Pub Date : 2024-11-01DOI: 10.1016/j.cageo.2024.105750
Daesoo Lee , Oscar Ovanger , Jo Eidsvik , Erlend Aune , Jacob Skauvold , Ragnar Hauge
Creating accurate and geologically realistic reservoir facies based on limited measurements is crucial for field development and reservoir management, especially in the oil and gas sector. Traditional two-point geostatistics, while foundational, often struggle to capture complex geological patterns. Multi-point statistics offers more flexibility, but comes with its own challenges related to pattern configurations and storage limits. With the rise of Generative Adversarial Networks (GANs) and their success in various fields, there has been a shift towards using them for facies generation. However, recent advances in the computer vision domain have shown the superiority of diffusion models over GANs. Motivated by this, a novel Latent Diffusion Model is proposed, which is specifically designed for conditional generation of reservoir facies. The proposed model produces high-fidelity facies realizations that rigorously preserve conditioning data. It significantly outperforms a GAN-based alternative. Our implementation on GitHub: github.com/ML4ITS/Latent-Diffusion-Model-for-Conditional-Reservoir-Facies-Generation
在有限的测量基础上创建准确且符合地质实际的储层面对于油田开发和储层管理至关重要，尤其是在石油和天然气领域。传统的两点地质统计虽然具有基础性，但往往难以捕捉复杂的地质模式。多点统计提供了更大的灵活性，但也面临着与模式配置和存储限制相关的挑战。随着生成对抗网络（GANs）的兴起及其在各个领域的成功应用，人们开始将其用于地貌生成。然而，计算机视觉领域的最新进展表明，扩散模型优于 GANs。受此启发，我们提出了一种新颖的潜在扩散模型，该模型专为有条件生成储层剖面而设计。该模型可生成高保真的储层面，并严格保留条件数据。它明显优于基于 GAN 的替代方法。我们在 GitHub 上的实现：github.com/ML4ITS/Latent-Diffusion-Model-for-Conditional-Reservoir-Facies-Generation
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Pub Date : 2024-11-01DOI: 10.1016/j.cageo.2024.105747
Paulo Henrique Ranazzi , Xiaodong Luo , Marcio Augusto Sampaio
In the past years, there is a growing interest in the applications of Generative Adversarial Networks (GANs) to generate geological models. Although GANs have proven to be an effective tool to learn and reproduce the complex data patterns present in some geological models, some challenges still remain open. Among others, a well-noticed problem is the need for a large number of samples to ensure high-quality training, which can be prohibitively expensive in some cases. As an attempt to offer a (possibly partial) solution to the aforementioned challenge, in this study, we investigate the feasibility and effectiveness of a zero-centered discriminator regularization technique for improving the performance of a GAN. Additionally, we evaluate an adaptive data augmentation technique to overcome the potential issue of limited training data, for the purpose of generating geologically feasible realizations of hydrocarbon reservoir models. Our findings demonstrate that a combination of the two techniques lead to notable performance improvements of a GAN. Particularly, it is observed that using the adaptive data augmentation technique in a GAN can yield similar results to those obtained by the GAN with a much larger dataset.
