A GIS-based borehole data management and 3D visualization system: a case study of Pitisal sand deposit along Puri Coast, Odisha, India

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1 JOURNAL OF COASTAL SCIENCES Journal homepage: ISSN: Volume 2 Issue No Pages A GIS-based borehole data management and 3D visualization system: a case study of Pitisal sand deposit along Puri Coast, Odisha, India T. Ranga Vital a *, G.K. Tripathy b, B. Mishra c a Department of Geology, K.K (A) College, Berhampur, Odisha , India b Tata Consultancy Services, Senior Consultant, Bhubaneswar, Odisha , India c Department of Geology, D.D Autonomous College, Keonjhar, Odisha , India A B S T R A C T The main objective of this study is to develop a prototype model of Geographic Information System (GIS) application for efficient management of borehole data. The study area of pitisal sand deposit is situated on the south-eastern side of Pitisal village (19º31'00":85º12'30") in Krushnaprasad block of Puri district and along southern coast of Odisha which covers an area of 1.46 Sq.km. This study describes the potential use of GIS for developing an expert system for geospatial analysis of subsurface data, and an interactive 3D visualization. The integration of GIS, GPS and database of beach sand mineral layers will be helpful for understanding the subsurface strata. 55 borehole data were collected from different locations of the study area and corresponding coordinates were measured with the help of GPS. Heavy mineral concentration maps were prepared at various planes parallel to sea surface level (-1, -2, -3, -4, -5, -6, -7 and -8 m depth) and a three dimensional subsurface model was generated using ArcGIS 3D Analyst. The result of IDW indicates the variation in heavy mineral concentration and distribution vertically and horizontally which will become a path finder during mineral exploitation. *Corresponding author, address: rangavital@gmail.com Phone: Journal of Coastal Sciences. All rights reserved A R T I C L E I N F O Received 2 March 2015 Accepted 28 March 2015 Available online 31 March 2015 Keywords Borehole data 3D visualization GPS IDW Heavy minerals Odisha 1. Introduction The state of Odisha is a leading producer of placer heavy minerals such as ilmenite, garnet, rutile, zircon, sillimanite and monazite, which are localized mostly in the sandy beaches of Odisha coast. The coastal stretch of Odisha extending for about 480 km coastline which has been greatly influenced by fluvial, marine, and aeolian processes. Occurrence of coastal sand dunes containing monazite, zircon, ilmenite, rutile, sillimanite in south Odisha, especially along Ganjam coast was known since a long time. Borehole data can provide useful information about both surface and subsurface conditions of the earth and they have been frequently used in a number of fields such as natural resource exploration, solving environmental problem, transportation, construction, and so on. Geological data are composed of borehole data, topographical data, geomorphological data and beach sand data etc,. Geographic Information System (GIS) can be one of the solutions for the management of borehole geological data because most of the geological data are referenced to locations of the earth and such spatial complexity can be well accommodated in GIS. The main objective of this paper was to create heavy mineral concentration maps at various planes parallel to sea surface level (-1, -2, -3, -4, -5, -6, -7 and -8 m depth) and preparation of a three dimensional subsurface model using ArcGIS 3D Analyst and to interpret heavy mineral concentration and distribution vertically and horizontally which will be useful during mineral exploitation Study area The present study area (Pitisal sand deposit) is situated on the southeastern side of Pitisal village in Krushnaprasad block of Puri district, along southern coast of Odisha. The location map of the study area is shown in figure 1. It extends from the boundary of Balipatapur to northeast end of Pitisal village. The litho units comprise khondalite suite of rocks, charnockites, granulites, gneisses and younger intrusives like anorthosite and pegmatites of Eastern Ghat group of rocks of archaean age. The quaternaries are represented by laterite and unconsolidated sand, alluvium of marine, aeolian, laustrine, fluvio-marine and fluvio-lactustrine environment found along the coastal tract. The general NE-SW trend of foliation of Eastern Ghats swings locally from NW-SE to E-W in the area (Rao et al. 2001). The sandy tract around Pitisal is elongated in N55 E-S55 W direction and it is bordered by Bay of Bengal in the east and the landward continuity of sand in the west which is marked by low-lying cultivated land and extension of Chilka lagoon. 3. Methodology 3.1 Data acquisition and data base creation

