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Volume 30 Issue 5
Oct.  2019
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Yuan Ouyang, Hanhu Liu, Xiao Wang, Shusheng Liu, Jinghua Zhang, Hui Gao. Spatial Distribution Prediction of Laterite Bauxite in Bolaven Plateau Using GIS. Journal of Earth Science, 2019, 30(5): 1010-1019. doi: 10.1007/s12583-019-1234-9
Citation: Yuan Ouyang, Hanhu Liu, Xiao Wang, Shusheng Liu, Jinghua Zhang, Hui Gao. Spatial Distribution Prediction of Laterite Bauxite in Bolaven Plateau Using GIS. Journal of Earth Science, 2019, 30(5): 1010-1019. doi: 10.1007/s12583-019-1234-9

Spatial Distribution Prediction of Laterite Bauxite in Bolaven Plateau Using GIS

doi: 10.1007/s12583-019-1234-9
More Information
  • Corresponding author: Hanhu Liu,
  • Received Date: 2018-06-21
  • Accepted Date: 2018-10-12
  • Publish Date: 2019-10-01
  • Mineral resources are the most important natural resources for developing countries. Bauxite is an indispensable mineral resource for industrial production. Potential assessment of bauxite is an im-portant issue in Indochina Peninsula. In this paper, the factors affecting the mineralization of the lateritic bauxite are analyzed. The collected spatial data are processed and the information is extracted to obtain the spatial extent of favorable constraints. Then, the spatial distribution of potential bauxites on the Bolaven Plateau has been investigated with a Boolean modeling process in GIS environment on the basis of some constraints such as rock, elevation, topographical features and vegetation coverage. Finally, based on the hydrogeological conditions and alteration information of Fe3+ and OH-, the bauxite mapping has been carried out. There are twenty bauxite metallogenic areas delineated, with a total area of 750 km2, which is 5% of the entire study area. This has greatly reduced the scope of the field investigation. Seven of the twenty predicted areas were validated in the field and six of them were found to have bauxite mineralization. Using the methods proposed in this study, the potential bauxite for the entire Bolaven Plateau could be achieved much more cheaply than the traditional methods. This study also provides a good idea for the prediction of laterite bauxite in the other regions of the Indochina Peninsula.
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Spatial Distribution Prediction of Laterite Bauxite in Bolaven Plateau Using GIS

doi: 10.1007/s12583-019-1234-9
    Corresponding author: Hanhu Liu

Abstract: Mineral resources are the most important natural resources for developing countries. Bauxite is an indispensable mineral resource for industrial production. Potential assessment of bauxite is an im-portant issue in Indochina Peninsula. In this paper, the factors affecting the mineralization of the lateritic bauxite are analyzed. The collected spatial data are processed and the information is extracted to obtain the spatial extent of favorable constraints. Then, the spatial distribution of potential bauxites on the Bolaven Plateau has been investigated with a Boolean modeling process in GIS environment on the basis of some constraints such as rock, elevation, topographical features and vegetation coverage. Finally, based on the hydrogeological conditions and alteration information of Fe3+ and OH-, the bauxite mapping has been carried out. There are twenty bauxite metallogenic areas delineated, with a total area of 750 km2, which is 5% of the entire study area. This has greatly reduced the scope of the field investigation. Seven of the twenty predicted areas were validated in the field and six of them were found to have bauxite mineralization. Using the methods proposed in this study, the potential bauxite for the entire Bolaven Plateau could be achieved much more cheaply than the traditional methods. This study also provides a good idea for the prediction of laterite bauxite in the other regions of the Indochina Peninsula.

