Mapping Alteration Minerals Using ZY-1 02D Hyperspectral Remote Sensing Data in Coalbed Methane Enrichment Areas

Chen, Li; Sui, Xinxin; Liu, Rongyuan; Chen, Hong; Liu, Yu; Zhang, Xian; Chen, Haomin

doi:10.3390/rs15143590

Cited by 6 publications

(4 citation statements)

References 52 publications

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“…Given the utilization of L2D PRISMA data, significant processing steps have been undertaken on this dataset. However, owing to the specific nature of these data and the necessity to assess the Earth's surface reflection, certain bands within this dataset prove unsuitable for mineral remote sensing studies [30,31]. These bands exhibit heightened sensitivity to water vapor, resulting in substantial absorptions within the spectral profile.…”

Section: Pre-processingmentioning

confidence: 99%

“…These bands exhibit heightened sensitivity to water vapor, resulting in substantial absorptions within the spectral profile. Notably, the absorption characteristics spanning (913 nm to 979 nm), (1078 nm to 1185 nm), (1317 nm to 1491 nm), and (1775 nm to 2044 nm) diminish the efficacy of absorption ranges crucial for mineralogical investigations [31]. Hence, the primary course of action entails the exclusion of these bands from further analysis.…”

Section: Pre-processingmentioning

confidence: 99%

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PRISMA Hyperspectral Remote Sensing Data for Mapping Alteration Minerals in Sar-e-Châh-e-Shur Region, Birjand, Iran

Habashi,

Jamshid Moghadam,

Mohammady Oskouei

et al. 2024

Remote Sensing

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Remote sensing satellite imagery consistently provides valuable and frequent information, enabling the exploration of mineral resources across immense, remote and harsh domains. Recent developments in spaceborne hyperspectral remote sensing have opened avenues to support diverse remote sensing applications, particularly in the realm of mineral exploration. This study evaluates the capabilities of the PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral satellite data for mapping alteration minerals using the Matched Filtering Unmixing (MFU) approach in the Sar-e-châh-e-shur, Birjand, Iran. Minerals such as richterite, augite, psilomelane, ilmenite, kaolinite, smectite, mirabilite, muscovite, and chlorite were identified using the vertex component analysis (VCA) technique. Subsequently, alteration mineral maps of the study area were generated using a matched filtering technique. Additionally, through the integration of X-ray diffraction (XRD) analysis, thin section examination, geochemical study of stream sediments, and interpretation of geological maps, potential alteration mineralization zones were delineated in the study area. Ultimately, the validation process, which included comparing the maps with the findings derived from the PRISMA remote sensing study, was conducted using the normal score equation. Thus, our results yielded a normalized score of 3.42 out of 4, signifying an 85.71% agreement with the regional geological characteristics of the study area. The results of this investigation highlight the substantial potential of the PRISMA dataset for systematic alteration mineral mapping and consequent exploration of ore minerals, specifically in challenging and inaccessible terrains.

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Section: Pre-processingmentioning

confidence: 99%

Section: Pre-processingmentioning

confidence: 99%

PRISMA Hyperspectral Remote Sensing Data for Mapping Alteration Minerals in Sar-e-Châh-e-Shur Region, Birjand, Iran

Habashi,

Jamshid Moghadam,

Mohammady Oskouei

et al. 2024

Remote Sensing

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“…As shown in Table 4, the MSK-RFFIM method resulted in enhanced accuracy compared with either the MSK or RFFIM method alone. Previous studies have also indicated that hybrid feature selection methods can enhance the overall effectiveness of feature selection (Chen et al, 2023;Wang et al, 2023). The MSK-RFFIM method was further analyzed using the top 20 bands from Input V3, Input V4, and Input V5 (refer to Table 5).…”

Section: Feature Selectionmentioning

confidence: 99%

Estimation of the dolomite content of carbonate rock outcrops based on spectral knowledge and machine learning

Wei,

Shao,

et al. 2024

Front. Earth Sci.

