The spatial distribution of mineralization; applications of Fry analysis

Vearncombe, J.R.; Vearncombe, S.

doi:10.2113/gsecongeo.94.4.475

Cited by 83 publications

(35 citation statements)

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“…These methods were not used in studies of landslide controls, but have been employed in studies of mineralization controls. Fry analysis has been applied to deduce trends of structures controlling mineralization (Vearncombe and Vearncombe, 1999;Carranza, 2009;Carranza et al, 2009) and geothermal fields (Carranza et al, 2008b). Distance distribution analysis has been applied to quantify spatial associations between mineral deposits and geological features (e.g., Bonham-Carter, 1985;Carranza and Hale, 2002;Carranza et al, 2008a).…”

Section: Discussionmentioning

confidence: 99%

Spatial analysis of mutual fault/fracture and slope controls on rocksliding in Darjeeling Himalaya, India

Ghosh

Carranza

2010

Geomorphology

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Section: Discussionmentioning

confidence: 99%

Spatial analysis of mutual fault/fracture and slope controls on rocksliding in Darjeeling Himalaya, India

Ghosh

Carranza

2010

Geomorphology

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“…Fry analysis can indicate the direction of tectonic stress by locating spots and points and calculating their direction distribution, thus indicating the distribution direction of point or line objects. Later, many scholars expanded the scene of application to spatial distribution analysis [39,40] and achieved good results [6]. Fry analysis [41] can operate manually by using a sheet of tracing paper on which a series of parallel reference lines (typically north pointing on a map) have been drawn and the location of each data point is recorded.…”

Section: Hot Spring Data Processing By the Fry Methodsmentioning

confidence: 99%

A Novel Workflow for Geothermal Prospectively Mapping Weights-of-Evidence in Liaoning Province, Northeast China

Sang

Liu

et al. 2017

Energies

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Geological faults are highly developed in the eastern Liaoning Province in China, where Mesozoic granitic intrusions and Archean and Paleoproterozoic metamorphic rocks are widely distributed. Although the heat flow value in eastern Liaoning Province is generally low, the hot springs are very developed. It is obvious that the faults have significant control over the distribution of hot springs, and traditional methods of spatial data analysis such as WofE (weight of evidence) usually do not take into account the direction of the distribution of geothermal resources in the geothermal forecast process, which seriously affects the accuracy of the prediction results. To overcome the deficiency of the traditional evidence weight method, wherein it does not take the direction of evidence factor into account, this study put forward a combination of the Fry and WofE methods, Fry-WofE, based on geological observation, gravity, remote sensing, and DEM (digital elevation model) multivariate data. This study takes eastern Liaoning Province in China as an example, and the geothermal prospect was predicted respectively by the Fry-WofE and WofE methods from the statistical data on the spatial distribution of the exposed space of geothermal anomalies the surface. The result shows that the Fry-WofE method can achieve better prediction results when comparing the accuracy of these two methods. Based on the results of Fry-WofE prediction and water system extraction, 13 favorable geothermal prospect areas are delineated in eastern Liaoning Province. The Fry-WofE method is effective in study areas where the geothermal distribution area is obviously controlled by the fault. We provide not only a new method for solving the similar issue of geothermal exploration, but also a new insight into the distribution of geothermal resources in Liaoning Province.

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“…Fry analysis was originally developed as a manual graphic method of spatial autocorrelation analysis of point patterns to assess strain partitioning in rocks (Fry, 1979), later applied to identify trends in the distribution of mineral deposits (Vearncombe and Vearncombe, 1999;Carranza, 2009;Dirks et al, 2013). The manual process of generating a Fry plot is as follows.…”

Section: Fry Analysismentioning

confidence: 99%

Spatial data analysis of mineral deposit point patterns: Applications to exploration targeting

Lisitsin

2015

Ore Geology Reviews

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The spatial distribution of mineralization; applications of Fry analysis

Cited by 83 publications

References 0 publications

Spatial analysis of mutual fault/fracture and slope controls on rocksliding in Darjeeling Himalaya, India

Spatial analysis of mutual fault/fracture and slope controls on rocksliding in Darjeeling Himalaya, India

A Novel Workflow for Geothermal Prospectively Mapping Weights-of-Evidence in Liaoning Province, Northeast China

Spatial data analysis of mineral deposit point patterns: Applications to exploration targeting

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