InversionNet: A Real-Time and Accurate Full Waveform Inversion with CNNs and continuous CRFs

Abstract

Full-waveform inversion problems are usually formulated as optimization problems, where the forward-wave propagation operator $f$ maps the subsurface velocity structures to seismic signals. The existing computational methods for solving full-waveform inversion are not only computationally expensive, but also yields low-resolution results because of the ill-posedness and cycle skipping issues of full-waveform inversion. To resolve those issues, we employ machine-learning techniques to solve the full-waveform inversion. Specifically, we focus on applying the convolutional neural network~(CNN) to directly derive the inversion operator $f^{-1}$ so that the velocity structure can be obtained without knowing the forward operator $f$. We build a convolutional neural network with an encoder-decoder structure to model the correspondence from seismic data to subsurface velocity structures. Furthermore, we employ the conditional random field~(CRF) on top of the CNN to generate structural predictions by modeling the interactions between different locations on the velocity model. Our numerical examples using synthetic seismic reflection data show that the propose CNN-CRF model significantly improve the accuracy of the velocity inversion while the computational time is reduced.

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• Physics-Consistent Data-driven Waveform Inversion with Adaptive Data Augmentation
• Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop
• Making Invisible Visible: Data-Driven Seismic Inversion with Spatio-temporally Constrained Data Augmentation
• Inversion of Time-Lapse Surface Gravity Data for Detection of 3D CO${}_2$ Plumes via Deep Learning
• Hybrid-DNNs: Hybrid Deep Neural Networks for Mixed Inputs
• Controllable seismic velocity synthesis using generative diffusion models
• Machine Learning Based Approach to Predict Ductile Damage Model Parameters for Polycrystalline Metals
• Revisit Geophysical Imaging in A New View of Physics-informed Generative Adversarial Learning
• Ambient Noise Full Waveform Inversion with Neural Operators
• On the Robustness and Generalization of Deep Learning Driven Full Waveform Inversion