Intelligent noise suppression for 3D post-stack seismic data of the Junggar Basin

Authors

MAO Haibo, Institute of Geophysics, Research Institute of Exploration and Development, Xinjiang Oilfield Company, PetroChina, Urumqi 830013, ChinaFollow
ZHOU Xin, Institute of Geophysics, Research Institute of Exploration and Development, Xinjiang Oilfield Company, PetroChina, Urumqi 830013, China
LI Xiaofeng, Institute of Geophysics, Research Institute of Exploration and Development, Xinjiang Oilfield Company, PetroChina, Urumqi 830013, China
PAN Long, Institute of Geophysics, Research Institute of Exploration and Development, Xinjiang Oilfield Company, PetroChina, Urumqi 830013, China
LIN Juan, Institute of Geophysics, Research Institute of Exploration and Development, Xinjiang Oilfield Company, PetroChina, Urumqi 830013, China
LIU Dawei, School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China
WANG Xiaokai, School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Abstract

Objective The Junggar Basin is recognized as a significant petroliferous basin in China, and its hydrocarbon exploration targets have shifted to deeper strata. However, the 3D seismic data of this basin suffer from low signal-to-noise ratios (SNRs) and high data volumes due to the basin's complex near-surface conditions, the great depths of exploration targets, and seismic data acquisition methods characterized by wide azimuths, broadbands, and high density. This complicates the identification of hydrocarbon exploration targets, rendering the improvement in the quality of the 3D seismic data by noise suppression vitally important. Methods The progress in the deep learning theory and the enhancement of hardware performance have significantly boosted the learning capability and processing efficiency of deep neural networks. Based on residual learning and batch normalization techniques, this study developed a three-dimensional denoising convolutional neural network (3D-DnCNN) and a deep learning-based noise suppression workflow applicable to the 3D seismic data of the Junggar Basin. Results and Conclusions To meet the actual demand of a large contiguous surveyed area in the Junggar Basin, high-quality labels were constructed using the noise suppression results of zones with high seismic coverage and SNRs, and the trained 3D-DnCNN was then applied to the entire study area. Compared to the conventional industrial workflow, the workflow developed in this study yielded more consistent seismic events, more intact faults preserved, and clearer top boundary and inner layers of the Carboniferous strata. Additionally, since the 3D-DnCNN learned the characteristics of offset-related arc noise in high-SNR zones, it outperformed the conventional industrial workflow in suppressing such noise across the entire surveyed area. By adjusting network parameters such as the network depth, convolution kernel size, and the strategy for selecting training samples, the 3D-DnCNN can be further optimized to adapt to seismic data from different areas, thereby enhancing the applicability and effectiveness of the seismic noise suppression technique.

Keywords

Junggar Basin, deep learning, convolutional neural network, noise suppression

DOI

10.12363/issn.1001-1986.24.02.0129

Recommended Citation

MAO Haibo, ZHOU Xin, LI Xiaofeng, et al. (2024) "Intelligent noise suppression for 3D post-stack seismic data of the Junggar Basin," Coal Geology & Exploration: Vol. 52: Iss. 11, Article 13.
DOI: 10.12363/issn.1001-1986.24.02.0129
Available at: https://cge.researchcommons.org/journal/vol52/iss11/13

Reference

[1] 陈文超，刘达伟，魏新建，等. 基于地震资料有效信息约束的深度网络无监督噪声压制方法[J]. 煤田地质与勘探，2021，49(1)：249−256.

CHEN Wenchao，LIU Dawei，WEI Xinjian，et al. Unsupervised noise suppression method for depth network seismic data based on prior information constraint[J]. Coal Geology & Exploration，2021，49(1)：249−256.

[2] 刘法启，张关泉. 小波变换与F-K算法在滤波中的应用[J]. 石油地球物理勘探，1996，31(6)：782−791.

LIU Faqi，ZHANG Guanquan. Application of wavelet transform and F-K algorithm in filtering[J]. Oil Geophysical Prospecting，1996，31(6)：782−791.

[3] 康冶，于承业，贾卧，等. f-x域去噪方法研究[J]. 石油地球物理勘探，2003，38(2)：136−138.

KANG Ye，YU Chengye，JIA Wo，et al. A study on noise-suppression method in f-x domain[J]. Oil Geophysical Prospecting，2003，38(2)：136−138.

[4] MALLAT S G. A theory for multiresolution signal decomposition：The wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，1989，11(7)：674−693.

[5] TANG Na，ZHAO Xian，LI Yue，et al. Adaptive threshold shearlet transform for surface microseismic data denoising[J]. Journal of Applied Geophysics，2018，153：64−74.

[6] LIU Cai，CHEN Changle，WANG Dian，et al. Seismic dip estimation based on the two-dimensional Hilbert transform and its application in random noise attenuation[J]. Applied Geophysics，2015，12(1)：55−63.

[7] 金雷，李月，杨宝俊. 用时频峰值滤波方法消减地震勘探资料中随机噪声的初步研究[J]. 地球物理学进展，2005，20(3)：724−728.

JIN Lei，LI Yue，YANG Baojun. Reduction of random noise for seismic data by time-frequency peak filtering[J]. Progress in Geophysics，2005，20(3)：724−728.

[8] DUNCAN G，BERESFORD G. Median filter behaviour with seismic data 1[J]. Geophysical Prospecting，1995，43(3)：329−345.

[9] CHEN Yangkang，ZU Shaohuan，WANG Yufeng，et al. Deblending of simultaneous source data using a structure-oriented space-varying Median filter[J]. Geophysical Journal International，2020，222(3)：1805−1823.

