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Finer soil properties mapping framework for broad-scale area: A case study of Hubei Province, China

GEODERMA 2024年 449卷

作者： Wang, Ruizhen Chen, Weitao Chen, Hao qin, xuwen China Univ Geosci Key Lab Geol Survey & Evaluat Minist Educ Wuhan 430074 Peoples R China China Univ Geosci Fac Comp Sci Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Nat Resource Informat Management & Di Wuhan 430074 Peoples R China China Geol Survey China Aero Geophys Survey & Remote Sensing Ctr Nat Beijing 100083 Peoples R China

Fine-scale spatial distribution of soil physicochemical properties is crucial for soil quality management, agriculture planning and geotechnical engineering. Existing soil map databases are usually developed in national scale, potentially leading to issues of coarse resolution and restricted applicability in fine-scaled studies. For broad-scale area, conventional digital soil mapping methods are challenging due to the lack of representative soil profiles and their uneven distribution. This study addresses these challenges by developing a downscaling-based framework to map 11 soil physicochemical properties at a 30-m resolution. The methodology involves constructing regression models using soil properties derived from coarse national soil maps and soil-forming covariates. Predictions are subsequently refined using fine-resolution covariates. To capture fine-scale spatial variability across diverse landscapes, 30-m resolution remote sensing data, relief, climate, and spatial covariates were prepared. The fine-gridding framework employs tree-based machine learning models, particularly random forest (RF), to enhance prediction accuracy. Evaluation of the fine-gridded soil maps demonstrated commendable results, with high consistency in summary statistics, semi-variograms, and evaluation metrics compared to reference maps. Mass preservation of RF-predicted maps exhibit high performances with both concordance correlation coefficients (CCCs) and coefficients of determination (R(2)s) exceeding 0.9 across most scenarios. This study provides a robust approach for enhancing the spatial resolution of soil maps, facilitating their use in local fine-scale applications. The proposed strategy offers a valuable solution for broad-scale areas that require finer soil maps but lack sufficient qualified soil profiles.

关键词： Digital soil mapping Broad-scale Fine gridding Downscaling Machine learning

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Geological Background Prototype Learning-Enhanced Network for Remote-Sensing-Based Engineering Geological Lithology Interpretation in Highly Vegetated Areas

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 8794-8809页

作者： Ouyang, Shubing Chen, Weitao qin, xuwen Yang, Jinzhong China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Geol Survey China Aero Geophys Survey & Remote Sensing Ctr Nat Beijing 100083 Peoples R China China Univ Geosci Key Lab Geol Survey & Evaluat Minist Educ Wuhan 430074 Peoples R China

Remote-sensing-based interpretation of engineering geological lithology units provides essential lithological information for engineering planning, design, and construction projects. However, dense vegetation cover can easily interfere with the lithological spectral characteristics of optical remote sensing. Utilizing known geological background prototype-guided knowledge as a guide can mitigate interpretation interference from vegetation. Hence, for the interpretation of engineering geological lithology units, we aimed to develop a novel semantic segmentation network, LSBPnet, which combines a geological background prototype-guided learning model with a U-Net-based model. The geological background prototype-guided learning model employs lithological prototype features learned from the surrounding geological units of the predicted target to identify similarities with the predicted target's features. The model encompasses three steps: inputting known background images and known labels, learning prototype features of the background information, and learning target features based on the background information prototype perception. Using the engineering geological lithology of the Yangtze River's middle reaches as the focus area, which is a region with high vegetation cover, the proposed network achieved a 13.94% increase in the overall accuracy compared with that of commonly prevalent semantic segmentation networks. Incorporating the geological background prototype-guided knowledge helped to strengthen the distinction and relationship between the target and its surrounding lithological features, thereby improving the assessment of prediction outcomes. The diverse ablation experiments further highlighted the importance of incorporating background prototype-guided knowledge.

