Depth effects correction and lithofacies prediction for geophysical inversion data

Oregon Convention Center 

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

Integrative analysis

Model-based clustering

Markov chain Monte Carlo

Geophyiscal inversion 

Section on Bayesian Statistical Science