过去几年中，人们对应用生成对抗网络（GANs）生成地质模型的兴趣与日俱增。尽管 GANs 已被证明是学习和重现某些地质模型中复杂数据模式的有效工具，但仍存在一些挑战。其中，一个广受关注的问题是需要大量样本来确保高质量的训练，而这在某些情况下可能会过于昂贵。为了尝试为上述挑战提供一种（可能是部分）解决方案，我们在本研究中探讨了零中心判别器正则化技术在提高 GAN 性能方面的可行性和有效性。此外，我们还评估了一种自适应数据增强技术，以克服训练数据有限的潜在问题，从而生成地质上可行的油气藏模型。我们的研究结果表明，这两种技术的结合可以显著提高 GAN 的性能。特别是，我们观察到，在一个 GAN 中使用自适应数据增强技术，可以获得与使用更大数据集的 GAN 类似的结果。
{"title":"Improving the training performance of generative adversarial networks with limited data: Application to the generation of geological models","authors":"Paulo Henrique Ranazzi , Xiaodong Luo , Marcio Augusto Sampaio","doi":"10.1016/j.cageo.2024.105747","DOIUrl":"10.1016/j.cageo.2024.105747","url":null,"abstract":"<div><div>In the past years, there is a growing interest in the applications of Generative Adversarial Networks (GANs) to generate geological models. Although GANs have proven to be an effective tool to learn and reproduce the complex data patterns present in some geological models, some challenges still remain open. Among others, a well-noticed problem is the need for a large number of samples to ensure high-quality training, which can be prohibitively expensive in some cases. As an attempt to offer a (possibly partial) solution to the aforementioned challenge, in this study, we investigate the feasibility and effectiveness of a zero-centered discriminator regularization technique for improving the performance of a GAN. Additionally, we evaluate an adaptive data augmentation technique to overcome the potential issue of limited training data, for the purpose of generating geologically feasible realizations of hydrocarbon reservoir models. Our findings demonstrate that a combination of the two techniques lead to notable performance improvements of a GAN. Particularly, it is observed that using the adaptive data augmentation technique in a GAN can yield similar results to those obtained by the GAN with a much larger dataset.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"193 ","pages":"Article 105747"},"PeriodicalIF":4.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.cageo.2024.105737
Zhiyu Lin , Jianliang Xie , Yao Tang , Jianghua Ran , Yongbin Tan
Terrain factor is an important factor affecting soil erosion, in which slope length is an important indicator of terrain factor. In this paper, we model the regional topographic relief with triangulated irregular network TIN, use the slope aspect of the TIN triangular surface to determine the flow direction, and propose an algorithm (RT-WFP) for extracting the slope length by tracing the water flow trajectory in reverse direction. The slope cutoff point rule is set in the algorithm to improve the accuracy of the slope length extraction results. We calculated the slope length of the experimental area of the small watershed of Golden Hook-shaped collapsing hill in Bailu Township, Ganxian District, and the rationality of the algorithm proposed in this paper is verified through the comparison and analysis with the traditional slope length extraction algorithm. The experimental results show that, compared with the traditional D8 algorithm, the water flow path extracted by this algorithm in the experimental area more closely matches the water flow path based on contour mapping, and the slope length extracted by this algorithm has a lower sensitivity to the resolution of the data, and the percentage of cells with a slope length value of no more than 300 m (the limit standard of the RUSLE model) at a resolution of 30 m reaches 94.19%.
地形因素是影响水土流失的重要因素，其中坡长是地形因素的重要指标。本文利用三角形不规则网络 TIN 对区域地形起伏进行建模，利用 TIN 三角形面的坡度来确定水流方向，并提出了一种通过反向追踪水流轨迹来提取坡长的算法（RT-WFP）。算法中设置了坡度截点规则，以提高坡长提取结果的准确性。我们计算了赣县白鹿镇金钩形塌陷山小流域实验区的坡长，通过与传统坡长提取算法的对比分析，验证了本文提出的算法的合理性。实验结果表明，与传统的 D8 算法相比，该算法在实验区提取的水流路径与基于等高线图的水流路径更加吻合，且该算法提取的坡长对数据分辨率的敏感性更低，在分辨率为 30 m 时，坡长值不大于 300 m（RUSLE 模型的极限标准）的单元占比达到 94.19%。
{"title":"A reverse tracing of the water flow path algorithm for slope length extraction based on triangulated irregular network","authors":"Zhiyu Lin , Jianliang Xie , Yao Tang , Jianghua Ran , Yongbin Tan","doi":"10.1016/j.cageo.2024.105737","DOIUrl":"10.1016/j.cageo.2024.105737","url":null,"abstract":"<div><div>Terrain factor is an important factor affecting soil erosion, in which slope length is an important indicator of terrain factor. In this paper, we model the regional topographic relief with triangulated irregular network TIN, use the slope aspect of the TIN triangular surface to determine the flow direction, and propose an algorithm (RT-WFP) for extracting the slope length by tracing the water flow trajectory in reverse direction. The slope cutoff point rule is set in the algorithm to improve the accuracy of the slope length extraction results. We calculated the slope length of the experimental area of the small watershed of Golden Hook-shaped collapsing hill in Bailu Township, Ganxian District, and the rationality of the algorithm proposed in this paper is verified through the comparison and analysis with the traditional slope length extraction algorithm. The experimental results show that, compared with the traditional D8 algorithm, the water flow path extracted by this algorithm in the experimental area more closely matches the water flow path based on contour mapping, and the slope length extracted by this algorithm has a lower sensitivity to the resolution of the data, and the percentage of cells with a slope length value of no more than 300 m (the limit standard of the RUSLE model) at a resolution of 30 m reaches 94.19%.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"193 ","pages":"Article 105737"},"PeriodicalIF":4.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-31DOI: 10.1016/j.cageo.2024.105764
Xiaoxu Dong , Yu Peng , Wenjing Li , Ying Liang , Yu Wang , Zheng Zeng
In this paper, the elastic function in the explanation of elastic outer boundary condition is regarded as polynomial functions of space variable and time variable , and this is incorporated into the analysis of fractal composite reservoirs. The Laplace space solution the fractal composite reservoir models, which have polynomial elastic outer boundary conditions, is achieved through a modified method of similarity construction and the Gaver-Stehfest numerical inversion technique is used to derive the semi-analytical solutions for the models in actual space. Next, the polynomial elastic function is turned into a first-order function about time variable. Curves of pressure in non-dimensional well bottom under different quadratic pressure gradient terms and primary control factors are drawn by using MATLAB software and their impact on non-dimensional well bottom are analyzed. It is proved that the three impractical outer boundary conditions are only a particular case of the polynomial elastic outer boundary conditions. The research in this paper expands the discussion scope of elastic outer boundary conditions, and has strong reference significance.