关键词： Geology Rocks Vegetation mapping Feature extraction Remote sensing Surveys Soft sensors Deep learning engineering geological lithology units prototype learning remote sensing semantic segmentation

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Accurate multiclassification and segmentation of gastric cancer based on a hybrid cascaded deep learning model with a vision transformer from endoscopic images

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INFORMATION SCIENCES 2024年 670卷

作者： Ul Haq, Ejaz Yong, qin Yuan, Zhou Huang, Jianjun Ul Haq, Rizwan qin, xuwen Dongguan Univ Technol Sch Comp Sci & Technol Dongguan Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Shenzhen Univ Sch Elect & Informat Engn Shenzhen Peoples R China Khushal Khan Khattak Univ KPK Karak Pakistan Shenzhen Int Exchange Coll Shenzhen Grade A1 Shenzhen Peoples R China

Compared with other forms of cancer, gastric cancer has high mortality and incidence rates, making it a major cause of death worldwide. Accurate diagnosis is crucial in the treatment of stomach cancer. Researchers have used deep learning techniques facilitated by developments in artificial intelligence to classify and segment endoscopic images of stomach cancer. Most recent research examining endoscopic images of stomach cancer has used a binary classification system, which is insufficient for practical use. False -positives and computational costs become problematic when segmentation is applied to all the images of healthy patients obtained throughout the evaluation. Hence, the expected level of performance has not been attained in the real-time multiclassification and segmentation of stomach cancer. In this study, we present a deep learning -based technique for multiclassification of endoscopic images by combining modified GoogLeNet and vision transformer (ViT) models and identifying invasive areas based on Faster RCNN. The classification of endoscopic images into three categories, namely, normal, early gastric cancer, and advanced gastric cancer, is accomplished by using a hybrid approach including modified GoogLeNet and vision transformer (ViT) models. Gastric cancer regions are then identified and segmented in the endoscopic images using the Faster R -CNN method. The Faster R -CNN algorithm is used with an endoscopic image as input, resulting in the generation of a bounding box and label image that accurately represents the gastric cancerous area. The proposed model achieved an accuracy, sensitivity and F1 -score of 97.4%, 97.5% and 95.9%, respectively, for the classification of noncancerous, early gastric cancer and advanced gastric cancer. Furthermore, the performance of the segmentation method was also validated based on evaluation metrics and achieved 96.7%, 96.6% and 95.5% accuracy, sensitivity and F1 -score, respectively, for the segmentation of noncancer

关键词： Gastric cancer Transformer Classification Segmentation Deep learning

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A novel finer soil strength mapping framework based on machine learning and remote sensing images

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COMPUTERS & GEOSCIENCES 2024年 182卷

作者： Wang, Ruizhen Wan, Siyang Chen, Weitao qin, xuwen Zhang, Guo Wang, Lizhe China Univ Geosci Key Lab Geol Survey & Evaluat Minist Educ Wuhan 430074 Peoples R China China Univ Geosci Fac Comp Sci Wuhan 430074 Peoples R China China Geol Survey Basic Survey Dept 45 Fuwai St Beijing 100037 Peoples R China

Soil strength is an important factor for assessing the vehicle trafficability in the wilds and making reliable offroad path planning. Rating Cone index (RCI) has been widely used as an indicator of soil strength for mobility assessment. Currently, regional RCI are mainly obtained by using Soil Moisture Strength Prediction (SMSP) Model based on the Unified Soil Classification System (USCS) soil types and soil moisture at critical depth. However, USCS classification is inaccessible directly in most soil databases. And current soil moisture products are of coarse spatial resolution and only reliable in soil surface layer. It is costly to arrange ground survey for USCS soil type and the fine soil moisture in deeper layers cannot be obtained directly. To fill this gap, a novel framework is proposed to generate finer RCI-based soil strength map. To gain USCS soil classification in an un-surveyed study site, RF, GBDT, XGBoost and LCE ensemble learning models were trained with data from gSSURGO Database which contains USCS soil types and soil properties, and then make prediction on data from SoilGrids Database by using same properties. Then, to obtain the finer soil moisture of critical depth, these treebased models were constructed with ground observation data, Sentinel-2 images, topographical data and soil properties with higher spatial resolution. The SMSP model is finally used to generate the finer daily soil strength map for the study site. The proposed framework highly improved the resolution and reliability of existing soil strength map generating methods, and can provide detailed soil strength information.

