A non-uniform interpolation method for seismic data based on a diffusion probabilistic model

CHEN Yao; YU Siwei; LIN Rongzhi

doi:10.12363/issn.1001-1986.24.03.0160

Volume 52 Issue 8

Aug. 2024

Turn off MathJax

Article Contents

Abstract

References

COAL GEOLOGY & EXPLORATION > 2024 > 52(8): 177-186. > DOI: 10.12363/issn.1001-1986.24.03.0160

CHEN Yao，YU Siwei，LIN Rongzhi. A non-uniform interpolation method for seismic data based on a diffusion probabilistic model[J]. Coal Geology & Exploration，2024，52(8)：177−186. DOI: 10.12363/issn.1001-1986.24.03.0160

Citation:

PDF (8343 KB)

A non-uniform interpolation method for seismic data based on a diffusion probabilistic model

School of Mathematics, Harbin Institute of Technology, Harbin 150001, China

More Information

Received Date: March 05, 2024
Revised Date: June 01, 2024

Graphical Abstract

Abstract

Abstract

Objective
The non-uniform interpolation of seismic data is identified as a prolonged challenge in energy exploration. Since geophones cannot be precisely placed at positions corresponding to theoretical grid points, current uniform interpolation techniques frequently suffer deviations and detail distortion.
Methods
This study proposed a novel non-uniform interpolation method based on a diffusion probabilistic model, which is an emerging generative model in deep learning that involves the diffusion and generation processes. In the diffusion process, noise is added to the complete seismic data iteratively to train the denoising capability of the neural network. In the generation process, the neural network is employed for iterative denoising of data containing noise to obtain the reconstructed data. In this study, interpolation operators were employed to calculate the deviations between iterative and sampled data. These deviations were then used as the additional inputs of the neural network to improve the non-uniform interpolation capability of the diffusion probabilistic model. In the numerical experiments, the non-uniform sampling was tested using 2D synthetic and actual datasets, and the uniform interpolation model was compared with the model in this proposed study.
Results and Conclusions
The results indicate that the proposed method significantly enhanced the processing capability of the diffusion probabilistic model for non-uniform sampling. The tests of synthetic and actual data revealed an increase of approximately 7 dB in the signal-to-noise ratio. Therefore, the proposed method can effectively improve the precision of deep learning for non-uniform interpolation, providing a new approach for non-uniform interpolation algorithms of seismic data.
- interpolation of seismic data,
- non-uniform sampling,
- deep learning,
- generative model,
- diffusion probabilistic model

FullText(HTML)

References (31)

