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Spatial Pattern Learning: Dip Structure Constraint Multi-View Convolutional Neural Network for Pre-Stacked Seismic Inversion
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DOI：10.1109/TGRS.2023.3307897.png)
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Abstract
En 中文
Seismic elastic parameters inversion is a method to predict the geophysical reservoir parameters, including P-wave velocity, S-wave velocity, and density, by using pre-stacked seismic data. Deep learning (DL) techniques have been utilized to establish complex and nonlinear inversion model. However, these DL-based inversion methods have some limitations. For instance, they often overlook the complementary observation distance information in pre-stacked seismic data at different incident angels, and they do not always consider the spatial structure and physical information conditions. As a result, the inversion solutions may be prone to falling into local minima. In order to alleviate this issue, we propose a spatial pattern learning method for pre-stacked seismic inversion (SPL-Inversion). First, multi-view convolutional neural network (CNN) is used to extract more complementary high-dimensional features of pre-stacked seismic data, which implies the spatial observation distance pattern of the input data. Second, the dip structure loss item is used to ensure the structural consistency between inverted results and seismic data, which constrains the spatial continuity. Third, forward physical constraint item improves the physical interpretability of inversion results. In addition, forward reconstruction result and estimated dip structure result can be used to automatically evaluate inversion results on unlabeled data. The proposed approach has been proven in enhancing the inversion accuracy and spatial continuity based on experimental results from both synthetic pre-stacked seismic data and real pre-stacked seismic data.
Keywords:
Data models
Convolutional neural networks
Geology
Mathematical models
Feature extraction
Estimation
Computational modeling
Deep learning (DL)
dip structure
multi-view
physical information
seismic inversion
velocity model
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W