Yuan Ouyang, Hanhu Liu, Xiao Wang, Shusheng Liu, Jinghua Zhang, Hui Gao. Spatial Distribution Prediction of Laterite Bauxite in Bolaven Plateau Using GIS. Journal of Earth Science, 2019, 30(5): 1010-1019. doi: 10.1007/s12583-019-1234-9
Citation: Yuan Ouyang, Hanhu Liu, Xiao Wang, Shusheng Liu, Jinghua Zhang, Hui Gao. Spatial Distribution Prediction of Laterite Bauxite in Bolaven Plateau Using GIS. Journal of Earth Science, 2019, 30(5): 1010-1019. doi: 10.1007/s12583-019-1234-9
  • Aluminum is the most widely used metal in the world, and it is also the second most important metal after steel (Maung et al., 2017). The best raw material for the production of aluminum is bauxite, with the development of the world economy and the accelerating process of industrialization, the demand for bauxite is increasing.

    The world's largest bauxite deposits are mainly distributed in tropical and subtropical regions on both sides of the equator, between latitude 30°S and 30°N. For example, the large gibbsite deposits are located in Guyana Shield, Guinea, Australia, Brazil and India (Tan, 1999; Yang, 1990). However, the Indochina Peninsula, located in the same latitude area, has a relatively low degree of exploitation despite the geological conditions are favorable for the mineralization (Zaw et al., 2014). At present, some scholars have carried out bauxite research in Indochina Peninsula, and obtained some achievements, but these studies mainly focus on basalt weathering process and its products (Schirrmeister and Störr, 1994). Most of these studies are based on the traditional field investigation, laboratory analysis methods, and there are few studies on the prediction of the bauxite spatial locations in this area.

    In the early 1980s, scholars began to apply GIS and remote sensing technology to comprehensive mineralization prediction research. They designed and developed the earliest mineral resource evaluation system, and later proposed the use of evidence weight method for comprehensive mineralization prediction, which is widely used in global mineralization prediction work (Agterberg, 1994; Agterberg et al., 1990). Today, GIS and remote sensing tools permit more accurate mapping of such resources by integrating favorable geological constraints using a GIS-based model (Chen et al., 2017; Wang et al., 2015; Liu et al., 2014). Especially when large amounts of data are gathered together, we need the help of GIS system to delineate target areas for further exploration of deposit. Mineral potential mapping is one field in mineral resource evaluation that is able to exploit GIS technology as a substitute for traditional working methods (Yousefi et al., 2017; Liu et al., 2014; Nykänen and Ojala, 2007). Using the quantitative comprehensive analysis method, the comprehensive information remote sensing prospecting model of the study area was established, and the target area was delineated for each research area, which achieved the purpose of mineralization prediction in each research area. In this paper, a proposed data-driven Boolean logic model was applied to integrate multi-source geospatial data with GIS techniques for mapping laterite bauxite potential. For this purpose, the controlling factors on the mineralization of lateritic bauxite in the study area are analyzed and the conceptual model of bauxite mineralization is established. The topographic information and hydrogeological information are extracted from the digital elevation model; and the mineralization information of bauxite is extracted from remote sensing data. Finally, we use the Boolean model, which has been previously tested in several studies on mineral potential assessment (Guha et al., 2013; Boroushaki and Malczewski, 2008; Carranza et al., 1999). At the same time, because the traditional Boolean logic model is too simple, we choose the favorable area of mineralization in each layer delineated by the Boolean logic model, and use the Euclidean distance method to score for the estimation and assignment of weights to evidential values, to delineate the possible distribution areas of the laterite bauxite for further fleld exploration and geochemical survey. The results have important reference value for the metallogenic prognosis of the lateritic bauxite in Indochina Peninsula and other areas.

  • Bolaven Plateau is located at longitude 105°00′E-107°00′E and latitude 14°00′N-16°00′N (Fig. 1). The north of the plateau is Saravan, the south is the border between Laos and Burma, the west is the Mekong River, and the east is Atapo. This is a north-west spread wide platform.

    Figure 1.  The location and basic geomorphology of the study area.

