基于全卷积残差收缩网络的地震波阻抗反演

doi:10.11720/wtyht.2023.1569

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Abstract

Convolutional neural networks(CNNs) have achieved good results in seismic wave impedance inversion,but the inversion accuracy and anti-noise performance need to be improved.Hence,this study proposed a seismic wave impedance inversion method based on the fully convolutional residual shrinkage network with channel-wise thresholds(FCRSN-CW).In this method,the attention mechanism and the soft thresholding were first added to the structure of the residual network to form a inversion network.Then,a synthetic seismic dataset was obtained through forward calculation using wave impedance data.Subsequently,the dataset was applied to train the FCRSN-CW.Finally,the seismic data were put into the trained FCRSN-CW to obtain the inversion results directly.The inversion results of the theoretical model show that the FCRSN-CW can accurately invert the wave impedance and possesses satisfactory learning capacity and anti-noise performance.The inversion results of field data demonstrates that the method based on FCRSN-CW can effectively achieve seismic wave impedance inversion.

Key words： convolutional neural network wave impedance inversion fully convolutional shrinkage network channel-wise threshold

Received: 25 November 2022 Published: 23 January 2024

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	Articles by authors
	Kang WANG
	Cai-Yun LIU
	Jie XIONG
	Yong-Chang WANG
	Huan-Fa HU
	Jia-Shuai KANG

Cite this article:

Kang WANG,Cai-Yun LIU,Jie XIONG, et al. Seismic wave impedance inversion based on the fully convolutional residual shrinkage network[J]. Geophysical and Geochemical Exploration, 2023, 47(6): 1538-1546.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2023.1569 OR https://www.wutanyuhuatan.com/EN/Y2023/V47/I6/1538

Flow chart of seismic impedance inversion using FCRSN-CW

Mean square error of predicted impedance and true impedance by different residual blocks

Architecture of the FCRSN-CW

Residual building unit before(a) and after(b) improvement(different thresholds by channels)

Impedance(a) and synthetic seismic data(b) generated by 30 Hz 0° phase Ricker wavelet

19] and FCRSN-CW respectively
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Inversion results of No.650(a) and No.1250(b) by FCRN^[19] and FCRSN-CW respectively

19](a) and FCRSN-CW (b)
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Predictions of FCRN^[19](a) and FCRSN-CW (b)

Mean square error of predicted impedance and true impedance by wavelet with different phases

Predicted impedance from noisy data

Mean square error of predicted impedance and true impedance with different noise

The predicted impedance is compared with the true impedance at shallow and deep PCC

Location of the Volve oil field(a),seismic data and well log data from the Volve oil field(b)

Input seismic data along well(a) and corresponding true and prediction impedance(b)

PCC of predicted impedance and true impedance by wavelet with different phases and frequencies

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