Seismic impedance inversion based on convolutional neural network and uncertainty quantization

Seismic impedance inversion is a technique that converts seismic data interface information into stratigraphic information, serving as a bridge between seismic data, well logging, and geological information. Although the existing deep learning impedance inversion methods have improved inversion accuracy, they do not involve the quantitative evaluation of uncertainty in the inversion results. Based on these effects, we improved the existing CNN impedance inversion model by incorporating Bayesian theory and approximate Bayesian inference. We implemented two methods for impedance inversion: one based on a Bayesian convolution neural network (BCNN), and the other is a CNN based on Monte Carlo (MC) sampling and Dropout using approximate Bayesian inference. These methods allow for uncertainty modeling and incorporation of uncertainty into the inversion model. The uncertainty of the inversion results is quantified through the output probability distribution. The results of field data applications clarify the effectiveness and characteristics of the two methods. MC Dropout CNN method is superior in efficiency due to approximate theory, while BCNN shows advantages in accuracy, resolution, and uncertainty quantification.