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AVO Inversion Based on Transfer Learning and Low-Frequency Model
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AI
DOI：10.1109/LGRS.2021.3132426.png)
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摘要
En 中文
Amplitude variation with offset (AVO) refers to the amplitude variation with offset. This relationship can be used to analyze lithology and identify the oil and gas reservoirs in seismic exploration. Traditional AVO inversion is a typical ill-posed problem. When deep learning is directly used for seismic inversion, there are three main issues. First, the label data are insufficient. Second, a network trained for one working area is not applicable to other working areas. Third, there are spatial discontinuities and instability problems in the inversion results. In this letter, we propose the AVO inversion method that combines transfer learning and low-frequency component constraints. Transfer learning strategy is introduced to solve two main problems: The label data are insufficient to train the network, and the trained network is not applicable to other regions. Taking the low-frequency component as the constraint term makes the solution easier to converge to the true value. The experimental results of a typical example show that our method not only effectively improves the prediction accuracy and spatial continuity of the inversion results, but also reduces dependence on logging data.
Keyword:
Feature extraction
Data models
Transfer learning
Mathematical models
Convolutional neural networks
Training
Oils
Amplitude variation with offset (AVO) inversion
deep learning (DL)
low frequency
transfer learning
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
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