A theory-guided deep-learning formulation and optimization of seismic waveform inversion

计算机科学 地震反演 反演(地质) Broyden–Fletcher–Goldfarb–Shanno算法 循环神经网络 反问题 算法 非线性系统 人工神经网络 波形 深度学习 数学优化 人工智能 地质学 数学 地震学 方位角 异步通信 雷达 数学分析 物理 构造学 电信 量子力学 计算机网络 几何学
作者
Jian Sun,Zhan Niu,K. A. Innanen,Junxiao Li,Daniel Trad
出处
期刊:Geophysics [Society of Exploration Geophysicists]
卷期号:85 (2): R87-R99 被引量:151
标识
DOI:10.1190/geo2019-0138.1
摘要

Deep-learning techniques appear to be poised to play very important roles in our processing flows for inversion and interpretation of seismic data. The most successful seismic applications of these complex pattern-identifying networks will, presumably, be those that also leverage the deterministic physical models on which we normally base our seismic interpretations. If this is true, algorithms belonging to theory-guided data science, whose aim is roughly this, will have particular applicability in our field. We have developed a theory-designed recurrent neural network (RNN) that allows single- and multidimensional scalar acoustic seismic forward-modeling problems to be set up in terms of its forward propagation. We find that training such a network and updating its weights using measured seismic data then amounts to a solution of the seismic inverse problem and is equivalent to gradient-based seismic full-waveform inversion (FWI). By refining these RNNs in terms of optimization method and learning rate, comparisons are made between standard deep-learning optimization and nonlinear conjugate gradient and limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimized algorithms. Our numerical analysis indicates that adaptive moment (or Adam) optimization with a learning rate set to match the magnitudes of standard FWI updates appears to produce the most stable and well-behaved waveform inversion results, which is reconfirmed by a multidimensional 2D Marmousi experiment. Future waveform RNNs, with additional degrees of freedom, may allow optimal wave propagation rules to be solved for at the same time as medium properties, reducing modeling errors.
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