Seismic Layer Segmentation Models with Channel Attention Block in Carbon Storage Study
Authors: 
Isack Farady, Chia-Chen Kuo, Sang Le, Chih-Wei Wang, Hui-Fuang Ng, Chih-Yang Lin, Ming-Jen WangAuthors Info & Claims
Pages 1 - 6
Abstract
Carbon capture technology is crucial for mitigating climate change and achieving the Paris Agreement's goal of limiting global warming below 1.5 degrees. Seismic data analysis, including facies layers, plays a vital role in assessing carbon capture and storage (CCS) potential by revealing subsurface geological structures. However, manual interpreting seismic images remains a labor-intensive, expert-driven task. To address this, automated seismic layer segmentation using deep learning techniques has gained traction, but improving segmentation accuracy remains a challenge. This work proposes a novel small attention block that leverages spatial learning paths for segmentation models like U-Net and BiSeNet. This block dynamically assigns weights to different channels within a feature map, enhancing segmentation performance on seismic layer datasets. We validate the effectiveness of our method by demonstrating improved performance on two benchmark datasets: TGS salt layer and SEG facies layer. Our findings reveal that integrating our lightweight attention mechanism can yield smoother segmentation results on seismic layers. This study is part of the ongoing effort in CCS research by focusing on improved segmentation accuracy of seismic data.
References
[1]
Mustafa Alfarhan, Mohamed Deriche, and Ahmed Maalej. 2022. Robust concurrent detection of salt domes and faults in seismic surveys using an improved UNet architecture. IEEE Access 10 (2022), 39424--39435.
[2]
Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang. 2022. Swin-unet: Unet-like pure transformer for medical image segmentation. In European conference on computer vision. Springer, 205--218.
[3]
Satinder Chopra and Kurt J Marfurt. 2005. Seismic attributes---A historical perspective. Geophysics 70, 5 (2005), 3SO--28SO.
[4]
Foivos I Diakogiannis, François Waldner, Peter Caccetta, and Chen Wu. 2020. ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data. ISPRS Journal of Photogrammetry and Remote Sensing 162 (2020), 94--114.
[5]
Kai Gao, Lianjie Huang, and Yingcai Zheng. 2022. Fault detection on seismic structural images using a nested residual U-Net. IEEE Transactions on Geoscience and Remote Sensing 60 (2022), 1--15.
[6]
Shijie Hao, Yuan Zhou, and Yanrong Guo. 2020. A brief survey on semantic segmentation with deep learning. Neurocomputing 406 (2020), 302--321.
[7]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770--778.
[8]
Jie Hu, Li Shen, and Gang Sun. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 7132--7141.
[9]
Samina Khalid, Tehmina Khalil, and Shamila Nasreen. 2014. A survey of feature selection and feature extraction techniques in machine learning. In 2014 Science and Information Conference. IEEE, 372--378.
[10]
Ailiang Lin, Bingzhi Chen, Jiayu Xu, Zheng Zhang, Guangming Lu, and David Zhang. 2022. Ds-transunet: Dual swin transformer u-net for medical image segmentation. IEEE Transactions on Instrumentation and Measurement 71 (2022), 1--15.
[11]
Shervin Minaee, Yuri Boykov, Fatih Porikli, Antonio Plaza, Nasser Kehtarnavaz, and Demetri Terzopoulos. 2021. Image segmentation using deep learning: A survey. IEEE transactions on pattern analysis and machine intelligence 44, 7 (2021), 3523--3542.
[12]
S Mostafa Mousavi and Gregory C Beroza. 2022. Deep-learning seismology. Science 377, 6607 (2022), eabm4470.
[13]
Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y Hammerla, Bernhard Kainz, et al. 2018. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018).
[14]
New Zealand Petroleum and Minerals (NZPM). 2020. 2020 SEG Annual Meeting Machine Learning Interpretation Workshop. Retrieved January 20, 2024 from https://public.3.basecamp.com/p/JyT276MM7krjYrMoLqLQ6xST
[15]
Erion-Vasilis Pikoulis and Emmanouil Z. Psarakis. 2015. Automatic seismic signal detection via record segmentation. IEEE Transactions on Geoscience and Remote Sensing 53, 7 (2015), 3870--2884.
[16]
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention-MICCAI 2015. Springer, 234--241.
