Well-logging-constrained impedance inversion is an effective process for predicting the thickness and bifurcation of coal seams. Wavelet changes in a complex region achieve the best match between the inverse and source wavelets, affecting the accuracy of the inversion solution and the ability to obtain accurate inverted acoustic impedance (AI) data. We have conducted the joint inversion of wavelet and AI data using iterative methods, which combined the conjugate-gradient (CG) method and particle-swarm-optimization (PSO) algorithm. The Marmousi AI model was used to prove the reliability of the method. The CG-PSO algorithm achieved excellent results compared with the statistical wavelet pickup method. The wavelet obtained by the CG-PSO algorithm is preferred for inversion operations. We applied a new method to invert field data and predict the thickness and bifurcation of coal seams in the karst region. The results find that the wavelet spectrum obtained by the CG-PSO matches the spectrum map of the coal seam in the Yuwang colliery. We determined the distribution of the thickness and bifurcation of the 101 panel, Yuwang Colliery, Yunnan Province. The average error of the predicted coal thickness is 0.17 m (14.4%), which verifies the feasibility and effectiveness of the method. The method provides insights into the AI inversion of constrained waves in complex regions.
Log-constrained inversion based on a conjugate gradient-particle swarm optimization algorithm
Guangui Zou, Yanhai Liu, Deliang Teng, Fei Gong, Jiasheng She, Ke Ren, and Chengyang Han
https://doi.org/10.1190/INT-2022-0109.1