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Model-data-driven seismic inversion method based on small sample data
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DOI：10.1016/s1876-3804(22)60331-8.png)
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摘要
En 中文
As sandstone layers in thin interbedded section are difficult to identify, conventional model-driven seismic inversion and data-driven seismic prediction methods have low precision in predicting them. To solve this problem, a model-data-driven seismic AVO (amplitude variation with offset) inversion method based on a space-variant objective function has been worked out. In this method, zero delay cross-correlation function and F norm are used to establish objective function. Based on inverse distance weighting theory, change of the objective function is controlled according to the location of the target CDP (common depth point), to change the constraint weights of training samples, initial low-frequency models, and seismic data on the inversion. Hence, the proposed method can get high resolution and high-accuracy velocity and density from inversion of small sample data, and is suitable for identifying thin interbedded sand bodies. Tests with thin interbedded geological models show that the proposed method has high inversion accuracy and resolution for small sample data, and can identify sandstone and mudstone layers of about one-30th of the dominant wavelength thick. Tests on the field data of Lishui sag show that the inversion results of the proposed method have small relative error with well-log data, and can identify thin interbedded sandstone layers of about one-15th of the dominant wavelength thick with small sample data.
Keyword:
small sample data
space-variant objective function
model-data-driven
neural network
seismic AVO inversion
thin interbedded sandstone identification
Paleocene
Lishui sag
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