    The basement of the study area is composed of the Triassic Cretaceous clastic sedimentary strata. The surface is covered by basalt formed by the volcanic eruption in the plateau. The distribution of basalt is centered on the dome of Paksong County, scattered around the plateau, with an area of more than 7 000 km2. The type of bauxite deposits form weathered crusts of Pliocene-Pleistocene tholeiitic basalts on the plateau of southern Indochina (Fan, 2000; Xinh et al., 1990). The spillover and eruption of these plateaus basalts began in the Late Pleistocene, ended in the Early Pleistocene. This provided a rich material foundation for the formation of bauxite. At the same time, it is very favorable to the formation of large-scale and super-large lateritic bauxite deposits coupled with the favorable climatic conditions and drainage conditions in the area (Gao et al., 2007; Hu, 2006; Yang et al., 2005).

    The tectonic position is the magmatic volcanic activity zone of the southern China coastal-Indochina Peninsula on the West Pacific coast. At the same time, it is located in the adjustment zone of the southeastern Peninsula Plate that occurred along the Honghe fault after collision between the Indian Plate and the Eurasian Plate. The volcanic activity of the Meso Cenozoic is strong, and it is the ore-concentrated area of the laterite bauxite (Hu and Gao, 2008; Metcalfe, 2002).

  • The main process of the proposed method is illustrated in Fig. 2. The process involves three steps: (1) Analyze the elements related to mineralization of bauxite, collecting spatial data related to mineralization, including geological maps, the digital elevation model and the Landsat OLI (operational land imager) images. (2) According to the spatial extent of favorable mineralization factors, the feature layers collected by Boolean logic are quantitatively divided according to Euclidean distance. (3) Based on the Boolean model, spatial analysis is carried out in the ArcGIS software to map potential areas for mineralization. The favorable area of mineralization and the alteration information of Fe3+ and OH- are synthetically analyzed, and field investigation is carried out, and the bauxite ore-forming area is finally delineated.

    Figure 2.  Methodology of mapping the potential bauxite.

  • The formation of lateritic bauxite is controlled by the sources of material, topography, hydrologic conditions and so on. It has certain distribution characteristics and laws (Bogatyrev et al., 2009).

  • Bauxites are products of subaerial chemical weathering with residual enrichment of Al, Fe and Ti (D'Argenio and Mindszenty, 1995). Bauxite deposits can be classified into two main types based on the bedrock lithology: bauxite deposits overlying alumosilicate rocks is defined as lateritic bauxites, and bauxite deposits lying on carbonate rocks is identified as karstic bauxites (Esmaeily et al., 2014; Hanilçi, 2013; Maclean et al., 1997; Bardossy, 1982). Due to the high porosity and permeability of basalt, the surface stability of plagioclase, pyroxene and olivine is poor, which is particularly beneficial to the laterite formation. So, the main source of bauxite in this area is basalt, the type of deposit is lateritic bauxite.

  • The control of topography is essentially to provide a good drainage condition, which makes the hydrolysis desilication and the separation of aluminum iron smooth (Ling et al., 2013). Bolaven Plateau is surrounded by cliffs and Canyon landforms. The interior of the plateau is quasi-plain with the low mountain, hill, residual hill and hillock. It has a lower height difference and a smaller slope. The topography of the bauxite area is mostly low mountain and hill, and the secondary topography is the residual hillock of the plateau. The elevation is about 700-1 300 m, and the height difference is usually from tens to more than 100 m. The ore bodies are distributed in the broad and gentle slopes of the ridge and the mound, the stratum of ores in the hilltop is the thickest. It is denuded to low-lying areas and controlled by the topography.

  • Regional climate has a close relation with the maturity of the deposit and laterization of the lateritic bauxite (Price et al., 1997; Schwarz, 1997). The formation area of lateritic bauxite is a tropical and subtropical region with annual average temperature above 22 ℃ and annual rainfall above 1 200 mm (Tan, 1999). The Bolaven Plateau is a monsoon-tropical rainforest climate with an average annual temperature of 20-30 ℃ and annual rainfall of 1 250-3 750 mm. In this climate, the rocks containing alumino-silicate and other rock-forming minerals are rapidly destroyed and separated, the separated alkali and alkaline earth metal ions make the water alkaline. Fe, Mn are easily Oxidized and Ca, Na, Mg, Si are easily lost while Al, Fe are very rich, this tropical weathering effect is the laterization.

