<i>Q</i>-compensated full waveform inversion for velocity and density

Wang, Yixin; Qu, Yingming

doi:10.1080/08123985.2021.1993059

Cited by 2 publications

(1 citation statement)

References 42 publications

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“…Several other directions need to be investigated next in regard to this method. The first is the presented method's realization of multi-parameter inversion; for example, it can invert parameters such as density and Q-value [38]. It also combines the multi-scale idea with multi-parameter inversion to improve the inversion accuracy of different parameters.…”

Section: Discussionmentioning

confidence: 99%

Multi-Scale Acoustic Velocity Inversion Based on a Convolutional Neural Network

Li,

Wu,

Liu

2024

Remote Sensing

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The full waveform inversion at this stage still has many problems in the recovery of deep background velocities. Velocity modeling based on end-to-end deep learning usually lacks a generalization capability. The proposed method is a multi-scale convolutional neural network velocity inversion (Ms-CNNVI) that incorporates a multi-scale strategy into the CNN-based velocity inversion algorithm for the first time. This approach improves the accuracy of the inversion by integrating a multi-scale strategy from low-frequency to high-frequency inversion and by incorporating a smoothing strategy in the multi-scale (MS) convolutional neural network (CNN) inversion process. Furthermore, using angle-domain reverse time migration (RTM) for dataset construction in Ms-CNNVI significantly improves the inversion efficiency. Numerical tests showcase the efficacy of the suggested approach.

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Section: Discussionmentioning

confidence: 99%