Remote Sensing in Lineament Identification: Examples from Western India

Dasgupta, Swagato; Mukherjee, Soumyajit

doi:10.1016/b978-0-12-814048-2.00016-8

Cited by 47 publications

(12 citation statements)

References 41 publications

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“…Dasgupta and Mukherjee (2017) in their repository compiled the stratigraphy of this basin. Dasgupta and Mukherjee (2019) and Biswas et al (2022) presented the morphotectonics of this basin in some detail. Meso-scale structures in this basin are available as a chapter in an atlas (Mukherjee et al 2020).…”

Section: Geologymentioning

confidence: 97%

Source rock properties and kerogen decomposition kinetics of Eocene shales from petroliferous Barmer basin, western Rajasthan, India

Kar¹,

Mani²,

Dasgupta³

et al. 2022

Preprint

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Hydrocarbon exploration and production are going on in Barmer basin (Rajasthan, India) formore than a decade. The potential source rocks are of Paleocene – Eocene age, and Mesozoicsiltstones form the reservoirs. The western and central portions of the Rajasthan basin arecharacterised by extensive lignite formations, which can be promising for artificialtransformation to oil and gas. We study the source rock properties, depositional environmentsand hydrocarbon generation potential of the Paleogene lignitic shales of the Giral lignite mineand the Cretaceous Sarnu siltstones for their source and reservoir rock potentiality. The totalorganic carbon content (TOC) of the Giral samples range between 0.76-49.83 wt% and thethermal maturity, as reflected by the pyrolysis Tmax, lies between 412-468 ℃. Sarnoo siltstones,on the other hand, have a very low TOC ranging from 0.02-0.08 wt% and a Tmax of 320-608 °C.The higher TOC and a lower oxygen index (OI) of Giral lignites and shaly lignites indicate theprevalence of a reducing depositional environment. Bulk organic geochemical parametersinvolving kerogen pyrolysis and thermal degradation kinetics indicate a more promisinghydrocarbon generation potential in the lignite than shales, which, however exhibits higherthermal maturity of organic matter. Giral lignites as well as shales show dominantly Type-III heterogeneous kerogen, which is sourced from terrestrial organic matter. This is also corroborated by a broader distribution of activation energy derived from the thermal decomposition of the kerogen. The kerogen transformation ratio (KTR) and the hydrocarbon generation rate GR) suggest a considerably earlier and quicker kerogen transformation. Samples from the Sarnoo area offer no significant information on the source rock characteristics, due to their lean organic nature. However, lignites and shaly lignites of the Giral mine are identified as excellent candidates for their suitability towards easy conversion into hydrocarbonproducts through artificial techniques.

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Section: Geologymentioning

confidence: 97%

Source rock properties and kerogen decomposition kinetics of Eocene shales from petroliferous Barmer basin, western Rajasthan, India

Kar¹,

Mani²,

Dasgupta³

et al. 2022

Preprint

Self Cite

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“…RS has added a new dimension to the monitoring of geogenese diversity, its characteristics, impacts, disturbances and biodiversity. RS can identify, monitor and describe geogene features (minerals, rock types), taxa (mountain species), structures (genesis patterns, lineaments) and functions (flow behaviour) that represent the tectonic architecture and its characteristics [ 78 ]. Detailed structure and pattern analyses using RS technologies help to interpret, classify, distinguish and thus identify the origin of different structures and patterns in the Siberian and Deccan Traps [ 78 ].…”

Section: Monitoring Geodiversity With Rs and The Traits Approachmentioning

confidence: 99%

“…RS can identify, monitor and describe geogene features (minerals, rock types), taxa (mountain species), structures (genesis patterns, lineaments) and functions (flow behaviour) that represent the tectonic architecture and its characteristics [ 78 ]. Detailed structure and pattern analyses using RS technologies help to interpret, classify, distinguish and thus identify the origin of different structures and patterns in the Siberian and Deccan Traps [ 78 ].…”

Section: Monitoring Geodiversity With Rs and The Traits Approachmentioning

confidence: 99%

Monitoring vegetation- and geodiversity with remote sensing and traits

Lausch,

Selsam,

Pause

et al. 2024

Phil. Trans. R. Soc. A.

