Relative geological time volume estimation method based on deep learning
LI Haishan1,2, YANG Wuyang1,2, WU Xinming3, WEI Xinjian1,2, XU Xin1,2
1. Research Institute of Petroleum Exploration & Development-Northwest, PetroChina, Lanzhou, Gansu 730020, China; 2. Key Laboratory of Internet of Things, CNPC, Lanzhou, Gansu 730020, China; 3. School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China
Abstract：Conventional automatic interpretation methods are difficult to correctly track seismic reflection horizons dislocated by faults in 3D seismic volume. Therefore, a relative geological time volume estimation method based on deep learning is proposed. Firstly, according to the estimation requirements of the relative geological time vo-lume, an estimation network of the relative geological time volume composed of an encoder-decoder framework is designed. Secondly, the estimation network of the relative geological time volume is trained by the generated accurately labeled synthetic training dataset with the structural similarity criterion as the loss function, and thus the network can accurately estimate the relative geological time volume from the seismic volume. Finally, the automatic tracking of multiple seismic reflection horizons is realized by extracting multiple constant iso-surfaces of the relative geological time volume. The test results show that this method not only shows excellent performance on the validation dataset but also achieves a positive application effect on the actual seismic volume. In addition, multiple seismic reflection horizons which can characterize the spatial shape of the stratum can be obtained at one time by using the estimated relative geological time volume.
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