2 Fig. 1 Location map of study area The study area of Pitisal sand deposit was surveyed along the length and breadth for sample collection. There are various data sources included in the process of collecting information for this work. The Survey of India Toposheet No.74E/2 on 1:50,000 scale was used for the preparation of base map. Three main steps to create 3D visualization are developed. Starting from data collection from the field, an appropriate geodatabase for geological and geomorphological data is developed and finally a 3D geomodel to cover both surface and subsurface was created to visualize the characteristics of the Pitisal study area. An attempt is made in this paper for prediction of suitable areas of mineral exploration with advent of GIS comprising various procedure and tools for development of decision rule and predictive modeling. Multi criteria analysis in GIS environment helps to analyze the 3D view of heavy mineral concentration with the help of geostatistical analysis to identify the spatial concentration of heavy minerals horizontally and vertically and also helps to identify the origin of the mineral deposit. The 55 sediment samples were collected from 0-8m depth at different locations covering various landforms (beach, berm, beach ridge, dunes) for heavy mineral studies. GPS survey of the deposit over a coastal length of 0.75 kilometer from boundary of Balipatapur sector to NE end of Pitisal deposit comprising a total area of 1.46 Sq. km was carried out to fix up borehole points and for contouring. For this purpose, a baseline was laid in open beach parallel to the general trend of coastline (N55 E-S55 W in the area). Transverse grid lines were laid at right angle to the baseline with 750m (Figure 2). All the field survey and sample collection inputs are integrated. 3.2 Surface and sub surface sampling The geographic extent of the study area in UTM coordinates are 85 12' " E to 85 14' " E and 19 30' " N to 19 31' " N. Normal field procedures were adopted for surface study, 25 focusing on direct field observations at every 30 m interval of the study area. The exploration work of this area was undertaken by Directorate of Geology, Odisha and this data was used to construct 3D visualization for demonstration of spatial variation of heavy mineral concentrations at different depths and further to highlight the highest concentration of heavy minerals corroborating the data of Directorate of Geology. Only 55 boreholes were considered out of 276 borehole locations as shown in figure 5 and 6 for analytical prospective in GIS and for making the analysis of spatial interpolation easier. The interpolated layer using IDW (Inverse Distance Weight) at 1m interval was created for three dimensional vision in which each of the measured point has a role in the interpolation. It is also assumed that the points which are located in farther distances would have lower effect compared to those which are closely located. 3.3 Software used The GIS software s like ArcGIS Desktop 10 and its extension ArcScene, and ERDAS IMAGINE 9.1, AutoCAD, Global Mapper and Arcview are used for data processing and subsurface visualization. All these software s are very effective and user friendly. 3.4 Heavy mineral separation Representative samples from various sub-environments were subjected to granulometric analysis by conventional sieve and pipette method adopting the procedures outlined by Krumbein and Pettijohn (1966) and Folk and Ward (1957) at 0.5 Phi interval. Broadly the composite sample was washed thoroughly with water and then treated with dilute HCI to remove silt, shell, and organic matter. Then the sand samples were classified into +40, +60, +100, +140, mesh size (ASTM). After separation of magnetite, if any,

