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arXiv · 2503.06038

A Label-Free High-Precision Residual Moveout Picking Method for Travel Time Tomography based on Deep Learning

Hongtao Wang·Jiandong Liang·Lei Wang·Shuaizhe Liang·Jinping Zhu·Chunxia Zhang·Jiangshe Zhang
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Abstract

Residual moveout (RMO) provides critical information for travel time tomography. The current industry-standard method for fitting RMO involves scanning high-order polynomial equations. However, this analytical approach does not accurately capture local saltation, leading to low iteration efficiency in tomographic inversion. Supervised learning-based image segmentation methods for picking can effectively capture local variations; however, they encounter challenges such as a scarcity of reliable training samples and the high complexity of post-processing. To address these issues, this study proposes a deep learning-based cascade picking method. It distinguishes accurate and robust RMOs using a segmentation network and a post-processing technique based on trend regression. Additionally, a data synthesis method is introduced, enabling the segmentation network to be trained on synthetic datasets for effective picking in field data. Furthermore, a set of metrics is proposed to quantify the quality of automatically picked RMOs. Experimental results based on both model and real data demonstrate that, compared to semblance-based methods, our approach achieves greater picking density and accuracy.

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Hongtao Wang, Jiandong Liang, Lei Wang, Shuaizhe Liang, Jinping Zhu, Chunxia Zhang, Jiangshe Zhang. 2025-03-08. A Label-Free High-Precision Residual Moveout Picking Method for Travel Time Tomography based on Deep Learning. https://arxiv.org/abs/2503.06038

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