关键词： Soil strength Rating cone index Machine learning Soil moisture

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Pore-Scale Investigation of Decomposition-Methods-Dependent Fluid Flow Properties in Hydrate-Bearing Sediments

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SPE JOURNAL 2024年第1期29卷 138-156页

作者： Bian, Hang qin, xuwen Wang, Zhiyuan Luo, Wanjing Lu, Cheng Zhou, Yingfang China Univ Geosci Sch Energy Resources Beijing Peoples R China China Geol Survey Beijing Peoples R China China Univ Petr Sch Petr Engn Dongying Peoples R China Univ Aberdeen Sch Engn Aberdeen Scotland

Depressurization (PD) and thermal stimulation (TS) are the primary methods for producing gas from natural gas hydrate (NGH) sediments. Fluid flow properties of the hydrate sediment, such as permeability, are fundamental parameters for simulating both processes. Most of the existing formulated permeability models are based on the numerical or experimental investigation of hydrate morphology evolution without considering the decomposition methods. In this study, we investigate the hydrate- decomposition- methods (PD and TS processes)- dependent fluid flow properties of hydrate sediments, which is achieved by microcomputed tomography (micro-CT) scanning of hydrate morphology evolution during PD- and TS- induced decomposition, as well as pore - scale modeling of fluid flow in the extracted 3D fluid- rock- hydrate images. We find that the decomposition behavior during TS is much more complicated than that during PD. The retardation zone in the PD sample increases the heterogeneity of the pore structure, while the secondary hydrates generated during TS cause even more heterogeneity in the pore space. The better facilitation of the TS method on hydrate split is favorable for the continuity of the gas phase. The pore-scale fluid flow simulation shows that the modified Kozeny- Carman (K-C) model is the best to describe the evolution of the normalized permeability with hydrate saturation during PD. However, a single model is not sufficient to describe the normalized permeability during TS decomposition due to the stronger heterogeneous porous structure reformed by the local accumulation of secondary hydrates. The two-phase flow capability is best at the initial stage of PD decomposition, while the two-phase flow region becomes wider as TS decomposition progresses. These results provide significant references for the simulation of the natural hydrate extraction process using different decomposition methods.

关键词： geologist asia government upstream oil & gas geology production logging decomposition reservoir surveillance hydrate hydrate decomposition pore space Flow Assurance Flow in porous media Hydrates Production logging Reservoir Fluid Dynamics Well & Reservoir Surveillance and Monitoring

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Lithological Unit Classification Based on Geological Knowledge-Guided Deep Learning Framework for Optical Stereo Mapping Satellite Imagery

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1-16页

作者： Zhou, Gaodian Chen, Weitao qin, xuwen Li, Jun Wang, Lizhe China Univ Geosci Fac Comp Sci Minist Educ Wuhan 430074 Peoples R China China Univ Geosci Minist Educ Key Lab Geol Survey & Evaluat Wuhan 430074 Peoples R China

Lithological unit classification (LUC) refers to the classification of different types of rocks within an area, and it has been widely used in many fields, such as resource surveys and infrastructure planning. However, traditional field surveys require a lot of resources and time. Since remote sensing technology can rapidly acquire information without regional limitations, many researchers have focused on classifying lithological units with remote sensing images. However, in an area covered by vegetation, the beneficial information directly provided by remote sensing images is limited. Moreover, lithological interpretation often requires geological prior knowledge for guidance, which cannot be provided by remote sensing images. Thus, this study designed a dual-branch deep learning model to extract geological prior knowledge from geological information, and improve the accuracy of LUC. In the process of feature transmission of the model, a Dense Attention residual-atrous spatial pyramid pooling (DA-ASPP) module was proposed to maximize the preservation of lithological units ' features. The DA-ASPP integrates the idea of dense connection into ASPP for multiscale object feature preservation and adds residual structure into the channel attention mechanism to screen out the representative features of lithological units. The study area was located in southeastern Hubei Province, China, with seven categories of lithological units. A total of seven deep learning networks were compared. The proposed method achieved a mean intersection over union (IOU) of 44.61% with a Macro-F1 of 56.54%, which were better than those of comparison models. Moreover, the visualization results demonstrated the superiority of the proposed model in LUC.

关键词： Dense attention residual dual branches lithological unit classification (LUC) prior knowledge remote sensing.

