A Label-Free High-Precision Residual Moveout Picking Method for Travel Time Tomography based on Deep Learning
This is an incremental improvement for geophysics applications, addressing local saltation and training data scarcity in RMO picking.
The paper tackled the problem of residual moveout (RMO) picking for travel time tomography by proposing a deep learning-based cascade method with data synthesis, achieving greater picking density and accuracy compared to semblance-based methods.
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.