  • The dynamic hydrologic system of groundwater directly affects the formation of lateritic bauxite (McFarlane, 1991). Groundwater permeation zone located in the upper part is the main zone for the active component to hydrolyze, the middle groundwater flow zone is in the lower boundary of the laterite bauxite. When the crust is relatively stable and slowly uplifting, the water level also moves slowly down. It can have sufficient time to make a laterization, which is favorable to the formation of bauxite.

  • The vegetation is an indirect mark of the laterite-bauxite delineation in the Bolaven Plateau (Liu et al., 2009; Cheng et al., 2008). In the study area, the surface layer of lateritic bauxite is mostly grassland. The periphery of bauxite is gradually transferred to shrubs and trees.

  • Based on the geological conditions, the metallogenic prediction model of Bauxite in Bolaven Plateau is established (Zhang et al., 2017; Chelgani and Jorjani, 2009; Price et al., 1997). We use the binary non-weighted Boolean logic method (Zaidi et al., 2015; Tré et al., 2010; Macmillan et al., 2007) to perform an "AND" operation on the Boolean logic condition variables for each input data layer (Ebbing et al., 2017; Cheng and Thompson, 2016), the result of the calculation is also a binary thematic map layer.

    Supported by the GIS software platform, spatial overlay analysis is carried out to narrow the range of metallogenic prediction, then potential metallogenic areas are further delineated by synthesizing alteration information, hydrological conditions and vegetation coverage (Yousefi and Carranza, 2017; Liu et al., 2011).

  • The deposit type of the study area is the laterite weathered crust residual gibbsite deposit on the silicate rocks, its main minerals are gibbsite, hematite and goethite. Secondary minerals are kaolinite, quartz and ilmenite. In addition, there are some dilute minerals such as phosphosiderite, chlorite and zircon. The main purpose of extracting bauxite mineralization information is to extract Al3+ and OH-. There are a lot of methods for extraction of OH- from remote sensing images (Pour et al., 2017; Clark and Roush, 1984). However, the signal of Al3+ in remote sensing images is weak and it is difficult to be detected. Bauxite is often associated with goethite and hematite and there is some correlation between them (Costa et al., 2014). It can be said that bauxite is found in areas where there is iron in the bauxite distribution area.

  • Boolean logic is technique customarily applied for knowledge-driven modeling of prospectivity for mineral deposits, whereby weights of values in evidential maps and weights of every evidence map are assigned based on expert opinion (Yousefi et al., 2016). A region that satisfies a specific condition is found, and when the condition is satisfied, it is determined to be "Yes", and when the condition is not satisfied, it is determined to be "No". The final result can be applied to logical "and" and logical "or" operations. The result graph shows only two results, "yes" or "no". The main advantage of the Boolean logic model is simplicity, but the downside is that each standard has the same importance. There are only two results in the resultant graph, "Yes" and "No".

    To generate a weighted geological evidential map, we used the Boolean logic model to calculate various ore-forming layers and delineate favorable ore-forming areas. We also used Euclidean distance method to choose the distance from the favorable region selected by the Boolean model. The favorable regions after classification are calculated according to Euclidean distance and then standardized. At the same time, the layers' assignment ranges in [0, 1].

    X* is normalized result, Xmin and Xmax are the minimum and maximum values of the sample data.

    The final favorable ore-forming area is based on the interaction of multiple influencing factors. Therefore, this paper chooses a multi-factor composite quantitative model to calculate the favorable area of mineralization.