References

[1]	SPITZ S. Seismic trace interpolation in the F–X domain[J]. Geophysics,1991,56(6):785−794. DOI: 10.1190/1.1443096
[2]	PORSANI M J. Seismic trace interpolation using half‐step prediction filters[J]. Geophysics,2012,64(5):1461−1467.
[3]	刘保童. 一种基于傅里叶变换的去假频内插方法及应用[J]. 煤田地质与勘探,2009,37(2):63−67. DOI: 10.3969/j.issn.1001-1986.2009.02.017 LIU Baotong. Dealiasing interpolation based on Fourier transform and its application[J]. Coal Geology & Exploration,2009,37(2):63−67. DOI: 10.3969/j.issn.1001-1986.2009.02.017
[4]	RONEN J. Wave–equation trace interpolation[J]. Geophysics,1987,52(7):973−984. DOI: 10.1190/1.1442366
[5]	ABMA R,KABIR N. 3D interpolation of irregular data with a POCS algorithm[J]. Geophysics,2006,71(6):91−97. DOI: 10.1190/1.2356088
[6]	SHAO Jie,WANG Yibo. Seismic data antialiasing interpolation using sparse Radon transform and dynamic mask function[J]. Geophysics,2022,87(5):437−449. DOI: 10.1190/geo2021-0465.1
[7]	MANENTI R,SACCHI M D. Tensor tree decomposition as a rank–reduction method for pre–stack interpolation[J]. Geophysical Prospecting,2023,71(8):1404−1419. DOI: 10.1111/1365-2478.13374
[8]	NIU Xiao,FU Lihua,ZHANG Wanjuan,et al. Seismic data interpolation based on simultaneously sparse and low–rank matrix recovery[J]. IEEE Transactions on Geoscience and Remote Sensing,2021,60:9100613.
[9]	YU Siwei,MA Jianwei,ZHANG Xiaoqun,et al. Interpolation and denoising of high–dimensional seismic data by learning a tight frame[J]. Geophysics,2015,80(5):119−132. DOI: 10.1190/geo2014-0396.1
[10]	ALMADANI M,WAHEED U,MASOOD M,et al. Dictionary learning with convolutional structure for seismic data denoising and interpolation[J]. Geophysics,2021,86(5):361−374. DOI: 10.1190/geo2019-0689.1
[11]	WANG Benfeng,ZHANG Ning,LU Wenkai,et al. Deep–learning–based seismic data interpolation:A preliminary result[J]. Geophysics,2019,84(1):11−20. DOI: 10.1190/geo2017-0495.1
[12]	李宇腾,程建远,鲁晶津,等. 基于人工神经网络的矿井直流电阻率法超前预测方法[J]. 煤田地质与勘探,2023,51(6):185−193. DOI: 10.12363/issn.1001-1986.22.07.0545 LI Yuteng,CHENG Jianyuan,LU Jingjin,et al. Direct current resistivity method for the advance prediction of water Hazards in coal mines based on an artificial neural network[J]. Coal Geology & Exploration,2023,51(6):185−193. DOI: 10.12363/issn.1001-1986.22.07.0545
[13]	古瑶,解海军,周子鹏,等. 基于Attention机制的CNN–BiLSTM瞬变电磁实时反演方法[J]. 煤田地质与勘探,2023,51(10):134−143. DOI: 10.12363/issn.1001-1986.22.12.1000 GU Yao,XIE Haijun,ZHOU Zipeng,et al. An Attention mechanism–based CNN–BiLSTM real–time transient electromagnetic method[J]. Coal Geology & Exploration,2023,51(10):134−143. DOI: 10.12363/issn.1001-1986.22.12.1000
[14]	FANG Wenqian,FU Lihua,ZHANG Meng,et al. Seismic data interpolation based on U–net with texture loss[J]. Geophysics,2021,86(1):41−54. DOI: 10.1190/geo2019-0615.1
[15]	WU Geng,LIU Yang,LIU Cai,et al. Seismic data interpolation using deeply supervised U–Net++ with natural seismic training sets[J]. Geophysical Prospecting,2023,71(2):227−244. DOI: 10.1111/1365-2478.13307
[16]	GUO Yuanqi,FU Lihua,LI Hongwei. Seismic data interpolation based on multi–scale transformer[J]. IEEE Geoscience and Remote Sensing Letters,2023,20:7504205.
[17]	PENG Junheng,LI Yong,LIAO Zhangquan. Irregularly spatial seismic missing data reconstruction using transformer with periodic skip connection[J]. IEEE Transactions on Geoscience and Remote Sensing,2023,61:5918613.
[18]	郭元奇,李志明. 基于卷积和Transformer联合网络的地震数据插值[J]. 工程地球物理学报,2023,20(3):393−401. DOI: 10.3969/j.issn.1672-7940.2023.03.013 GUO Yuanqi,LI Zhiming. Seismic data interpolation based on joint convolutional and transformer networks[J]. Chinese Journal of Engineering Geophysics,2023,20(3):393−401. DOI: 10.3969/j.issn.1672-7940.2023.03.013
[19]	HO J,JAIN A,ABBEEL P. Denoising diffusion probabilistic models[EB/OL]. 2020:2006.11239. http://arxiv.org/abs/2006.11239v2.
[20]	XIAO Yi,YUAN Qiangqiang,JIANG Kui,et al. EDiffSR:An efficient diffusion probabilistic model for remote sensing image super–resolution[J]. IEEE Transactions on Geoscience and Remote Sensing,2024,62:1−14.
[21]	LUGMAYR A,DANELLJAN M,ROMERO A,et al. RePaint:Inpainting using denoising diffusion probabilistic models[C]. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans,LA,USA. IEEE,2022:11451–11461.
[22]	LI Haoying,YANG Yifan,CHANG Meng,et al. SRDiff:Single image super–resolution with diffusion probabilistic models[J]. Neurocomputing,2022,479:47−59. DOI: 10.1016/j.neucom.2022.01.029
[23]	LIU Qi,MA Jianwei. Generative interpolation via a diffusion probabilistic model[J]. Geophysics,2024,89(1):65−85. DOI: 10.1190/geo2023-0182.1
[24]	YU Siwei,MA Jianwei. Simultaneous off-the-grid regularization and reconstruction for 3D seismic data by a new combined sampling operator[J]. Geophysics,2023,88(4):291−302. DOI: 10.1190/geo2022-0500.1
[25]	YU Siwei,MA Jianwei,ZHAO Bangliu. Off-the-grid vertical seismic profile data regularization by a compressive sensing method[J]. Geophysics,2020,85(2):157−168. DOI: 10.1190/geo2019-0357.1
[26]	CAROZZI F,SACCHI M. Interpolated multichannel singular spectrum analysis:A reconstruction method that honors true trace coordinates[J]. Geophysics,2021,86(1):55−70. DOI: 10.1190/geo2019-0806.1
[27]	ZWARTJES P M,SACCHI M D. Fourier reconstruction of nonuniformly sampled,aliased seismic data[J]. Geophysics,2007,72(1):21−32. DOI: 10.1190/1.2399442
[28]	MA J,PLONKA G. The curvelet transform[J]. IEEE Signal Processing Magazine,2010,27(2):118−133. DOI: 10.1109/MSP.2009.935453
[29]	DIAKOGIANNIS F I,WALDNER F,CACCETTA P,et al. ResUNet–a:A deep learning framework for semantic segmentation of remotely sensed data[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2020,162:94−114. DOI: 10.1016/j.isprsjprs.2020.01.013
[30]	KINGMA D P,BA J. Adam:A method for stochastic optimization[C]//Proceedings of the International Conference on Learning Representations. 2014.
[31]	RONNEBERGER O,FISCHER P,BROX T. U–net:Convolutional networks for biomedical image segmentation[M]//NAVAB N,HORNEGGER J,WELLS W M,et al. Lecture notes in computer science. Cham:Springer International Publishing,2015:234–241.