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Geodiversity has shaped and structured the Earth's surface at all spatio-temporal scales, not only through long-term processes but also through medium- and short-term processes. Geodiversity is, therefore, a key control and regulating variable in the overall development of landscapes and biodiversity. However, climate change and land use intensity are leading to major changes and disturbances in bio- and geodiversity. For sustainable ecosystem management, temporal, economically viable and standardized monitoring is needed to monitor and model the effects and changes in vegetation- and geodiversity. RS approaches have been used for this purpose for decades. However, to understand in detail how RS approaches capture vegetation- and geodiversity, the aim of this paper is to describe how five features of vegetation- and geodiversity are captured using RS technologies, namely: (i) trait diversity, (ii) phylogenetic/genese diversity, (iii) structural diversity, (iv) taxonomic diversity and (v) functional diversity. Trait diversity is essential for establishing the other four. Traits provide a crucial interface between in situ , close-range, aerial and space-based RS monitoring approaches. The trait approach allows complex data of different types and formats to be linked using the latest semantic data integration techniques, which will enable ecosystem integrity monitoring and modelling in the future. This article is part of the Theo Murphy meeting issue ‘Geodiversity for science and society’.

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“…Traditional lithology and mineral mapping methods can be summarized into three categories: image enhancement methods, spectral feature analysis methods, and object-oriented-based methods. Image enhancement methods such as minimum noise fraction rotation (MNF), 10,11 principal component analysis (PCA), 12 and band ratio 13,14 method aim to enhance the relevant feature of litho-units through transformation processing such as dimensionality reduction. Such methods are simple and effective, but they are mainly used to enhance the expression of lithology-related features and cannot directly achieve classification.…”

Section: Introductionmentioning

confidence: 99%

3D autoencoder algorithm for lithological mapping using ZY-1 02D hyperspectral imagery: a case study of Liuyuan region

Zhang

Li³

et al. 2021

J. Appl. Rem. Sens.

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A hyperspectral image (HSI) contains hundreds of spectral bands, which provide detailed spectral information, thus offering an inherent advantage in classification. The successful launch of the Gaofen-5 and ZY-1 02D hyperspectral satellites has promoted the need for large-scale geological applications, such as mineral and lithological mapping (LM). In recent years, following the success of computer vision, deep learning methods have shown their advantage in solving the problem of hyperspectral classification. However, the combination of deep learning and HSI to solve the problem of geological mapping is insufficient. We propose a new 3D convolutional autoencoder for LM. A pixel-based and cube-based 3D convolutional neural network architecture is designed to extract spatial-spectral features. Traditional and machine learning methods are employed as competing methods, trained on two real hyperspectral datasets, and evaluated according to the overall accuracy, F1 score, and other metrics. Results indicate that the proposed method can provide convincing results for LM applications on the basis of the hyperspectral data provided by the ZY-1 02D satellite. Compared with traditional methods, the combination of deep learning and hyperspectral can provide more efficient and highly accurate results. The proposed method has better robustness than supervised learning methods and shows great promise under small sample conditions. As far as we know, this work is the first attempt to apply unsupervised spatial-spectral feature learning technology in LM applications, which is of great significance for large-scale applications. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Remote Sensing in Lineament Identification: Examples from Western India

Cited by 47 publications

References 41 publications

Source rock properties and kerogen decomposition kinetics of Eocene shales from petroliferous Barmer basin, western Rajasthan, India

Source rock properties and kerogen decomposition kinetics of Eocene shales from petroliferous Barmer basin, western Rajasthan, India

Monitoring vegetation- and geodiversity with remote sensing and traits

3D autoencoder algorithm for lithological mapping using ZY-1 02D hyperspectral imagery: a case study of Liuyuan region

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