Deep learning-driven velocity modeling based on seismic reflection data and multi-scale training sets
HAN Mingliang1, ZOU Zhihui1,2, MA Rui1,3
1. Key Lab of Submarine Geosciences and Prospecting Techniques, MOE, College of Marine Geosciences, Ocean University of China, Qingdao, Shandong 266100, China; 2. Evaluation and Detection Technology Laboratory of Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong 266061, China; 3. Shanghai Transwarp Co. Ltd, Shanghai 200030, China
Abstract：With the enlargement of seismic data volume, conventional methods of seismic velocity modeling are facing challenges to stability, accuracy, and computational efficiency. A deep learning-driven velocity modeling method based on seismic reflection data and multi-scale training sets was proposed in this paper. This method combined reflection waveforms and velocity spectra into the input of a full convolutional neural network and adopted the dropout layer in the neural network to improve the generalization ability. Moreover, it integrated multi-scale training sets to realize the mapping from seismic data to velocity models. To test the effectiveness and applicability of this method in different geologic structures, numerical experiments were carried out with layered models, isolated anomaly models, and the BP salt dome models. Experimental results show when seismic reflection waveforms and velocity spectra are combined as the feature dataset of deep learning, the accuracy of velocity modeling is higher than that in the case where they were adopted individually. It overcomes the defects of unstable modeling caused by using reflection waveform alone and low modeling accuracy induced by using velocity spectra alone. Furthermore, the accuracy of velocity modeling results in anomaly boundaries with the multi-scale velocity model to construct the training set is higher than that with a single-scale model as the training set. After only one training process, the deep neural network can quickly build the velocity model of the underground structure which is similar to the velocity structure in the training set. Therefore, compared with the conventional method, it has higher computational efficiency and deserves promotion when building large amounts of velocity models.
韩明亮, 邹志辉, 马锐. 利用反射地震资料和多尺度训练集的深度学习速度建模[J]. 石油地球物理勘探, 2021, 56(5): 935-946.
HAN Mingliang, ZOU Zhihui, MA Rui. Deep learning-driven velocity modeling based on seismic reflection data and multi-scale training sets. Oil Geophysical Prospecting, 2021, 56(5): 935-946.
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