石油物探2025，Vol.64Issue(4)：595-621,27.DOI:10.12431/issn.1000-1441.2024.0029
基于物理信息神经网络的地震波阻抗反演方法综述
A review of seismic impedance inversion methods based on physics-informed neural network
摘要
Abstract
Seismic impedance inversion is one of the key research subjects in the field of seismic exploration.Its basic goal is to quantitatively predict the wave impedance of underground medium from the seismic data.In recent years,with the rapid advancement of artificial intelligence technology,many scholars have proposed a variety of deep leaning(DL)based seismic impedance inversion methods.These methods embed geophysical information,such as physical laws,empirical formulas,and prior expert knowledge,into deep networks from different perspectives,effectively reducing the multi-solution and enhancing the physical interpretability of the inversion problem.This paper presents a review of these methods,which incorporate geophysical information by three strategies:①designing network architectures embedded with geophysical knowledge;②applying data constraints;③constructing multi-objective loss functions.The network architectures embedded with geophysical knowledge include:a forward physical model module,a reflection coefficient inversion model module,a seismic data spatiotemporal feature representation module,and a synthetic-to-real domain adaptation module.Data constraints include:generating diverse synthetic samples to train the deep network,and quantifying prior knowledge as inputs to the network.Multi-objective loss functions encompass physics-informed regularization terms,including closed-loop loss,generative adversarial loss,dynamic time warping(DTW)loss,spatial structure loss,and uncertainty loss.These strategies can reduce the multi-solution and enhance the reliability of the inversion problem from different perspectives.Finally,this paper provides two prospects for the DL-based seismic impedance inversion method:①multi-modal large models with robust comprehension and knowledge reasoning capabilities can be leveraged together with multi-modal data to enhance the generalization of the inversion model;②the multi-model fitting method can be combined with physics-informed neural network(PINN)to transform the"one-to-many"problem of inversion modeling into a"one-to-one"problem within each seismic facies segment,thereby reducing the multi-solution of the seismic impedance inversion.关键词
地震反演/多解性/深度学习/物理信息神经网络/多目标损失函数Key words
seismic inversion/multi-solution/deep learning/physics-informed neural network/multi-objective loss function分类
能源科技引用本文复制引用
宋操,陆文凯,耿伟恒,段旭东,王钰清,王琦,马绮铭,李尹硕..基于物理信息神经网络的地震波阻抗反演方法综述[J].石油物探,2025,64(4):595-621,27.基金项目
国家自然科学基金国家重大科研仪器研制项目(部门推荐)(42327901)、国家自然科学基金面上项目(41974126)和国家重点研究与发展计划资助项目(2018YFA0702501)共同资助.This research is financially supported by the Major Research Project on Scientific Instrument Development,National Natural Science Foundation of China(recommended via department,Grant No.42327901),the National Natural Science Foundation of China(General Program,Grant No.41974126)and the National Key Research and Development Program of China(Grant No.2018YFA0702501). （部门推荐）