[17]
Swalpa Kumar Roy, Shiv Ram Dubey, Subhrasankar Chatterjee, and Bidyut Baran Chaudhuri. 2020. FuSENet: fused squeeze-and-excitation network for spectral-spatial hyperspectral image classification. IET Image Processing 14, 8 (2020), 1653--1661.
[18]
Jonny Rutqvist, Antonio P. Rinaldi, Frederic Cappa, Pierre Jeanne, Alberto Mazzoldi, Luca Urpi, Yves Guglielmi, and Victor Vilarrasa. 2016. Fault activation and induced seismicity in geological carbon storage - Lessons learned from recent modeling studies. Journal of Rock Mechanics and Geotechnical Engineering 8, 6 (2016), 789--804.
[19]
Robert R Stewart, James E Gaiser, R James Brown, and Don C Lawton. 2002. Converted-wave seismic exploration: Methods. Geophysics 67, 5 (2002), 1348--1363.
[20]
TGS. 2018. TGS Salt Identification Challenge. Retrieved November 19, 2023 from https://www.kaggle.com/competitions/tgs-salt-identification-challenge/data
[21]
UNFCCC. 2015. Adoption of the Paris Agreement FCCC/CP/2015/L.9/Rev.1 United Nations Framework Convention on Climate Change. Retrieved March 19, 2024 from https://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf.
[22]
Manuel Blanco Valentín, Clécio R Bom, Juliana M Coelho, Maury Duarte Correia, Márcio P De Albuquerque, Marcelo P de Albuquerque, and Elisângela L Faria. 2019. A deep residual convolutional neural network for automatic lithological facies identification in Brazilian pre-salt oilfield wellbore image logs. Journal of Petroleum Science and Engineering 179 (2019), 474--503.
[23]
Benfeng Wang, Ning Zhang, Wenkai Lu, and Jialin Wang. 2019. Deep-learning-based seismic data interpolation: A preliminary result. Geophysics 84, 1 (2019), V11--V20.
[24]
Xinming Wu, Luming Liang, Yunzhi Shi, and Sergey Fomel. 2019. FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation. Geophysics 84, 3 (2019), IM35--IM45.
[25]
Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, and Nong Sang. 2018. Bisenet: Bilateral segmentation network for real-time semantic segmentation. In Proceedings of the European conference on computer vision (ECCV). 325--341.
[26]
Yuxi Zhang, Yang Liu, Haoran Zhang, and Hao Xue. 2019. Seismic facies analysis based on deep learning. IEEE Geoscience and Remote Sensing Letters 17, 7 (2019), 1119--1123.
[27]
Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. 2019. Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39, 6 (2019), 1856--1867.
Index Terms
- Seismic Layer Segmentation Models with Channel Attention Block in Carbon Storage Study
Recommendations
Effects of spatial and temporal climatic variability on terrestrial carbon and water fluxes in the Pacific Northwest, USA
The Pacific Northwest (PNW) of the conterminous United States is characterized by large variations in climate and topography, and provides an ideal geographic domain for studying interactions between regional climate and vegetation dynamics. We examined ...
A robust absorbing layer method for anisotropic seismic wave modeling
When applied to wave propagation modeling in anisotropic media, Perfectly Matched Layers (PML) exhibit instabilities. Incoming waves are amplified instead of being absorbed. Overcoming this difficulty is crucial as in many seismic imaging applications, ...
A spatial squeeze and multimodal feature fusion attention network for multiple tumor segmentation from PET–CT Volumes
AbstractTumor segmentation is a key step in computer-aided diagnosis. The PET–CT co-segmentation method combines the high sensitivity of PET images and the anatomical information of CT images. For whole-body multiple tumors, such as soft tissue sarcoma, ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
June 2024
30 pages
ISBN:9798400706592
DOI:10.1145/3661813
This work is licensed under a Creative Commons Attribution International 4.0 License.
Sponsors
In-Cooperation
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 03 June 2024
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
EnvSys '24
Sponsor:
EnvSys '24: 2nd Workshop on Advances in Environmental Sensing Systems for Smart Cities
June 3 - 7, 2024
Minato-ku, Tokyo, Japan
Acceptance Rates
Overall Acceptance Rate 7 of 8 submissions, 88%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 26 Sep 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in