A New Band Ratio Approach for Discriminating Calcite and Dolomite by ASTER Imagery in Arid and Semiarid Regions

Beirami, Meisam Rasouli; Tangestani, Majid H.

doi:10.1007/s11053-020-09648-w

Cited by 10 publications

(4 citation statements)

References 45 publications

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“…sorption peak at 2.3 µm, which is different from other rocks [55]. In addition to w absorption features, the average reflectance spectrum of limestone has a carbonate v tion absorption peak at 2.3 µm.…”

Section: Dwt Of Spectral Curvesmentioning

confidence: 85%

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Improving Rock Classification with 1D Discrete Wavelet Transform Based on Laboratory Reflectance Spectra and Gaofen-5 Hyperspectral Data

Guo,

Jiang

2023

Remote Sensing

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The high intra-class variability of rock spectra is an important factor affecting classification accuracy. The discrete wavelet transform (DWT) can capture abrupt changes in the signal and obtain subtle differences between the spectra of different rocks. Taking laboratory spectra and hyperspectral data as examples, high-frequency features after DWT were used to improve the discrimination accuracy of rocks. Various decomposition levels, mother wavelet functions, and reconstruction methods were used to compare the accuracy. The intra-class variability was measured using the intra-class Spectral Angle Mapper (SAM). Our results show that the high-frequency features could improve the discrimination accuracy of laboratory spectra by 13.4% (from 46.5% to 59.9%), compared to the original spectral features. The accuracy of image spectra in two study areas increased by 8.6% (from 68.3% to 76.9%) and 7.2% (from 81.3% to 88.5%), respectively. Haar wavelets highlighted the spectral differences between different rocks. After DWT, intra-class SAM reduced and intra-class variability of rocks decreased. The Pearson correlation coefficient indicated a negative correlation between intra-class variability and overall accuracy. It suggested that improving classification accuracy by reducing intra-class variability was feasible. Though the result of lithological mapping still leaves room for improvement, this study provides a new approach to reduce intra-class variability, whether using laboratory spectra or hyperspectral data.

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Section: Dwt Of Spectral Curvesmentioning

confidence: 85%

“…Andesite has low overall reflectance and water absorption peaks at 1.4 µm and 1.9 µm. Limestone exhibits an absorption peak at 2.3 µm, which is different from other rocks [55]. In addition to water absorption features, the average reflectance spectrum of limestone has a carbonate vibration absorption peak at 2.3 µm.…”

Section: Dwt Of Spectral Curvesmentioning

confidence: 88%

Improving Rock Classification with 1D Discrete Wavelet Transform Based on Laboratory Reflectance Spectra and Gaofen-5 Hyperspectral Data

Guo,

Jiang

2023

Remote Sensing

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“…v 1 + 2ν 3 Clark et al, 1990;Gaffey, 1987;Hunt, 1977;Rasouli Beirami and Tangestani, 2020 2,350 3v 3 Clark et al, 1990;Gaffey, 1987;Hunt, 1977;Rasouli andTangestani, 2020 2,248 -Gaffey (1987) 2,160 v 1 + 2ν 3 + ν 4 , 3v 1 + 2ν 4 Clark et al, 1990;Gaffey, 1987;Hunt, 1977;Rasouli Beirami and Tangestani, 2020 2,000 2v 1 + 2ν 3 Clark et al, 1990;Gaffey, 1987;Hunt, 1977;Rasouli Beirami and Tangestani, 2020 1,900 v 1 + 3ν 3 Clark et al, 1990;Gaffey, 1987;Hunt, 1977;Rasouli Beirami andTangestani, 2020 1,740 -Gaffey (1987) 2.2.3 Feature selection based on random forest feature importance (RFFIM)…”

Section: 550mentioning

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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“…The position of the 2315–2335 nm absorption-band is commonly used to differentiate dolomite from calcite in the laboratory 10 , 11 and in remotely-sensed imagery 18 , 21 , 53 . Van der Meer 13 suggested that this absorption-band has a linear relationship with the degree of dolomitization and Zaini et al 14 , 15 applied this approach to track the band positions of synthetic mixtures of calcite and dolomite.…”

Section: Interpretationsmentioning

confidence: 99%

Shortwave infrared hyperspectral imaging as a novel method to elucidate multi-phase dolomitization, recrystallization, and cementation in carbonate sedimentary rocks

McCormick

Corlett

Stacey

et al. 2021

Sci Rep

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Carbonate rocks undergo low-temperature, post-depositional changes, including mineral precipitation, dissolution, or recrystallisation (diagenesis). Unravelling the sequence of these events is time-consuming, expensive, and relies on destructive analytical techniques, yet such characterization is essential to understand their post-depositional history for mineral and energy exploitation and carbon storage. Conversely, hyperspectral imaging offers a rapid, non-destructive method to determine mineralogy, while also providing compositional and textural information. It is commonly employed to differentiate lithology, but it has never been used to discern complex diagenetic phases in a largely monomineralic succession. Using spatial-spectral endmember extraction, we explore the efficacy and limitations of hyperspectral imaging to elucidate multi-phase dolomitization and cementation in the Cathedral Formation (Western Canadian Sedimentary Basin). Spectral endmembers include limestone, two replacement dolomite phases, and three saddle dolomite phases. Endmember distributions were mapped using Spectral Angle Mapper, then sampled and analyzed to investigate the controls on their spectral signatures. The absorption-band position of each phase reveals changes in %Ca (molar Ca/(Ca + Mg)) and trace element substitution, whereas the spectral contrast correlates with texture. The ensuing mineral distribution maps provide meter-scale spatial information on the diagenetic history of the succession that can be used independently and to design a rigorous sampling protocol.

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A New Band Ratio Approach for Discriminating Calcite and Dolomite by ASTER Imagery in Arid and Semiarid Regions

Cited by 10 publications

References 45 publications

Improving Rock Classification with 1D Discrete Wavelet Transform Based on Laboratory Reflectance Spectra and Gaofen-5 Hyperspectral Data

Improving Rock Classification with 1D Discrete Wavelet Transform Based on Laboratory Reflectance Spectra and Gaofen-5 Hyperspectral Data

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

Shortwave infrared hyperspectral imaging as a novel method to elucidate multi-phase dolomitization, recrystallization, and cementation in carbonate sedimentary rocks

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