    Y is a favorable area divided by a multi-factor composite model (an area with a value close to 1); X1 is the weight of the rock that is beneficial to mineralization; X2 is the weight of the elevation favorable for mineralization; X3 is the weight of the slope favorable for mineralization; X4 is the weight of the height difference favorable for mineralization; X5 is the weight of vegetation beneficial to mineralization; and X6 is the weight of alterations of OH-, Fe3+ beneficial to mineralization.

  • The main lithology in the study area is divided into two categories, one is basalt in magmatic rocks and the other is sandstone and conglomerate in sedimentary rocks. Bolaven Plateau began to erupt from the Miocene to the formation of basalt, and the lava was distributed in the valley of the plain around Bolaven Plateau (Maycock and Stone, 1994). Basalt is mainly composed of olivine basalt and iddingsite basalt, these rocks are characterized by black, dense massive structure, cryptocrystal microcrystal structure, massive structure and pore structure. The composition and structure of basalt have many advantages in favor of weathering, such as fine mineral particles, porous rocks, and generally columnar joints. Once the rocks are hydrolyzed, the alkali metal and alkaline earth metals are immediately lost and formed layered silicate minerals. The layered silicate minerals become loose and the weathering speed is very fast. At the same time, the bottom sandstone has high porosity and good with the upper basalt, and the drainage conditions are good. The basalt has been thoroughly weathered, thus forming lateritic bauxite. (Hill et al., 2000). Basalt is the only rocks that potentially weathered into bauxite in the study area.

  • Bolaven Plateau is a gentle platform spreading from north to west with an elevation of 300-1 300 m and the average altitude is 1 000 m. According to the collected data, the Bolaven Plateau bauxite is generally located at an elevation of 900-1 000 m (Hu, 2006), 700-1 300 m (Cheng et al., 2012), about 700-1 300 m (Jia, 2011), 800-1 200 m (Diao, 2014). Therefore, the favorable elevation of bauxite in the study area is 700-1 300 m.

  • Topographic factors play an important role in the formation of lateritic bauxite. The mountains with little height differences or hilly terrains are favorable to the formation of weathering deposits. It can ensure rainwater to penetrate the surface of the water level and cause favorable drainage conditions by local erosion. This will have a positive chemical weathering effect (Zhang, 2014). Therefore, for bauxite, the most favorable landform is a little undulating mound or low mountain. Bauxite with high grade can be formed only in rock areas with low height difference, good drainage conditions and high permeability (Cheng et al., 2008). Topography can be characterized by slope and terrain relief.

    (1) Slope: The lateritic bauxite deposits in the study area are distributed on the quasi plain and on the erosion surface where have smaller topographic undulation. Previous studies believe that the bauxite in Bolaven Plateau were layered along the topography, and the slope was basically the same as that of the topographic slope. They think the favorable slope should be 0°-20° (Jia, 2011; Luo et al., 2011; Cheng et al., 2008), 6°-25° (Xue et al., 2009). Based on the analysis of the slope of the bauxite metallogenic area, the favorable slope of the ore area is 5°-25°.

    (2) Topographic relief: The topographic relief is an important index to describe the characteristics of topographic height difference. Different scholars have different views on the topographic height difference in lateritic bauxite areas of Bolaven Plateau. The views are as follows. In the bauxite area, the terrain is low and hilly, and the topographic height difference is about 60 m (Xue et al., 2009). The height difference in bauxite area is generally about 100 m (Luo et al., 2011), < 50 m (Gao et al., 2007), 50-150 m (Cheng et al., 2008). In fact, there is a certain relationship between topographic relief and slope, but they can not be replaced by each other. Considering that the topographic slope of 5°-25° is favorable for bauxite mineralization, in this study, the vertical height difference within a horizontal distance of 180 m is used to calculate the height difference according to DEM (digital elevation model). By calculating the height difference, the 10-85 m height difference zones are the favorable area for the mineralization.

  • There is less overlying vegetation in lateritic bauxite, which can be measured by vegetation coverage. In this paper, the vegetation index is used to approximate estimate vegetation coverage based on OLI data and pixel dichotomy model (Wang et al., 2017; Zhang et al., 2013).

