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.
View on arXiv@article{wang2025_2503.06038,
title={ A Label-Free High-Precision Residual Moveout Picking Method for Travel Time Tomography based on Deep Learning },
author={ Hongtao Wang and Jiandong Liang and Lei Wang and Shuaizhe Liang and Jinping Zhu and Chunxia Zhang and Jiangshe Zhang },
journal={arXiv preprint arXiv:2503.06038},
year={ 2025 }
}