3 with the help of hand magnet, each fraction was treated with Bromoform (specific gravity 2.89) for separation of light (mostly quartz) from heavy minerals. Then the heavy mineral fractions were subjected to electromagnetic separation to obtain magnetic and nonmagnetic fraction. The percentages of different constituents of heavy minerals (both magnetic and non-magnetic fractions) have been determined by grain counting under microscope. J O U R N A L O F C O A S T A L S C I E N C E S Before constructing the geodatabase and starting the data interpretation, a systematic and complete data validation is conducted for all records to guarantee that no errors exist. The database consists of several tables that can be prepared in a spread sheet, i.e., borehole id, borehole locations(x, y, z) lithology etc., and integrate to geodatabase. Finally, a data analysis and geological interpretation starts in the 3D modeling system. The 3D borehole data contains borehole location details with heavy mineral concentration can be created using ArcScene, an application program of ArcGIS. For calculation of concentration of heavy minerals and their distribution, the Pitisal spatial database (Figure 3 and 4) stores borehole spatial data of selected 55 boreholes collected in the Pitisal study area, such as borehole location, heavy mineral and geomorphological data. The Integrated geodatabase is quite effective for data management, 3D modeling, viewing and geospatial information query (Sahoo et al. 1999), GIS based multi-criteria analysis and modeling. Fig. 3 Integrated geodatabase Fig. 2 The 55 Borehole samples map 3.5 Creation of geodatabase According to Dangermond (2006), preparation of geodatabase can be regarded as an important step in geological studies. The varieties of functionalities beside its simplicity led to prevalence of geodatabase in recent years in most of the fields of sciences including geomatics and geology. A geodatabase, as a tool in geological study, can contain data of spatial locations and shapes of geomorphological and geological features which are saved as three main categories of point (Shape), line and polygon (Zhou et al. 2007). In ESRI-ArcGIS package, ArcCatalog is capable to create a personal geodatabase. Normally, features with the same types of attributes should be located in a certain dataset. Two main datasets in this study are considered according to the available geographical data, borehole data and geomorphological layers data. In ArcCatalog, after defining a personal geodatabase, two datasets address the correct coordinate positions. Since the study area is located in UTM zone 45 N, it is necessary to deal with two datasets in the corresponding coordinate systems. The feature classes are created based on the data which were imported later as points, lines and polygons. Moreover, any other information and set of data which might be useful later, is added to the geodatabase so that one can access these data easily. 26 Fig. 4 Pitisal spatial data 3.6 Spatial interpolation Inverse Distance Weighted (IDW) interpolation method (Spatial Interpolation) was used for generating the heavy mineral concentration surfaces. Based on the heavy mineral grade attributes stored in integrated geodatabase, using the interpolation tools of 3D Analyst extension in ArcGIS, a series of layered heavy mineral concentration maps (raster and contour) were generated at various planes parallel to sea surface level (-1, -2, -3, -4, -5, -6, -7 and -8 m depth). IDW interpolation can be accomplished when the data points

4 are spaced dense enough, so that the fluctuation in the surface can interfere in the surface s shape (Childes, 2004). Since the method is based on the distance of points from the location of predicted output cell (the assigned weight), the distance between points are definitely important. However, according to Lu and Wong (2007), the method besides possessing advantages such as appropriate speed in operation and simplicity, has its own shortages. They maintain not only the same assigned weight in the study area irrespective of distribution and concentration of data, but a weight according to patterns of the neighbors around un-sampled points. In other words, this weight is based on characteristics of neighbors around unsampled points instead of only distance to the closest sample points (Lu and Wong 2007). IDW is the most suitable interpolation method for the case study. The capabilities of 3D modeling software to do gridding on the one hand, and characteristics of the data points which are distributed over the area on the other hand propose the implementation of IDW. Figure 7 to 14 are created for each layer for total heavy mineral with respect to 1m depth interval up to 8m using ArcGIS 3D Analyst and the result of IDW is indicating the way heavy mineral concentration and distribution varies vertically and horizontally