Cited By

Cited by

Periodical cited type(8)

1.	李新旺，马素英，刘淼，付银环，徐宏斌，赵雪朋. 河北省深层地下水回补研究. 中国水利. 2024(07): 28-33 .
2.	刘晓秀，曾一凡，武强，孟世豪，梁季月，侯柱平. 基于绿色生态开采的我国西部生态脆弱矿区“煤-水-热”共采范式（英文）. Engineering. 2024(07): 209-222 .
3.	曾一凡，刘晓秀，武强，梁季月. 双碳背景下“煤-水-热”正效协同共采理论与技术构想. 煤炭学报. 2023(02): 538-550 .
4.	郭艳，桂和荣，魏久传，胡满聪，郭祥东，聂锋，陈永青，解建，叶爽，李俊. 区域注浆扰动下渗流场-化学场演化及耦合作用. 煤炭科学技术. 2023(07): 152-166 .
5.	马莲净，王颂，杜松，赵宝峰，张阳，卢才武. 宁东煤田枯竭油层回注存储高矿化度矿井水技术思路. 煤炭科学技术. 2023(12): 149-158 .
6.	张雷，徐智敏，袁慧卿，孙亚军，郭娟，陈天赐，李鑫，刘琪. 深部开采高盐矿井水减排治理技术体系构建与实现. 煤炭科学技术. 2023(12): 208-219 .
7.	李超峰，王疆霞，刘峰，刘基，胡扬，徐中华. 彬长矿区含水层地下水水化学演化规律研究. 煤炭科技. 2022(04): 102-110 .
8.	苏旭楠. 刘家沟组作为矿井水深部转移存储目的层可行性分析. 中国煤炭地质. 2022(S2): 47-53 .

Other cited types(1)

Get Citation

PDF

XML

Article Metrics

Article views (127) PDF downloads (46) Cited by(9)

A non-uniform interpolation method for seismic data based on a diffusion probabilistic model

Abstract

References

Cited by

Periodical cited type(8)

Other cited types(1)

Catalog

Article Metrics

Related

A non-uniform interpolation method for seismic data based on a diffusion probabilistic model

Abstract

References

Cited by

Periodical cited type(8)

Other cited types(1)

Catalog

Article Metrics

Related

Export File

Citation

Format

Content