Abstract Number:
2361
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Yibo ZHAI (1), Yujian Hou (2), Zhanxiang He (3), Fangda Song (4)
Institutions:
(1) Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China, (2) Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Changsha, China, (3) Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology, Shenzhen, China, (4) School of Data Science, The Chinese University of Hong Kong, Shenzhen, China
Co-Author(s):
Yujian Hou
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring
Zhanxiang He
Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology
Fangda Song
School of Data Science, The Chinese University of Hong Kong, Shenzhen
First Author:
Yibo ZHAI
Department of Statistics, The Chinese University of Hong Kong
Presenting Author:
Abstract Text:
Geophysical inversion is the mathematical process of predicting underground geophysical measurements at different depths from the wave signals detected on the ground, like seismic waves. Three-dimensional reconstruction of lithofacies based on geophysical inversion data enables determining the location and depth of drilling. However, the regularization step involved in geophysical inversion leads to significant differences between the geophysical inversion data and drilled rock data. Therefore, geologists usually have to annotate inversion results manually based on experience. The inversion differences of the same lithofacies at different depths will further hamper manual annotation. Therefore, we develop an unsupervised hierarchical Bayesian model to cluster different lithofacies, correct for the depth effects automatically, and finally recover the 3D geological structures from inversion data. We also consider the spatial continuity of lithofacies distribution in our hierarchical model. Finally, we predict the lithofacies distribution of an oil base located in Northeastern China, and the lithofacies prediction based on inversion data is highly consistent with drilling rock samples
Keywords:
Integrative analysis|Model-based clustering|Markov chain Monte Carlo|Geophyiscal inversion| |
Sponsors:
Section on Bayesian Statistical Science
Tracks:
Applications in Applied Sciences
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