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CUG-STCN: A seabed topography classification framework based on knowledge graph-guided vision mamba network

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2025年 136卷

作者： Wang, Haoyi Chen, Weitao Li, Xianju Liang, Qianyong qin, xuwen Li, Jun China Univ Geosci Fac Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Key Lab Geol Survey & Evaluat Minist Educ Wuhan 430074 Peoples R China China Geol Survey Guangzhou Marine Geol Survey Guangzhou 511400 Peoples R China China Geol Survey China Aero Geophys Survey & Remote Sensing Ctr Nat Beijing 100083 Peoples R China

Multibeam sounding is a high-precision remote sensing method for seabed detection. Seabed topography classification is crucial for marine science research, resource exploration and engineering. When using multibeam data for seabed topography automatic classification, the fuzzy boundaries of different topographic entities, redundancy of multimodal data, and the lack of geological knowledge guidance have led to low classification accuracy. Thus, a knowledge graph-guided vision mamba seabed topography classification network (CUG-STCN) was constructed, consisting of three modules: (1) The long sequence modeling mamba-based encoder addresses the fuzzy seabed topography boundary. It uses 2D-selective-scan to create image blocks in different scanning directions. By combining with the selective state space model to capture long-range dependencies and ensure transmission of spatial context information while maintaining linear computational complexity. (2) The crossmodal information interaction and fusion module addresses the redundancy of multimodal information. By employing a bidirectional information interaction mechanism, it captures the correlations of seabed topography between different modalities and achieving feature fusion. (3) The seabed topography knowledge graph-guided semantic perception module guides the geological knowledge. It constructs seabed topography knowledge vectors through entity query and word embedding, using the similarity between vectors to create a similarity measurement matrix. It provides geological knowledge, enhancing the modeling capability of complex seabed topography relationship. CUG-STCN achieves OA of 90.11% and mIOU of 48.50%, outperforming six mainstream networks, which at most, achieve the OA and mIOU improvements of 5.37% and 14.18%. Notably, the application of CUG-STCN in other regions demonstrates its strong generalization performance.

关键词： Multibeam Seabed topography Mamba Knowledge graph Remote sensing

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Case study on the combined use of surface seawater injection and depressurization for sustainable natural gas hydrate extraction

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JOURNAL OF CLEANER PRODUCTION 2025年 494卷

作者： Zhang, Zhaobin Li, Yuxuan Li, Shouding He, Jianming Li, Xiao Lu, Cheng qin, xuwen Chinese Acad Sci Inst Geol & Geophys Key Lab Deep Petr Intelligent Explorat & Dev Beijing 100029 Peoples R China Univ Chinese Acad Sci Coll Earth & Planetary Sci Beijing 100049 Peoples R China China Geol Survey Minist Nat Resources Beijing 100037 Peoples R China

In response to the issue of inefficient extraction of natural gas hydrates due to a lack of heat supplementation during long-term production, a combined extraction method integrating surface seawater injection and depressurization has been proposed. Under the premise that the surface seawater injection pipeline possesses certain thermal insulation properties, this method can sustainably harness solar energy to replenish the thermal energy of gas hydrate reservoirs. Numerical simulations based on a validated three-dimensional heterogeneous model from the 2013 Nankai Trough pilot production in Japan demonstrate that this method significantly enhances hydrate extraction efficiency compared to depressurization alone. The results show that, over a 500-day production scenario, methane recovery efficiency can be enhanced by up to 150%. The study reveals that only an appropriate injection temperature can effectively improve the efficiency of hydrate exploitation. Compared to the depressurization-only method, this extraction approach increases the temperature driving force (TDF) near the injection well improved by up to 12 K. However, higher injection rates suppress depressurization effects, requiring a balance between injection temperature and rate. The proposed approach also facilitates more uniform reservoir deformation, reducing localized subsidence risks. These findings suggest that the combined surface seawater injection and depressurization method provides an effective and environmentally friendly solution for sustainable hydrate exploitation.

关键词： Natural gas hydrate Surface seawater injection Injection temperature and rate Temperature driving force Reservoir deformation

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Remote sensing intelligent interpretation for geology: from the perspective of geological exploration

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INTERNATIONAL GEOLOGY REVIEW 2025年第10期67卷 1389-1391页

作者： Afandi, Achmad Chen, Weitao Li, Xianju qin, xuwen Wang, Lizhe Univ Pendidikan Indonesia Dept Phys Educ Bandung Indonesia

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Remote sensing intelligent interpretation for geology: from perspective of geological exploration

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INTERNATIONAL GEOLOGY REVIEW 2025年第12期67卷 1637-1639页

作者： Helmiza, Fitria Chen, Weitao Li, Xianju qin, xuwen Wang, Lizhe Univ Diponegoro Phys Semarang Indonesia

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