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Double-scale supervised inversion with a data-driven forward model for low-frequency impedance recovery
发布时间:2021-12-22
摘要:
Low-frequency information is important in reducing the nonuniqueness of absolute impedance inversion and for quantitative seismic interpretation. In traditional model-driven impedance inversion methods, low-frequency impedance background is from an initial model and is almost unchanged during the inversion process. Moreover, the inversion results are limited by the quality of the modeled seismic data and the extracted wavelet. To alleviate these issues, we investigate a double-scale supervised impedance inversion method based on the gated recurrent encoder-decoder network (GREDN). We first train the decoder network of GREDN called the forward operator, which can map impedance to seismic data. We then implement the well-trained decoder as a constraint to train the encoder network of GREDN called the inverse operator. Besides matching the output of the encoder with broadband pseudo-well impedance labels, data generated by inputting the encoder output into the known decoder match the observed narrowband seismic data. Both the broadband impedance information and the already-trained decoder largely limit the solution space of the encoder. Finally, after training, only the derived optimal encoder is applied to unseen seismic traces to yield broadband impedance volumes. The proposed approach is fully data-driven and does not involve the initial model, seismic wavelet and model-driven operator. Tests on the Marmousi model illustrate that the proposed double-scale supervised impedance inversion method can effectively recover low-frequency components of the impedance model, and demonstrate that low frequencies of the predicted impedance originate from well logs. Furthermore, we apply the strategy of combining the double-scale supervised impedance inversion method with a model-driven impedance inversion method to process field seismic data. Tests on a field data set show that the predicted impedance results not only reveal a classical tectonic sedimentation history, but also match the corresponding results measured at the locations of two wells.
© 2022 Society of Exploration Geophysicists.
ISSN号:0016-8033
卷、期、页:v 87,n 2,
发表日期:2021-11-02
影响因子:2.608800
期刊分区(SCI为中科院分区):三区
收录情况:EI(工程索引),地学领域高质量科技期刊分级目录(国外T1)
发表期刊名称:Geophysics
通讯作者:桑文镜,焦新奇,罗亚能
第一作者:袁三一,王尚旭
论文类型:期刊论文
论文概要:袁三一,王尚旭,桑文镜,焦新奇,罗亚能,Double-scale supervised inversion with a data-driven forward model for low-frequency impedance recovery,Geophysics,2021,v 87,n 2,
论文题目:Double-scale supervised inversion with a data-driven forward model for low-frequency impedance recovery