本文将弹性外边界条件解释中的弹性函数视为空间变量 r 和时间变量 t 的多项式函数，并将其纳入分形复合储层的分析中。通过改进的相似性构造方法实现了具有多项式弹性外边界条件的分形复合储层模型的拉普拉斯空间解，并利用 Gaver-Stehfest 数值反演技术得出了模型在实际空间的半解析解。然后，将多项式弹性函数转化为关于时间变量的一阶函数。利用 MATLAB 软件绘制了不同二次压力梯度项和主控因素下的非三维井底压力曲线，并分析了它们对非三维井底的影响。结果证明，三种不切实际的外边界条件只是多项式弹性外边界条件的一种特殊情况。本文的研究拓展了弹性外边界条件的讨论范围，具有很强的借鉴意义。
{"title":"Application of polynomial type elastic outer boundary conditions in fractal composite reservoir seepage model","authors":"Xiaoxu Dong , Yu Peng , Wenjing Li , Ying Liang , Yu Wang , Zheng Zeng","doi":"10.1016/j.cageo.2024.105764","DOIUrl":"10.1016/j.cageo.2024.105764","url":null,"abstract":"<div><div>In this paper, the elastic function in the explanation of elastic outer boundary condition is regarded as polynomial functions of space variable <span><math><mrow><mi>r</mi></mrow></math></span> and time variable <span><math><mrow><mi>t</mi></mrow></math></span>, and this is incorporated into the analysis of fractal composite reservoirs. The Laplace space solution the fractal composite reservoir models, which have polynomial elastic outer boundary conditions, is achieved through a modified method of similarity construction and the Gaver-Stehfest numerical inversion technique is used to derive the semi-analytical solutions for the models in actual space. Next, the polynomial elastic function is turned into a first-order function about time variable. Curves of pressure in non-dimensional well bottom under different quadratic pressure gradient terms and primary control factors are drawn by using MATLAB software and their impact on non-dimensional well bottom are analyzed. It is proved that the three impractical outer boundary conditions are only a particular case of the polynomial elastic outer boundary conditions. The research in this paper expands the discussion scope of elastic outer boundary conditions, and has strong reference significance.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105764"},"PeriodicalIF":4.2,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1016/j.cageo.2024.105743
Xinyuan Zhu , Kewen Li , Zhixuan Yang , Zhaohui Li
As deep learning becomes increasingly prevalent in seismic impedance inversion, 3D data-driven approaches have garnered substantial interest. However, two critical challenges persist. First, existing methodologies predominantly rely on Convolutional Neural Networks (CNNs), which, due to the inherent locality of convolutional operations, are inadequate in capturing the global context of seismic data. This limitation notably hinders their performance in inverting complex subsurface structures, such as salt bodies. Second, the current inversion frameworks are prone to overfitting, particularly when trained on limited seismic datasets. To address these challenges, we propose SwinInver, a novel backbone network that integrates the Swin Transformer as its fundamental unit, coupled with a high-resolution network design to facilitate comprehensive global modeling of intricate subsurface structures. Furthermore, we incorporate adversarial training to enhance the inversion process and effectively mitigate overfitting. Experimental evaluations demonstrate that SwinInver significantly surpasses conventional CNN-based approaches in both synthetic and field data scenarios, providing a more accurate and reliable framework for seismic impedance inversion.