    VFC is vegetation coverage. NDVIsoil is an NDVI value that is completely bare soil or no any vegetation. NDVIveg represents the NDVI value of pixels completely covered by vegetation, that is the NDVI value of pure vegetation pixels.

  • In this study, the US Landsat-8 satellite remote sensing image is processed by radiometric calibration and atmospheric correction. At the same time, the combination of band ratio and principal component analysis (PCA) is used. Among them, OLI bands 2, 5, 6, 7 combinations were used to extract alteration anomalies related to OH- based on PCA (Ali and Pour, 2014); OLI bands 2, 4, 5, 6 combinations were used to extract alteration anomalies related to Fe3+ (Han and Nelson, 2015); OLI 6/7 was used to extract alteration information related to bauxite mineralization. Finally, a new image is formed by combination of PCA (2 567) 1, OLI 6/7 and PCA (2 567) 3. This band combination not only reflects the geomorphological features, but also distinguishes the information of bauxite mineralization, OH-, Fe3+, sandstone and vegetation.

    Compared with the known mineral map, we chose information such as bauxite, iron ore, river, forest and sandstone as the reference endmembers. The spectral angle mapping method was used to classify and get the final classification map (Fig. 3).

    Figure 3.  Score of favorable mapping constraints. (a) Rocks; (b) elevation ranges; (c) slopes; (d) height differences; (e) vegetation coverage; (f) alterations of OH-, Fe3+.

  • The spatial evidence of the potential areas of lateritic bauxite is limited by the different geological features described in Section 3. Firstly, the basalt distribution area is delineated by geological map and remote sensing images. Secondly, we determine the favorable range, slope, topography and vegetation, and the range of metallogenic favorable region is mapped by binary non-weighted Boolean logic method (Fig. 4). Finally, the potential range of bauxite is obtained by synthetically analyzing the superposition of iron-stained and hydroxyl alteration information (Fig. 5).

    Figure 4.  Sketch map of the analysis process of bauxite metallogenic area.

    Figure 5.  Prediction distribution ranges of bauxite mineralization.

    Figure 6.  Field survey of the potential area. (a) Weathered sections of basalt; (b) weathering surface of basalt; (c) basalt with a vesicular structure; (d) basalt weathering surface, pore enlargement.

    A total of 20 bauxite potential regions have been delineated in this study. According to the characteristics of remote sensing images, topography, hydrogeological features and alteration information in the potential area, the 20 predicted potential areas are divided into four categories. Among them, the first target is the potential area P4. The metallogenic geological conditions and topography conditions are good in this area, and the mineralized information extracted from remote sensing images is obvious, concentrated and distributed in large area. The second target is the potential areas P1, P2, P5 and P9. The four regions have favorable metallogenic geological conditions and good topographic features. The mineralized information extracted from remote sensing images is obvious, and the mineralization and distribution area has a certain scale. The third target is the potential areas P6, P8, P17, P19, P20, these areas have metallogenic geological conditions and mineralized signs, and the alteration information extracted from remote sensing image has certain mineralization features. However, since the remote sensing image information is more scattered or the features of topography are less advantageous, the range of the bauxite is not easy to be delineated. It is noteworthy that the potential range of P19 is large and the topography and geology conditions are good, but its vegetation coverage is slightly higher. The fourth category is the remaining potential area. These potential areas basically have metallogenic geological conditions and topography features, and have some alteration information signs, but the intensity, the mineralization scope and the potential area is smaller than others.

  • This route is mainly aimed at the bauxite in Bolaven Plateau to verify the potential areas. A total of 7 potential areas were verified because some roads were blocked or muddy. We have completed the observation of 8 geological points, 13 photographs and 7 samples.

    There are 7 potential areas in this survey, of which 6 potential areas (P4, P8, P10, P15, P16 and P17) are all weathered basalts with bauxite mineralization; while the P2 potential area is mainly sandstone and no bauxite mineralization showing.

