Mineral mapping in the Pyramid Lake basin: Hydrothermal alteration, chemical precipitates and geothermal energy potential

Kratt, Chris; Calvin, W. M.; Coolbaugh, Mark

doi:10.1016/j.rse.2010.05.006

Cited by 49 publications

(22 citation statements)

References 17 publications

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“…These tufas are typically observed as draping structures on hard, high-angle surfaces, including bedrock outcrops as well as older tufa deposits, such as the tower structures. These draping frondose tufas have previously been interpreted as formed by wave action along paleoshorelines (Kratt, Calvin, & Coolbaugh, 2010).…”

Section: Winnemucca Dry Lake Nevada Usamentioning

confidence: 95%

Microbially influenced lacustrine carbonates: A comparison of Late Quaternary Lahontan tufa and modern thrombolite from Fayetteville Green Lake, NY

et al. 2019

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Carbonate microbialites in lakes can serve as valuable indicators of past environments, so long as the biogenicity and depositional setting of the microbialite can be accurately determined. Late Pleistocene to Early Holocene frondose draping tufa deposits from Winnemucca Dry Lake (Nevada, USA), a subbasin of pluvial Lake Lahontan, were examined in outcrop, petrographically, and geochemically to determine whether microbially induced precipitation is a dominant control on deposition. These observations were compared to modern, actively accumulating microbialites from Fayetteville Green Lake (New York, USA) using similar methods. In addition, preserved microbial DNA was extracted from the Lahontan tufa and sequenced to provide a more complete picture of the microbial communities. Tufas are texturally and geochemically similar to modern thrombolitic microbialites from Fayetteville Green Lake, and the stable isotopic composition of organic C, N, inorganic C, and O supports deposition associated with a lacustrine microbial mat environment dominated by photosynthetic processes. DNA extraction and sequencing indicate that photosynthetic microbial builders were present during tufa deposition, primarily Chloroflexi and Proteobacteria with minor abundances of Cyanobacteria and Acidobacteria. Based on the sequencing results, the depositional environment of the tufas can be constrained to the photic zone of the lake, contrasting with some previous interpretations that put tufa formation in deeper waters. Additionally, the presence of a number of mesothermophilic phyla, including Deinococcus–Thermus, indicates that thermal groundwater may have played a role in tufa deposition at sites not previously associated with groundwater influx. The interpretation of frondose tufas as microbially influenced deposits provides new context to interpretations of lake level and past environments in the Lahontan lake basins.

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Section: Winnemucca Dry Lake Nevada Usamentioning

confidence: 95%

Microbially influenced lacustrine carbonates: A comparison of Late Quaternary Lahontan tufa and modern thrombolite from Fayetteville Green Lake, NY

et al. 2019

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“…A cascaded approach using ASTER data for regional alteration mapping and local follow-up by Hyperion for target selection was presented as a tool for mineral exploration (Bishop et al, 2011). More recently, active hydrothermal systems have been investigating in the context of geothermal energy resources (Vaughan et al, 2005;Yang et al, 2000Yang et al, , 2001Kratt et al, 2010;Hellman and Ramsey, 2004). Fig.…”

Section: Applications In Geology: the Vis-swir Rangementioning

confidence: 99%

Multi- and hyperspectral geologic remote sensing: A review

van der Meer,

van der Werff,

van Ruitenbeek

et al. 2012

International Journal of Applied Earth Observation and Geoinfor

847

370

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“…Satellite remote-sensing data and advances in digital image processing (DIP) techniques provided a new impulse to the development of lithological mapping. Spectral data from space and airborne sensors were widely applied to geological mapping, including lithological discrimination [1][2][3][4][5][6][7][8], structural mapping [9], hydrothermal alteration [10][11][12], and economic mineral deposits [13][14][15][16][17]. Because of their cost effectiveness, especially in mapping inaccessible areas [4,[18][19][20] and in the production of small-scale maps, remote-sensing methods provide a good alternative to traditional field work [21].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

Bachri

Hakdaoui

Raji

et al. 2019

IJGI

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Remote sensing data proved to be a valuable resource in a variety of earth science applications. Using high-dimensional data with advanced methods such as machine learning algorithms (MLAs), a sub-domain of artificial intelligence, enhances lithological mapping by spectral classification. Support vector machines (SVM) are one of the most popular MLAs with the ability to define non-linear decision boundaries in high-dimensional feature space by solving a quadratic optimization problem. This paper describes a supervised classification method considering SVM for lithological mapping in the region of Souk Arbaa Sahel belonging to the Sidi Ifni inlier, located in southern Morocco (Western Anti-Atlas). The aims of this study were (1) to refine the existing lithological map of this region, and (2) to evaluate and study the performance of the SVM approach by using combined spectral features of Landsat 8 OLI with digital elevation model (DEM) geomorphometric attributes of ALOS/PALSAR data. We performed an SVM classification method to allow the joint use of geomorphometric features and multispectral data of Landsat 8 OLI. The results indicated an overall classification accuracy of 85%. From the results obtained, we can conclude that the classification approach produced an image containing lithological units which easily identified formations such as silt, alluvium, limestone, dolomite, conglomerate, sandstone, rhyolite, andesite, granodiorite, quartzite, lutite, and ignimbrite, coinciding with those already existing on the published geological map. This result confirms the ability of SVM as a supervised learning algorithm for lithological mapping purposes.

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Mineral mapping in the Pyramid Lake basin: Hydrothermal alteration, chemical precipitates and geothermal energy potential

Cited by 49 publications

References 17 publications

Microbially influenced lacustrine carbonates: A comparison of Late Quaternary Lahontan tufa and modern thrombolite from Fayetteville Green Lake, NY

Microbially influenced lacustrine carbonates: A comparison of Late Quaternary Lahontan tufa and modern thrombolite from Fayetteville Green Lake, NY

Multi- and hyperspectral geologic remote sensing: A review

Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

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