{"title":"SwinInver: 3D data-driven seismic impedance inversion based on Swin Transformer and adversarial training","authors":"Xinyuan Zhu , Kewen Li , Zhixuan Yang , Zhaohui Li","doi":"10.1016/j.cageo.2024.105743","DOIUrl":"10.1016/j.cageo.2024.105743","url":null,"abstract":"<div><div>As deep learning becomes increasingly prevalent in seismic impedance inversion, 3D data-driven approaches have garnered substantial interest. However, two critical challenges persist. First, existing methodologies predominantly rely on Convolutional Neural Networks (CNNs), which, due to the inherent locality of convolutional operations, are inadequate in capturing the global context of seismic data. This limitation notably hinders their performance in inverting complex subsurface structures, such as salt bodies. Second, the current inversion frameworks are prone to overfitting, particularly when trained on limited seismic datasets. To address these challenges, we propose SwinInver, a novel backbone network that integrates the Swin Transformer as its fundamental unit, coupled with a high-resolution network design to facilitate comprehensive global modeling of intricate subsurface structures. Furthermore, we incorporate adversarial training to enhance the inversion process and effectively mitigate overfitting. Experimental evaluations demonstrate that SwinInver significantly surpasses conventional CNN-based approaches in both synthetic and field data scenarios, providing a more accurate and reliable framework for seismic impedance inversion.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105743"},"PeriodicalIF":4.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-28DOI: 10.1016/j.cageo.2024.105745
Kaili Liu , Jianmeng Sun , Han Wu , Xin Luo , Fujing Sun
Microscopic pore structure forms the foundation for studying shale gas adsorption and transport mechanisms and for establishing geological models. However, most current methods for analyzing microporous structure through physical experiments are time-consuming and labor-intensive. Hence, there is a need to automate pore segmentation and extract pore microstructural information from shale SEM images quickly and accurately. This will significantly enhance the efficiency of digital rock analysis and related computational simulations. This study used scanning electron microscopy (SEM) images of shale from a certain region in China to investigate the relationship between the microscopic structure of shale pores and the macroscopic permeability. Firstly, a semantic image segmentation model called TransUnet, based on deep learning, was used to segment the pore images and extract the micro-pore structure parameters. Then, the relationship between the macroscopic permeability parameters and the micro-pore structure was analyzed using a fractal apparent permeability calculation model. Finally, the permeability of the shale was calculated to improve the efficiency of geological exploration and reduce experimental costs. The experimental results show that this study provides an effective image processing method for the SEM quantification of shale microstructure and extraction of permeability parameters.
微观孔隙结构是研究页岩气吸附和传输机制以及建立地质模型的基础。然而，目前通过物理实验分析微孔结构的方法大多耗时耗力。因此，需要从页岩扫描电镜图像中快速、准确地自动进行孔隙分割并提取孔隙微观结构信息。这将大大提高数字岩石分析和相关计算模拟的效率。本研究利用中国某地区页岩的扫描电子显微镜（SEM）图像，研究页岩孔隙微观结构与宏观渗透率之间的关系。首先，利用基于深度学习的语义图像分割模型 TransUnet 对孔隙图像进行分割并提取微观孔隙结构参数。然后，利用分形表观渗透率计算模型分析了宏观渗透率参数与微孔结构之间的关系。最后，计算出页岩的渗透率，从而提高地质勘探效率，降低实验成本。实验结果表明，本研究为 SEM 定量页岩微观结构和提取渗透率参数提供了一种有效的图像处理方法。
{"title":"Shale sample permeability estimation using fractal parameters computed from TransUnet-based SEM image segmentation","authors":"Kaili Liu , Jianmeng Sun , Han Wu , Xin Luo , Fujing Sun","doi":"10.1016/j.cageo.2024.105745","DOIUrl":"10.1016/j.cageo.2024.105745","url":null,"abstract":"<div><div>Microscopic pore structure forms the foundation for studying shale gas adsorption and transport mechanisms and for establishing geological models. However, most current methods for analyzing microporous structure through physical experiments are time-consuming and labor-intensive. Hence, there is a need to automate pore segmentation and extract pore