Automatic Velocity Picking Based on Improved Mask R-CNN

Velocity spectrum analysis is the main method used to determine the normal moveout (NMO) velocity. However, conventional velocity spectrum analysis methods are inefficient and are typically affected by subjective factors, resulting in a deviation of the selected velocity from the actual velocity. Consequently, we developed a velocity-picking method that uses a mask region-based convolutional neural network (Mask R-CNN). The proposed method first generates a large number of training datasets, including velocity spectrum and labels, and then feeds them to the network for training. After the network is trained with the training data, it automatically extracts time–velocity functions from the velocity spectrum and outputs them for subsequent data processing. The proposed method converts the picking of velocity-energy clusters in seismic data processing into object detection in image processing. Therefore, to enable the method to accurately detect velocity-energy clusters in the velocity spectrum, according to the characteristics of velocity-energy clusters, the anchor scale and anchor ratio of the traditional Mask R-CNN are improved and a Mask R-CNN structure suitable for velocity-energy cluster detection is constructed. We solved the problem of object duplicate detection in the traditional Mask R-CNN by improving the nonmaximum suppression algorithm. The results of experiments conducted on synthetic and field data indicate that the proposed method quickly and accurately extracts time–velocity functions from the velocity spectrum. Furthermore, it obtains better results for velocity picking, NMO-corrected common midpoint gathers, and stacked sections than the traditional Mask R-CNN and the man–computer interaction velocity-picking (MIVP) method.