[10] PORSANI M J，URSIN B，SILVA M G，et al. Dip-adaptive singular-value decomposition filtering for seismic reflection enhancement[J]. Geophysical Prospecting，2013，61(1)：42−52.

[11] 马继涛，王建花，刘国昌. 基于频率域奇异值分解的地震数据插值去噪方法研究[J]. 石油物探，2016，55(2)：205−213.

MA Jitao，WANG Jianhua，LIU Guochang. Seismic data noise attenuation and interpolation using singular value decomposition in frequency domain[J]. Geophysical Prospecting for Petroleum，2016，55(2)：205−213.

[12] HE Kaiming，ZHANG Xiangyu，REN Shaoqing，et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas，NV，USA. IEEE，2016：770–778.

[13] RUSSAKOVSKY O，DENG Jia，SU Hao，et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision，2015，115(3)：211−252.

[14] BELINKOV Y，GEHRMANN S，PAVLICK E. Interpretability and analysis in neural NLP[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics：Tutorial Abstracts. Online. Stroudsburg，PA，USA：Association for Computational Linguistics，2020：5.

[15] RADFORD A ，NARASIMHAN K，SALIMANS T，et al. Improving language understanding by generative pre-training[J]. OpenAI Blog，2018.

[16] BROWN T，MANN B，RYDER N，et al. Language models are few-shot learners[J]. Advances in Neural Information Processing Systems，2020，33：1877−1901.

[17] CHEN Chenyi，SEFF A，KORNHAUSER A，et al. DeepDriving：Learning affordance for direct perception in autonomous driving[C]//2015 IEEE International Conference on Computer Vision (ICCV). Santiago，Chile. IEEE，2015：2722–2730.

[18] XUE Qingwen，WANG Ke，LU J J，et al. Rapid driving style recognition in car-following using machine learning and vehicle trajectory data[J]. Journal of Advanced Transportation，2019，2019：9085238.

[19] CHEN Jie，WU Zhongcheng，ZHANG Jun. Driving safety risk prediction using cost-sensitive with nonnegativity-constrained autoencoders based on imbalanced naturalistic driving data[J]. IEEE Transactions on Intelligent Transportation Systems，2019，20(12)：4450−4465.

[20] PEREIRA S，PINTO A，ALVES V，et al. Brain tumor segmentation using convolutional neural networks in MRI images[J]. IEEE Transactions on Medical Imaging，2016，35(5)：1240−1251.

[21] 于子叶，储日升，盛敏汉. 深度神经网络拾取地震P和S波到时[J]. 地球物理学报，2018，61(12)：4873−4886.

YU Ziye，CHU Risheng，SHENG Minhan. Pick onset time of P and S phase by deep neural network[J]. Chinese Journal of Geophysics，2018，61(12)：4873−4886.

[22] ROSS Z E，YUE Yisong，MEIER M A，et al. PhaseLink：A deep learning approach to seismic phase association[J]. Journal of Geophysical Research：Solid Earth，2019，124(1)：856−869.

[23] 许祥，邹志辉，韩明亮，等. 联合地震初至走时与早至波形的深度学习速度建模[J]. 地球物理学报，2023，66(12)：5107−5122.

XU Xiang，ZOU Zhihui，HAN Mingliang，et al. Deep-learning velocity model building by jointly using seismic first arrivals and early-arrival waveforms[J]. Chinese Journal of Geophysics，2023，66(12)：5107−5122.

[24] DUAN Xudong，ZHANG Jie. Reflection residual statics estimated using a high-resolution neural network[J]. Geophysics，2022，87(4)：U121−U134.

[25] CHOI Y，JO Y，SEOL S J，et al. Deep learning spectral enhancement considering features of seismic field data[J]. Geophysics，2021，86(5)：V389−V408.

[26] CHEVITARESE D，SZWARCMAN D，SILVA R M D，et al. Seismic facies segmentation using deep learning[C]//2018 AAPG Annual Convention & Exhibition. Salt Lake City，Utah，USA. Tulsa，OK，USA：American Association of Petroleum Geologists，2018：1–16.

[27] ZHANG Guoyin，WANG Zhizhang，CHEN Yangkang. Deep learning for seismic lithology prediction[J]. Geophysical Journal International，2018，215(2)：1368−1387.

[28] YUAN Sanyi，LIU Jiwei，WANG Shangxu，et al. Seismic waveform classification and first-break picking using convolution neural networks[J]. IEEE Geoscience and Remote Sensing Letters，2018，15(2)：272−276.

[29] WU Xinming，LIANG Luming，SHI Yunzhi，et al. FaultSeg3D：Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation[J]. Geophysics，2019，84(3)：IM35−IM45.

[30] ZHANG Yinxue，TIAN Xuemin，DENG Xiaogang，et al. Seismic denoising based on modified BP neural network[C]//2010 Sixth International Conference on Natural Computation. Yantai，China. IEEE，2010：1825–1829.

[31] KLOCHIKHINA E，CRAWLEY S，FROLOV S，et al. Leveraging deep learning for seismic image denoising[J]. First Break，2020，38(7)：41−48.

[32] ZHANG Kai，ZUO Wangmeng，CHEN Yunjin，et al. Beyond a Gaussian denoiser：Residual learning of deep CNN for image denoising[J]. IEEE Transactions on Image Processing，2017，26(7)：3142−3155.

[33] TRICKETT S. F-xy Cadzow noise suppression[C]//SEG Technical Program Expanded Abstracts 2008. Society of Exploration Geophysicists，2008：2586–2590.

[34] FEHMERS G C，HÖCKER C F W. Fast structural interpretation with structure-oriented filtering[J]. Geophysics，2003，68(4)：1286.