microstructural information from shale SEM images quickly and accurately. This will significantly enhance the efficiency of digital rock analysis and related computational simulations. This study used scanning electron microscopy (SEM) images of shale from a certain region in China to investigate the relationship between the microscopic structure of shale pores and the macroscopic permeability. Firstly, a semantic image segmentation model called TransUnet, based on deep learning, was used to segment the pore images and extract the micro-pore structure parameters. Then, the relationship between the macroscopic permeability parameters and the micro-pore structure was analyzed using a fractal apparent permeability calculation model. Finally, the permeability of the shale was calculated to improve the efficiency of geological exploration and reduce experimental costs. The experimental results show that this study provides an effective image processing method for the SEM quantification of shale microstructure and extraction of permeability parameters.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105745"},"PeriodicalIF":4.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1016/j.cageo.2024.105742
Hadi Farhadi , Hamid Ebadi , Abbas Kiani , Ali Asgary
Accurate surface water detection and mapping using Remote Sensing (RS) imagery is crucial for effective water and flood management and for supporting natural ecosystems and human development. In recent years, RS technology and satellite image processing have significantly advanced in flood and permanent water extraction, particularly in water index, clustering, classification, and sub-pixel analysis. Water-index-based techniques, distinguished by their quickness and convenience, offer notable advantages. The dynamic and extensive nature of surface water and flooded areas make the water index particularly effective for monitoring large areas. However, challenges arise due to the complexity of ground surfaces in aquatic environments, including shadows in built-up, vegetated, and mountainous regions, narrow water bodies, and muddy water. This research presents a new Flood Mapping Index using Sentinel-2 imagery (SFMI) designed to address these challenges and identify water and flooded areas more accurately. The SFMI utilizes visible and near-infrared bands derived from Sentinel-2 data, employing 10-m bands to compensate for errors arising from spectral and spatial changes more effectively. The SFMI index is designed based on the spectral signatures of various land cover classes, utilizing the potential of 10-m resolution bands to identify water bodies and flood areas. Unlike the most conventional methods, the SFMI identifies and extracts water and flood regions without complex thresholding, and thus mitigates the impact of irrelevant features, such as dense vegetation and rugged topography on the flood and water body maps. The proposed index was tested in two large areas with high spectral diversity, yielding promising results. The SFMI index demonstrates an average overall accuracy of 97.1% for pre-flood water extraction, 97.95% for post-flood water extraction, and 98% for flooded area extraction. Moreover, the results showed an average kappa coefficient of 0.958 for pre-flood water extraction, 0.965 for post-flood water extraction, and 0.978 for flooded area extraction. The performance of the SFMI index for extracting flooded areas (ΔSFMI) is superior to its performance for water extraction both before and after the flood. However, it is essential to note that the accuracy of the flooded area map is contingent on the accuracy of the water area map both before and after the flood. Thus, the SFMI index based on 10-m Sentinel-2 imagery accurately detects floods and water bodies over time, without relying on thresholding, making it suitable for flood management and monitoring various water bodies like dams, lakes, wetlands, and rivers. The findings underscore the applicability of the proposed SFMI index in diverse and spectrally rich areas, demonstrating its effectiveness in monitoring various surface water bodies, detecting floods, and managing flood crises.
