首页|Probabilistic seismic inversion based on physics-guided deep mixture density network

Probabilistic seismic inversion based on physics-guided deep mixture density network

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Deterministic inversion based on deep learning has been widely utilized in model parameters estimation.Constrained by logging data,seismic data,wavelet and modeling operator,deterministic inversion based on deep learning can establish nonlinear relationships between seismic data and model parameters.However,seismic data lacks low-frequency and contains noise,which increases the non-uniqueness of the solutions.The conventional inversion method based on deep learning can only establish the deter-ministic relationship between seismic data and parameters,and cannot quantify the uncertainty of inversion.In order to quickly quantify the uncertainty,a physics-guided deep mixture density network(PG-DMDN)is established by combining the mixture density network(MDN)with the deep neural network(DNN).Compared with Bayesian neural network(BNN)and network dropout,PG-DMDN has lower computing cost and shorter training time.A low-frequency model is introduced in the training process of the network to help the network learn the nonlinear relationship between narrowband seismic data and low-frequency impedance.In addition,the block constraints are added to the PG-DMDN framework to improve the horizontal continuity of the inversion results.To illustrate the benefits of proposed method,the PG-DMDN is compared with existing semi-supervised inversion method.Four synthetic data examples of Marmousi Ⅱ model are utilized to quantify the influence of forward modeling part,low-frequency model,noise and the pseudo-wells number on inversion results,and prove the feasibility and stability of the proposed method.In addition,the robustness and generality of the pro-posed method are verified by the field seismic data.

Deep learningProbabilistic inversionPhysics-guidedDeep mixture density network

Qian-Hao Sun、Zhao-Yun Zong、Xin Li

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National Key Laboratory of Deep Oil and Gas,China University of Petroleum(East China),Qingdao,266580,Shandong,China

Laoshan Laboratory,Qingdao,266580,Shandong,China

CNOOC Research Institute Ltd.,Beijing 100028,China

Shandong Province Foundation for Laoshan National Laboratory of Science and Technology FoundationNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaScience Foundation from Innovation and Technology Support Program for Young Scientists in Colleges of Shandong Province and Mini

LSKJ20220340042174139420301032019RA2136

2024

10.1016/j.petsci.2023.12.015

石油科学(英文版)
中国石油大学（北京）

石油科学(英文版)

EI
影响因子：0.88
ISSN：1672-5107
年,卷(期)：2024.21(3)