1. School of Computer Science and Engineering (School of Cyberspace Security), University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China;
2. School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China;
3. Yangtze Delta Region Institute of University of Electronic Science and Technology of China, Huzhou, Huzhou, Zhejiang 313000, China;
4. SINOPEC Petroleum Exploration and Production Research Institute, Beijing 100083, China
Abstract：In the field of oil and gas exploration and development,high-precision matching of multiwave seismic signals needs to be conducted to give full play to the technical advantages of multiwave and multi-component seismic exploration. This research proposes an intelligent matching method based on deep learning for multiwave seismic signals. Different from the traditional method that changes such features of the seismic signal as propagation time,phase,and frequency to complete the matching,this method uses the powerful feature extraction ability of the convolutional neural network (CNN) to directly extract the waveform features of the seismic signal. Moreover,converted wave (PS) extraction by resampling,longitudinal wave (PP) and converted wave feature loss weighting,and the Adam gradient descent algorithm to update PS wave features are also applied so that the waveform of the PS wave approaches that of the PP wave in the time domain with no overall changes. The dynamic,kinematic,and geometric features,such as the propagation time,phase,and frequency,of multiwave seismic signals are matched automatically through the waveform matching between the PP wave and the PS wave. The application of the 3D3C seismic data from Xinchang in the Western Sichuan Depression shows that this method does not require manual intervention in the matching of multiwave seismic signals and that it has the advantages of high precision,high efficiency,intelligence,and automation. In addition to maintaining its original characteristics, the matched PS wave obtains a dominant frequency,bandwidth,and waveform closer to those of the PP wave. Effectively describing the formation contact relationship and being more conducive to geological interpretations,such as fault identification,formation tracking,and lithological boundary chara-〖JP〗cterization,the proposed method lays a solid foundation for subsequent applications such as multiwave contrast geological interpretation and joint inversion.
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