{"title":"Introducing a new index for flood mapping using Sentinel-2 imagery (SFMI)","authors":"Hadi Farhadi , Hamid Ebadi , Abbas Kiani , Ali Asgary","doi":"10.1016/j.cageo.2024.105742","DOIUrl":"10.1016/j.cageo.2024.105742","url":null,"abstract":"<div><div>Accurate surface water detection and mapping using Remote Sensing (RS) imagery is crucial for effective water and flood management and for supporting natural ecosystems and human development. In recent years, RS technology and satellite image processing have significantly advanced in flood and permanent water extraction, particularly in water index, clustering, classification, and sub-pixel analysis. Water-index-based techniques, distinguished by their quickness and convenience, offer notable advantages. The dynamic and extensive nature of surface water and flooded areas make the water index particularly effective for monitoring large areas. However, challenges arise due to the complexity of ground surfaces in aquatic environments, including shadows in built-up, vegetated, and mountainous regions, narrow water bodies, and muddy water. This research presents a new Flood Mapping Index using Sentinel-2 imagery (SFMI) designed to address these challenges and identify water and flooded areas more accurately. The SFMI utilizes visible and near-infrared bands derived from Sentinel-2 data, employing 10-m bands to compensate for errors arising from spectral and spatial changes more effectively. The SFMI index is designed based on the spectral signatures of various land cover classes, utilizing the potential of 10-m resolution bands to identify water bodies and flood areas. Unlike the most conventional methods, the SFMI identifies and extracts water and flood regions without complex thresholding, and thus mitigates the impact of irrelevant features, such as dense vegetation and rugged topography on the flood and water body maps. The proposed index was tested in two large areas with high spectral diversity, yielding promising results. The SFMI index demonstrates an average overall accuracy of 97.1% for pre-flood water extraction, 97.95% for post-flood water extraction, and 98% for flooded area extraction. Moreover, the results showed an average kappa coefficient of 0.958 for pre-flood water extraction, 0.965 for post-flood water extraction, and 0.978 for flooded area extraction. The performance of the SFMI index for extracting flooded areas (ΔSFMI) is superior to its performance for water extraction both before and after the flood. However, it is essential to note that the accuracy of the flooded area map is contingent on the accuracy of the water area map both before and after the flood. Thus, the SFMI index based on 10-m Sentinel-2 imagery accurately detects floods and water bodies over time, without relying on thresholding, making it suitable for flood management and monitoring various water bodies like dams, lakes, wetlands, and rivers. The findings underscore the applicability of the proposed SFMI index in diverse and spectrally rich areas, demonstrating its effectiveness in monitoring various surface water bodies, detecting floods, and managing flood crises.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105742"},"PeriodicalIF":4.2,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1016/j.cageo.2024.105744
Chengyang Han , Guangui Zou , Hen-Geul Yeh , Fei Gong , Suzhen Shi , Hao Chen
Fault prediction in coal mining is crucial for safety, and recent technological advancements are steering this field towards supervised intelligent interpretation, moving beyond traditional human-machine interaction. Currently, support vector machine (SVM) predictions often rely on seismic attribute data; however, the poor quality of some fault data characteristics hampers their predictive capability. To localize the fault based on original seismic data and improve SVM prediction we propose the W-SVM algorithm, which integrates wavelet transform and SVM. Through wavelet transform, we localize fault features in seismic data, which are then used for SVM prediction. Validation using real data confirms the feasibility of the W-SVM approach. The W-SVM model successfully identifies 34 known faults. Beyond achieving high prediction accuracy, the model exhibits improved stability and generalization. The difference among the evaluation metrics for training, validation, and testing is within 5%. Moreover, this study localizes the response of faults through wavelet transform, simplifies the dataset preparation process, improves computational efficiency, and increases overall applicability. This advancement further promotes the development of intelligent identification of faults in coal mines.
