patch-based Gaussian mixture model (GMM), which is designed using available well logs. In this method, firstly, the non-local means (NLM) method estimates acoustic impedance around wells in terms of the similarity of local seismic records. The extrapolated multichannel impedance are then decomposed into impedance patches. Using patched data rather than a window or single trace for training samples to obtain the GMM parameters, which contain local lateral structural information, can provide more impedance structure details and enhance the stability of the inversion result. Next, the expectation maximization (EM) algorithm is used to obtain the GMM parameters from the patched data. Finally, we apply the alternating direction method of multipliers (ADMM) to solve the conventional Bayesian inference illustrating the role of regularization, and construct the objective function using the GMM parameters. Therefore, the inversion results are compliant with the local structural features extracted from the borehole data. Both synthetic and field data tests validate the performance of our proposed method. Compared with other conventional inversion methods, our method shows promise in providing a more accurate and stable inversion result.

Point Cloud Registration Algorithm Combined Gaussian Mixture Model and Point-to-Plane Metric

2018 ◽  
Vol 30 (4) ◽  
pp. 642
Author(s):  
Guichao Lin ◽  
Yunchao Tang ◽  
Xiangjun Zou ◽  
Qing Zhang ◽  
Xiaojie Shi ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Point Cloud ◽  
Gaussian Mixture ◽  
Point Cloud Registration ◽  
Registration Algorithm

Voice Conversion Based on Matrix Variate Gaussian Mixture Model Using Multiple Frame Features

2016 ◽  
Author(s):  
Yi Yang ◽  
Hidetsugu Uchida ◽  
Daisuke Saito ◽  
Nobuaki Minematsu
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Voice Conversion ◽  
Multiple Frame
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