Geological mapping using Random Forests applied to Remote Sensing data: a demonstration study from Msaidira-Souk Al Had, Sidi Ifni inlier (Western Anti-Atlas, Morocco)

2020 ◽  
Author(s):  
Imane Bachri ◽  
Mustapha Hakdaoui ◽  
Mohammed Raji ◽  
Abdelmajid Benbouziane
Keyword(s):  
Remote Sensing ◽  
Random Forests ◽  
Remote Sensing Data ◽  
Geological Mapping ◽  
Sensing Data

Information Extraction and Metallogenic Prediction of Qiangduo Area in Tibet Based on Multi-Source Remote Sensing Data

2014 ◽  
Vol 962-965 ◽  
pp. 127-131
Author(s):  
Xin Xing Liu
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Geological Mapping ◽  
Advanced Technology ◽  
Data Types ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Metallogenic Prediction

Remote sensing technology as a kind of new and advanced technology has been playing an important role in geological mapping and prospecting. A single kind of remote sensing data always has both advantages and disadvantages. And with multispectral remote sensing data types increasing, the integrated application of multi-source remote sensing data will be one of the development trend of remote sensing geology. In this paper, comprehensive utilization of multi-source remote sensing data such as ETM+, ASTER, Worldview-II and DEM, lithology and geological structure of Qiangduo area in Tibet were interpreted in different levels and mineralized alteration information also was extracted. Then on the basis of modern metallogenic theory, analyzed the multiple mineralization favorite information, established the remote sensing prediction model, and on the GIS platform, carried out metallogenic prediction of the study area. The field validation shows that the results of the prediction are relatively accurate and remote sensing technology can improve the efficiency of geological work.

Using ASTER remote sensing data set for geological mapping, in Namibia

2005 ◽  
Vol 30 (1-3) ◽  
pp. 97-108 ◽  
Author(s):  
Cécile Gomez ◽  
Christophe Delacourt ◽  
Pascal Allemand ◽  
Patrick Ledru ◽  
R. Wackerle
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Geological Mapping ◽  
Data Set ◽  
Sensing Data

scholarly journals Lithologic mapping using Random Forests applied to geophysical and remote-sensing data: A demonstration study from the Eastern Goldfields of Australia

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. B183-B193 ◽  
Author(s):  
Stephen Kuhn ◽  
Matthew J. Cracknell ◽  
Anya M. Reading
Keyword(s):  
Remote Sensing ◽  
Random Forests ◽  
Information Entropy ◽  
Remote Sensing Data ◽  
Geochemical Data ◽  
Limited Data ◽  
Additional Tool ◽  
Sensing Data ◽  
Starting Point ◽  
Lithologic Mapping

The Eastern Goldfields of Western Australia is one of the world’s premier gold-producing regions; however, large areas of prospective bedrock are under cover and lack detailed lithologic mapping. Away from the near-mine environment, exploration for new gold prospects requires mapping geology using the limited data available with robust estimates of uncertainty. We used the machine learning algorithm Random Forests (RF) to classify the lithology of an underexplored area adjacent to the historically significant Junction gold mine, using geophysical and remote-sensing data, with no geochemical sampling available at this reconnaissance stage. Using a sparse training sample, 1.6% of the total ground area, we produce a refined lithologic map. The classification is stable, despite including parts of the study area with later intrusions and variable cover depth, and it preserves the stratigraphic units defined in the training data. We assess the uncertainty associated with this new RF classification using information entropy, identifying those areas of the refined map that are most likely to be incorrectly classified. We find that information entropy correlates well with inaccuracy, providing a mechanism for explorers to direct future expenditure toward areas most likely to be incorrectly mapped or geologically complex. We conclude that the method can be an effective additional tool available to geoscientists in a greenfield, orogenic gold setting when confronted with limited data. We determine that the method could be used either to substantially improve an existing map, or produce a new map, taking sparse observations as a starting point. It can be implemented in similar situations (with limited outcrop information and no geochemical data) as an objective, data-driven alternative to conventional interpretation with the additional value of quantifying uncertainty.

Application of ASTER remote sensing data to geological mapping of basement domains in arid regions: a case study from the Central Anti-Atlas, Iguerda inlier, Morocco

2013 ◽  
Vol 7 (6) ◽  
pp. 2407-2422 ◽  
Author(s):  
M’hamed El Janati ◽  
Abderrahmane Soulaimani ◽  
Hassan Admou ◽  
Nasrrddine Youbi ◽  
Ahmid Hafid ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Arid Regions ◽  
Remote Sensing Data ◽  
Geological Mapping ◽  
Sensing Data

scholarly journals A comparison of Landsat 8 (OLI) and Landsat 7 (ETM+) in mapping geology and visualising lineaments: A case study of central region Kenya

2015 ◽  
Vol XL-7/W3 ◽  
pp. 897-903 ◽  
Author(s):  
M. W. Mwaniki ◽  
M. S. Moeller ◽  
G. Schellmann
Keyword(s):  
Remote Sensing ◽  
Knowledge Base ◽  
Remote Sensing Data ◽  
Factor Loading ◽  
Geological Mapping ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Data ◽  
Landsat 7 ◽  
Loading Analysis

Availability of multispectral remote sensing data cheaply and its higher spectral resolution compared to remote sensing data with higher spatial resolution has proved valuable for geological mapping exploitation and mineral mapping. This has benefited applications such as landslide quantification, fault pattern mapping, rock and lineament mapping especially with advanced remote sensing techniques and the use of short wave infrared bands. While Landsat and Aster data have been used to map geology in arid areas and band ratios suiting the application established, mapping in geology in highland regions has been challenging due to vegetation land cover. The aim of this study was to map geology and investigate bands suited for geological applications in a study area containing semi arid and highland characteristics. Therefore, Landsat 7 (ETM+, 2000) and Landsat 8 (OLI, 2014) were compared in determining suitable bands suited for geological mapping in the study area. The methodology consist performing principal component and factor loading analysis, IHS transformation and decorrelation stretch of the FCC with the highest contrast, band rationing and examining FCC with highest contrast, and then performing knowledge base classification. PCA factor loading analysis with emphasis on geological information showed band combination (5, 7, 3) for Landsat 7 and (6, 7, 4) for Landsat 8 had the highest contrast and more contrast was enhanced by performing decorrelation stretch. Band ratio combination (3/2, 5/1, 7/3) for Landsat 7 and (4/3, 6/2, 7/4) for Landsat 8 had more contrast on geologic information and formed the input data in knowledge base classification. Lineament visualisazion was achieved by performing IHS transformation of FCC with highest contrast and its saturation band combined as follows: Landsat 7 (IC1, PC2, saturation band), Landsat 8 (IC1, PC4, saturation band). The results were compared against existing geology maps and were superior and could be used to update the existing maps.

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