{"title":"Intelligent fault prediction with wavelet-SVM fusion in coal mine","authors":"Chengyang Han , Guangui Zou , Hen-Geul Yeh , Fei Gong , Suzhen Shi , Hao Chen","doi":"10.1016/j.cageo.2024.105744","DOIUrl":"10.1016/j.cageo.2024.105744","url":null,"abstract":"<div><div>Fault prediction in coal mining is crucial for safety, and recent technological advancements are steering this field towards supervised intelligent interpretation, moving beyond traditional human-machine interaction. Currently, support vector machine (SVM) predictions often rely on seismic attribute data; however, the poor quality of some fault data characteristics hampers their predictive capability. To localize the fault based on original seismic data and improve SVM prediction we propose the W-SVM algorithm, which integrates wavelet transform and SVM. Through wavelet transform, we localize fault features in seismic data, which are then used for SVM prediction. Validation using real data confirms the feasibility of the W-SVM approach. The W-SVM model successfully identifies 34 known faults. Beyond achieving high prediction accuracy, the model exhibits improved stability and generalization. The difference among the evaluation metrics for training, validation, and testing is within 5%. Moreover, this study localizes the response of faults through wavelet transform, simplifies the dataset preparation process, improves computational efficiency, and increases overall applicability. This advancement further promotes the development of intelligent identification of faults in coal mines.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105744"},"PeriodicalIF":4.2,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142560840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1016/j.cageo.2024.105746
P. Anbazhagan, Sauvik Halder
The arrival times of P and S waves, originating from earthquakes, diverse seismic tests, and events, are crucial geotechnical parameters. Derived from the inversion of these travel times, VP (P-wave velocity) and VS (S-wave velocity) are pivotal in geotechnical engineering, correlating directly with dynamic soil properties and enabling calculations of Poisson's Ratio (ν), Young's modulus (E), Shear modulus (μ), and Bulk modulus (B). Both VP and VS are crucial for evaluating soil behaviour under various conditions, aiding in modelling soil for settlement, wave propagation, seismic wave interaction, liquefaction potential analysis, seismic response analysis, and many more. The selection of arrival times for seismic tests, including Crosshole, Downhole, and Uphole tests, is done manually, which is time-consuming and potentially erroneous. To address this issue, various algorithms have been developed to automate the picking process. Some of these algorithms use wavelet transforms and Bayesian information criteria, while others use machine learning techniques such as artificial neural networks. These methods vary in terms of their accuracy, yet each one possesses inherent limitations when it comes to processing data with different levels of signal-to-noise ratio. The advancement of automated algorithms for determining arrival times is an ongoing and dynamic field of research. Apart from the existing research focused on determining the arrival time of P waves, there is a dearth of studies investigating the detection of S wave arrival times. To fill this gap, this study proposes new approaches for detecting both P and S wave arrival time(s). One approach entails the utilization of an iterative optimization algorithm to accurately fit a curve to the leading edge of the P waveform. The arrival time is determined by calculating a fraction relative to the highest point obtained from the fitted peak. The second approach entails identifying the exact moment of the S wave's arrival by determining the points of intersection between the oppositely polarized S waveforms. These methods provide a promising approach for automatically detecting both P and S wave arrival time(s), which has the potential to improve the precision and efficiency in picking up arrival time(s).
源于地震、各种地震试验和事件的 P 波和 S 波的到达时间是至关重要的岩土参数。VP（P 波速度）和 VS（S 波速度）是岩土工程中的关键参数，直接与动态土壤特性相关，可用于计算泊松比 (ν)、杨氏模量 (E)、剪切模量 (μ) 和体积模量 (B)。VP 和 VS 对于评估土壤在各种条件下的行为至关重要，有助于为沉降、波传播、地震波相互作用、液化潜力分析、地震响应分析等方面的土壤建模。地震测试（包括横孔、井下和井上测试）的到达时间选择需要人工完成，既费时又可能出错。为了解决这个问题，人们开发了各种算法来实现挑选过程的自动化。其中一些算法使用小波变换和贝叶斯信息标准，另一些则使用人工神经网络等机器学习技术。这些方法的准确性各不相同，但在处理不同信噪比的数据时，每种方法都有其固有的局限性。用于确定到达时间的自动算法的发展是一个持续且充满活力的研究领域。除了现有的以确定 P 波到达时间为重点的研究外，还缺乏对 S 波到达时间检测的研究。为了填补这一空白，本研究提出了检测 P 波和 S 波到达时间的新方法。其中一种方法是利用迭代优化算法将曲线精确拟合到 P 波的前缘。通过计算与拟合峰值最高点相对的分数来确定到达时间。第二种方法是通过确定相对极化的 S 波形之间的交点来确定 S 波到达的确切时刻。这些方法为自动检测 P 波和 S 波的到达时间提供了一种可行的方法，有可能提高拾取到达时间的精度和效率。
{"title":"A novel algorithm for identifying arrival times of P and S Waves in seismic borehole surveys","authors":"P. Anbazhagan, Sauvik Halder","doi":"10.1016/j.cageo.2024.105746","DOIUrl":"10.1016/j.cageo.2024.105746","url":null,"abstract":"<div><div>The arrival times of P and S waves, originating from earthquakes, diverse seismic tests, and events, are crucial geotechnical parameters. Derived from the inversion of these travel times, V<sub>P</sub> (P-wave velocity) and V<sub>S</sub> (S-wave velocity) are pivotal in geotechnical engineering, correlating directly with dynamic soil properties and enabling calculations of Poisson's Ratio (<strong>ν</strong>), Young's modulus (E), Shear modulus (μ), and Bulk modulus (B). Both V<sub>P</sub> and V<sub>S</sub> are crucial for evaluating soil behaviour under various conditions, aiding in modelling soil for settlement, wave propagation, seismic wave interaction, liquefaction potential analysis, seismic response analysis, and many more. The selection of arrival times for seismic tests, including