Abstract
With the application of deep learning algorithms in the industry, artificial intelligent technology has been developed in the field of seismic data interpretation in petroleum geophysical prospecting. This paper first starts from the analysis and research of Fully Convolutional Networks (FCN), U-Net model, the calculation of its lower accuracy results were analyzed, and the shortcomings of the model were found and pointed out; then it was proposed to introduce the High-Resolution Network (HR-Net) model into the field of intelligent interpretation of seismic data, and improve its network algorithm to make it more suitable for 3D space seismic data analysis and processing. Considering that the interpretation results of the FCN, U-Net, HR-Net algorithm cannot fully reflect the periodic phenomena and laws in the depth of the formation, the author improves HR-Net model and the high-resolution semantic fusion of the HR-Net model is also improved. The research result is the improved HR-Net algorithm model, which has certain application and promotion value in interpreting reservoirs and predicting faults from seismic image data.
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Acknowledgments
The project was supported by Sinopec Key Laboratory of Geophysics Fund Project ((Project Number: 36750000-23-FW0399-0010).
The project also was supported by Shandong Yingcai University. Fund Project Name: Lithofacies Prediction Method Based on RNN Model (Project Number: YCKY22011).
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He, Yh., Yu, M., Ji, Sq., Miao, Hp. (2024). Deep Learning Study on Seismic Data Interpretation Method. In: Lin, J. (eds) Proceedings of the International Field Exploration and Development Conference 2023. IFEDC 2023. Springer Series in Geomechanics and Geoengineering. Springer, Singapore. https://doi.org/10.1007/978-981-97-0272-5_22
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