    Through the anomaly verification, the characteristics of the regions with better bauxite mineralization are as follows

    (1) The original rock is dark and massive basalt with obvious stomatal structure. After the weathering of the surface, the porosity becomes larger and the color becomes tawny.

    (2) The topography is mainly undulating hills and plateau edge. The landform is mainly sparse forest, grassland, bare land and agricultural land.

    (3) From the exposed profile, there are three layers from top to bottom: One is the humus and laterite layer, the main ingredient is purple red, brown red clay, gravel clay, humus and eluvial material; local nodular bauxite containing a small amount and a small amount of limonite. The second layer is bauxite belt. The layer is brownish-red, khaki-containing clay bauxite, the ore is nodule, block. The mineral composition is mainly gibbsite, content is 30%-90%. The secondary minerals are limonite and clay minerals. Bottom layer is bedrock. It is usually the Cretaceous weathering layer or the basalt. The Cretaceous weathering layer is sand and mudstone lithotripsy. Basalt remnant lithology is dominated by weathered basaltic fragments; part of the basalt weathering strong, can form brown yellow, reddish brown, maroon clay, gravel clay.

  • The distribution range of favorable metallogenic factors determines the range of the potential of bauxite, and the characteristics of different metallogenic elements are different. There are two different types of favorable metallogenic features.

    The first type is the favorable rock and elevation for the laterite bauxite. These potential areas have a good consistency and the number of surface graphic elements is less (Fig. 3). The advantage of this type is that it has a clear range of potential metallogenic areas.

    The second type is favorable slope, relief and vegetation. These are calculated by digital elevation model and remote sensing images. This kind of potential areas have a poor consistency, and the number of surface graphic elements is increasingly scattered (Fig. 3). This has a direct impact on the continuity of potential metallogenic areas and the probability of mineralization. When delineating the specific mineralization area of bauxite, we have to follow this principle: the favorable metallogenic factors with good connectivity have good metallogenic probability and vice versa.

  • In this paper, the range of metallogenic regions which have been calculated by binary non-weighted Boolean logic method based on favorable metallogenic elements is still very wide, its area is 2 392 km2. This range is still quite challenging for field surveys. Therefore, it is necessary to further reduce the potential area of bauxite mineralization. Hydroxyl and iron dye alteration information has played a great role in this process.

    However, alteration information is weak and its distribution is scattered, the best way to apply alteration information is artificial visual judgment. Through the application of alteration information, the final delineation of the metallogenic range is only 750 km2, which is 5% of the whole area, and this greatly reduces the workload of field investigation. It is noteworthy that most of the bauxite mineralization areas discussed in this paper are areas where vegetation is not well developed. However, some scholars have suggested that the vegetation in the bauxite deposit area is dense (Jia, 2011; Luo et al., 2011). On the basis of the existing multispectral data, it is difficult to extract alteration information from vegetation dense areas. In order to carry out alteration identification in dense vegetation areas, the future remote sensing technology with satellite carrying hyperspectral will be a good solution, but the current satellite hyperspectral technology is still at an experimental stage.

  • In this paper, by using multi-spatial data and the corresponding logical model, a favorable area for bauxite mineralization in the study area is obtained. At the same time, according to the multi-spectral satellite remote sensing data of OLI, metallogenic alteration information extraction model has been adopted to extract the mineralization information of lateritic bauxite. The metallogenic prognosis of the lateritic bauxite deposit in the study area is defined by the comprehensive hydrogeological conditions and alteration information.

    The results of this study can be considered as an important input for the assessment and classiflcation of lateritic bauxites in Bolaven Plateau. Using the methods proposed in this study, the bauxite potential for the entire Bolaven Plateau could be achieved much more cheaply than the traditional methods. This idea helps to promote the investigation of the laterite bauxite near the equator.

  • This work was supported by the China Geological Survey (No. 121201010000150013) and the National Natural Science Foundation of China (No. 41102225). The final publication is available at Springer via

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