which is useful during mineral exploitation. These depth-wise layers are integrated to form a 3D pattern of the heavy mineral concentration. arrangements of heavies particularly their concentration and distribution. Proper vertical exaggeration was given for the individual layers at 1m depth interval for visualization of heavy mineral concentration as shown in figure 15 and 16. Fig. 7 Spatial distribution of HM 0-1m Fig. 8 Spatial distribution of HM 1-2m Fig. 5 Borehole Location map of the Study area, 3D view of the 55 boreholes Fig. 9 Spatial distribution of HM 2-3m Fig.10 Spatial distribution of HM 3-4m Fig. 6 Borehole Location map of the Study area superimposed with Satellite Image 3.7 3D Visualization of heavy mineral concentration A 3D visualization of the model is a salient approach of this study. The ArcGIS software creates a surface by interpolating 55 borehole points, although it is unable to develop a comprehensive 3D solid model representing both surface and subsurface. ArcGIS 3D analyst and Arc Scene tools were chosen. An attempt was made to design 3D geomodel to study the distribution of total heavy minerals in the study area using the inputs collected from field and laboratory analysis. This geomodel helped in deciphering the spatial Fig.11 Spatial distribution of HM 4-5m Fig.12 Spatial distribution of HM 5-6m 27

5 7 to 14. This shows that interpolation gives more accurate results that are close to the exact field condition. Similarly from the created maps for Pitisal beach placer deposits, complete beach placer minerals profile of any location coming under the study area can be found out accurately. 5. Conclusion Fig.13 Spatial distribution of HM 6-7m Fig.14 Spatial distribution of HM 7-8m An attempt has been made to design a 3D geomodel to study the distribution of heavy minerals in the study area using the inputs collected from field and laboratory analysis. By interpolating the points data, a spatial 3D information system was developed using the ArcGIS software and its extensions. This geomodel helped in deciphering the spatial arrangements of heavies particularly their concentration and distribution. Proper vertical exaggeration was given for the individual layers at varying depths with the help of the 3D visualization of the entire study area with the heavy mineral concentration in individual layers with varying depths. The exclusively very high concentration of heavy minerals has been shown in 3D model with IDW interpolation. 3D models are useful to identify the potential locations of placer minerals for sustainable exploration. This study has confirmed that the GIS is capable to pinpoint the location for optimum exploitation of economically viable beach placer minerals. Acknowledgements The author is thankful to the Director of Geology, Government of Odisha and HOD, Department of Geology KK (A) College for their active cooperation. The constructive suggestions of Mr. P. C. Mishra, Sr Geologist, Govt. of Odisha is gratefully acknowledged. References Fig.15 3D View (Vertical) of heavy mineral distribution (8m Subsurface) Childs, C Interpolating surfaces in ArcGIS spatial analyst, ESRI education services. Available at: Dangermond, J An overview of GIS concepts, the Geodatabase, and the ArcGIS family of products. ESRI Electronic Book: Folk, R. L., Ward, W. C Brazo river bar, a study in the significance of grain size parameters. Journal of Sedimentary Petrology, 27, Krumbein, W. C., Pettijohn, F. J Manual of Sedimentary Petrography. Appleton Century Croft, New York, 549. Lu, G., Wong, D An adaptive inverse-distance weighting spatial interpolation technique. Computers & Geosciences, 34, Rao, R. G., Sahoo, P., Panda, N. K Heavy Mineral Sand Deposits of Odisha. Exploration and Research for Atomic Minerals, 13, Sahoo, N., Jothimani, P., Tripathy, G. K Multi-Criteria Analysis in GIS environment for natural resource development- A case study on gold exploration, Geospatial Application Papers, pp1-4. Zhou, W., Chen, G., Li, H., Luo, H., Huang, S GIS application in mineral resource analysis a case study of offshore marine placer gold at Nome. Computers & Geosciences, 33, Fig.16 3D View (Lateral) of heavy mineral distribution (8m Subsurface) 4. Results and Discussion The deposits in the study area is a multi-mineral placer type comprising valuable heavy minerals. In order to validate interpolation results, IDW interpolation is done for the surface layer with 55 borehole points and maps were generated as shown in figure 28