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Accurately estimating the dolomite content in carbonate rocks is crucial for optimizing oil and gas exploration and production strategies. Hyperspectral techniques for estimating dolomite content have advantages in terms of efficiency, cost-effectiveness, and non-destructiveness compared with traditional laboratory methods. Despite the abundance of hyperspectral data, feature selection and extraction remain challenging. In this study, hyperspectral data collected from surface outcrop in the field using the analytical spectral device (ASD) were applied to construct model for estimating dolomite content. Firstly, the data were preprocessed via outlier analysis and continuum transformation. Next, a hybrid approach integrating spectral knowledge with machine learning was proposed and applied to facilitate efficient and precise feature selection of the hyperspectral data; in this approach, preliminary screening based on spectral knowledge is followed by further hyperspectral data feature selection using a random forest algorithm. The selected features were then combined using a support vector regression algorithm to obtain the estimation model. Finally, the accuracy of the model was evaluated using the hyperspectral data from field outcrop samples. To further verify the effectiveness of this method, various combinations of eight input variables and four machine learning algorithms were compared. Among all combinations, our model achieved the highest accuracy with a test R2 value of 0.91 and a root-mean-square error of only 0.122. The proposed method is practical and efficient and provides precise quantitative data for field geologists to identify the mineral distribution in outcrops. Thus, our method provides robust support for understanding reservoir characteristics and has significant practical value in geological surveys and mineral exploration.

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“…In addition, compared to traditional remote sensing data, hyperspectral data can provide richer spectral information, thus reflecting the spectral response characteristics of different lithologies more accurately. This can provide effective technical support for geological exploration and mineral resource development (Chen L. et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Machine learning algorithms for lithological mapping using Sentinel-2 and SRTM DEM in highly vegetated areas

Chen,

Dong,

Wang

et al. 2023

Front. Ecol. Evol.

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Lithological mapping in highly vegetated areas using remote sensing techniques poses a significant challenge. Inspired by the concept of “geobotany”, we attempted to distinguish lithologies indirectly using machine learning algorithms (MLAs) based on Sentinel-2 and SRTM DEM in Zhangzhou City, Fujian Province. The study area has high vegetation cover, with lithologies that are largely obscured. After preprocessing such as cloud masking, resampling, and median image synthesis, 17 spectral bands and features from Sentinel-2 and 9 terrain features from DEM were extracted. Five widely used MLAs, MD, CART, SVM, RF, and GBDT, were trained and validated for lithological mapping. The results indicate that advanced MLAs, such as GBDT and RF, are highly effective for nonlinear modeling and learning with relative increases reaching 8.18%∼11.82% for GBDT and 6.36%∼10% for RF. Compared with optical imagery or terrain data alone, combining Sentinel-2 and DEM significantly improves the accuracy of lithological mapping, as it provides more comprehensive and precise spectral characteristics and spatial information. GBDT_Sen+DEM utilizing integrated data achieved the highest classification accuracy, with an overall accuracy of 63.18%. This study provides a case study for lithological mapping of areas with high vegetation cover at the local level. This also reinforces the idea that merging remote sensing and terrain data significantly enhances the precision and reliability of the lithological mapping methods.

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Mapping Alteration Minerals Using ZY-1 02D Hyperspectral Remote Sensing Data in Coalbed Methane Enrichment Areas

Cited by 6 publications

References 52 publications

PRISMA Hyperspectral Remote Sensing Data for Mapping Alteration Minerals in Sar-e-Châh-e-Shur Region, Birjand, Iran

PRISMA Hyperspectral Remote Sensing Data for Mapping Alteration Minerals in Sar-e-Châh-e-Shur Region, Birjand, Iran

Estimation of the dolomite content of carbonate rock outcrops based on spectral knowledge and machine learning

Machine learning algorithms for lithological mapping using Sentinel-2 and SRTM DEM in highly vegetated areas

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