{"title":"3-D UXSE-Net for Seismic Channel Detection Based on Satellite Image Enhanced Synthetic Datasets","authors":"Xinke Zhang;Yihuai Lou;Naihao Liu;Daosheng Ling;Yunmin Chen","doi":"10.1109/JSTARS.2025.3550578","DOIUrl":null,"url":null,"abstract":"Channels are essential indicators of sedimentary environments and play a vital role in geological applications, such as hydrocarbon exploration, sediment transport, and the study of ancient river geomorphology. Deep learning (DL) techniques have shown great potential in improving channel detection accuracy and efficiency. However, insufficient labeled training data remains a key challenge for refining DL models. To address this issue, we propose a workflow that automatically generates synthetic datasets by integrating channel features extracted from high-resolution satellite images. We first extract river channel features and grayscale values from satellite images. These extracted features are then used to construct reflectivity models, incorporating structural deformations based on seismic reflector dips. The reflectivity models are subsequently convolved with wavelets to generate synthetic datasets. These synthetic datasets are used to train the proposed 3-D UXSE-Net, which integrates the 3-D UX-Net architecture with the squeeze-and-excitation blocks. The model generates improved feature representations that enhance performance by combining convolutional neural networks for local feature extraction and Transformer-based modules for capturing global context. We validate our approach by applying the model to both synthetic and 3-D field seismic datasets. Our results show that 3-D UXSE-Net outperforms baseline methods, including the coherence-based approach and other DL models, and demonstrates strong generalization to field data even when trained solely on synthetic data. Comparisons of different methods highlight the effectiveness of the synthetic data generation approach for DL model training.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8300-8311"},"PeriodicalIF":4.7000,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10923698","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10923698/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Channels are essential indicators of sedimentary environments and play a vital role in geological applications, such as hydrocarbon exploration, sediment transport, and the study of ancient river geomorphology. Deep learning (DL) techniques have shown great potential in improving channel detection accuracy and efficiency. However, insufficient labeled training data remains a key challenge for refining DL models. To address this issue, we propose a workflow that automatically generates synthetic datasets by integrating channel features extracted from high-resolution satellite images. We first extract river channel features and grayscale values from satellite images. These extracted features are then used to construct reflectivity models, incorporating structural deformations based on seismic reflector dips. The reflectivity models are subsequently convolved with wavelets to generate synthetic datasets. These synthetic datasets are used to train the proposed 3-D UXSE-Net, which integrates the 3-D UX-Net architecture with the squeeze-and-excitation blocks. The model generates improved feature representations that enhance performance by combining convolutional neural networks for local feature extraction and Transformer-based modules for capturing global context. We validate our approach by applying the model to both synthetic and 3-D field seismic datasets. Our results show that 3-D UXSE-Net outperforms baseline methods, including the coherence-based approach and other DL models, and demonstrates strong generalization to field data even when trained solely on synthetic data. Comparisons of different methods highlight the effectiveness of the synthetic data generation approach for DL model training.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.