Crosshole, Downhole, and Uphole tests, is done manually, which is time-consuming and potentially erroneous. To address this issue, various algorithms have been developed to automate the picking process. Some of these algorithms use wavelet transforms and Bayesian information criteria, while others use machine learning techniques such as artificial neural networks. These methods vary in terms of their accuracy, yet each one possesses inherent limitations when it comes to processing data with different levels of signal-to-noise ratio. The advancement of automated algorithms for determining arrival times is an ongoing and dynamic field of research. Apart from the existing research focused on determining the arrival time of P waves, there is a dearth of studies investigating the detection of S wave arrival times. To fill this gap, this study proposes new approaches for detecting both P and S wave arrival time(s). One approach entails the utilization of an iterative optimization algorithm to accurately fit a curve to the leading edge of the P waveform. The arrival time is determined by calculating a fraction relative to the highest point obtained from the fitted peak. The second approach entails identifying the exact moment of the S wave's arrival by determining the points of intersection between the oppositely polarized S waveforms. These methods provide a promising approach for automatically detecting both P and S wave arrival time(s), which has the potential to improve the precision and efficiency in picking up arrival time(s).</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105746"},"PeriodicalIF":4.2,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1016/j.cageo.2024.105740
Qinyang Dai , Liming Zhang , Peng Wang , Kai Zhang , Guodong Chen , Zhangxing Chen , Xiaoming Xue , Jian Wang , Chen Liu , Xia Yan , Piyang Liu , Dawei Wu , Guoyu Qin , Xingyu Liu
In the face of escalating global energy demands, this study introduces an Adaptive Constraint-Guided Surrogate Enhanced Evolutionary Algorithm (ACG-EBS) for optimizing horizontal well placements in oil reservoirs. Addressing the complex challenge of maximizing oil production, the ACG-EBS integrates geological, engineering, and economic considerations into a novel optimization framework. This algorithm stands out for its adept navigation through a complex and discrete decision space of horizontal well placements, an area where traditional methods often encounter challenges. Key innovations include the Adaptive Constraint Initialization Mechanism (ACIM) and the Evolutionary Constraint-Tailored Candidate Refinement strategy (ECTCR), which collectively elevate the feasibility of candidate solutions. An enhanced balance strategy harmonizes comprehensive and niche surrogate models, optimizing the balance between exploration and exploitation. Through testing on both two-dimensional and three-dimensional reservoir models, the ACG-EBS has proven highly effective in identifying optimal well placements that align with field deployment realities and maximize economic returns. This contribution significantly supports the ongoing evolution of oilfield development optimization, showcasing the algorithm's potential to enhance oil production and economic outcomes.
{"title":"Adaptive constraint-guided surrogate enhanced evolutionary algorithm for horizontal well placement optimization in oil reservoir","authors":"Qinyang Dai , Liming Zhang , Peng Wang , Kai Zhang , Guodong Chen , Zhangxing Chen , Xiaoming Xue , Jian Wang , Chen Liu , Xia Yan , Piyang Liu , Dawei Wu , Guoyu Qin , Xingyu Liu","doi":"10.1016/j.cageo.2024.105740","DOIUrl":"10.1016/j.cageo.2024.105740","url":null,"abstract":"<div><div>In the face of escalating global energy demands, this study introduces an Adaptive Constraint-Guided Surrogate Enhanced Evolutionary Algorithm (ACG-EBS) for optimizing horizontal well placements in oil reservoirs. Addressing the complex challenge of maximizing oil production, the ACG-EBS integrates geological, engineering, and economic considerations into a novel optimization framework. This algorithm stands out for its adept navigation through a complex and discrete decision space of horizontal well placements, an area where traditional methods often encounter challenges. Key innovations include the Adaptive Constraint Initialization Mechanism (ACIM) and the Evolutionary Constraint-Tailored Candidate Refinement strategy (ECTCR), which collectively elevate the feasibility of candidate solutions. An enhanced balance strategy harmonizes comprehensive and niche surrogate models, optimizing the balance between exploration and exploitation. Through testing on both two-dimensional and three-dimensional reservoir models, the ACG-EBS has proven highly effective in identifying optimal well placements that align with field deployment realities and maximize economic returns. This contribution significantly supports the ongoing evolution of oilfield development optimization, showcasing the algorithm's potential to enhance oil production and economic outcomes.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105740"},"PeriodicalIF":4.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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