Mining history in Liberia is often plagued with difficulties of uncertainty of commercial quality and quantity of mineral existence in a particular region. Previous studies conducted at Mount Tokadeh, study area which lies between latitude 7o15’N and 7o45’N and longitude 8o15’W and 8o45’W was distributed in three ore zones; the Oxide Ore, Transitional Ore and Primary Ore. It was also proven that there is some considerable amount of silica and alumina in this ore deposit but the extent of these impurities within this ore deposit were unknown.
The main aim of this research was to investigate the use of information gain from kriging interpolation techniques (Ordinary Kriging, Indicator Kriging and Universal Kriging) to estimate iron ore resources and categorize selective mining unit as High Grade Ore (HGO) or Direct Shipping Ore (DSO).

Field data were processed in excel template and exported into shapefile format inputted into ArcGIS/Arcmap 10.2.1 for interpolation using three main kriging interpolators. Four classes of creative colors were used to delineate the relative quality of mineral distribution within mining site. The final output maps (Prediction map, Probability map and Error of Prediction map) were obtained. Voxler was used to model borehole data in 3D format and was overlayed on the output kriged map for validation. The results showed that Indicator Kriging which uses threshold was the best interpolation method that categorizes the various mining units. Integrated method using Kriging in GIS was introduced and implemented in this work to determine the prospect of using this approach in mapping the spatial division of iron, silica and aluminum content and tonnage of iron ore.

Before 1989, Liberia was the world’s sixth largest exporter of iron ore which was contributing to as much as 64% of total exports and 25% Gross Domestic Product (GDP). However, there has been no production until 2003, making the iron ore sector a prime mover for economic growth (Chadwick, 2011).

Liberia’s mineral development policy and mining code envisaged that exploitation would be balanced appropriately with sustainable environmental preservation (MLME , 2010). Other laws governing the mining sector in Liberia, particularly the National Environmental Protection Agency of Liberia’s Environmental Protection and Management Law, (2002) required a mandatory environmental impact assessment prior to exploitation (MFA, 2003), and the facilitation of conservation of the biological diversity of Liberia. These require the use of knowledge driven exploitation of minerals in order not to degrade the land in search of such, at places without a high probability of ore finds.

Geostatistical methods and Geographic Information Systems can provide the necessary tools for ensuring knowledge-based and targeted exploitation.

Geostatistics uses spatial and temporal patterning to exploit the relationships that help to model potential values of a variable at unsampled points, and these analyzed in GIS, would limit the search of minerals to areas with high probability of mineral ore occurrences.

1.2 Problem statement
The mining industry is often plagued with difficulties mainly due to uncertain existence of the prospected mineral of interest in commercial quantities, the grade and tonnage available and the exact location within a region where minerals are likely to be found. Previous studies conducted in the study area show that ore deposits are distributed in three ore zones (Oxide, Transition and Primary ore zones) (Amikiya, 2014). It was also established that iron concentration increases with decreased silica content in all the ore zones. However, the distribution of iron, silica and alumina in the three ore zones is not known. The applications of geostatistics method can provide a means of estimating both the grade and tonnage of the various grades at unsampled points together with estimated errors. This would reduce the uncertainty or investment risk and helps control the number of exploration drilling requirement as well as establishing decisions to mine based on grade and tonnage.

1.3 Aim and Objectives
1.3.1 Main Aim
The main aim is to investigate the use of information gained from Indicator Kriging (IK), ordinary Kriging (OK) and Universal Kriging (UK) in estimating Iron Ore resources and apply the best method to categorize selective mining unit as High Grade (HG) or Direct Shipping Ore (DSO).

1.3.2 Specific Objectives
The specific objectives are to:
Use an integrated methodology of Kriging in GIS to demonstrate the possibility of mapping the spatial distribution of iron, silica and alumina in the ore deposit of the Tokadeh Study area.

Delineate the relative magnitudes (tonnages) using creative colors to each mineral type at various locations of the study area.

Prepare Prediction maps, Error of Prediction maps, Quantile maps, Probability maps and sampling point map for these mineral deposits using GIS.

1.4 Scope of current work
This work is limited to Mt. Tokadeh concession area of Liberia. The integration of geostatistics and GIS is used to predict the mineral distribution of Tokadeh ore deposits using sampled data collected from 110 drilled bore holes. There are several geostatistical interpolation methods, but in this research, the kriging procedures of interpolations (indicator kriging, ordinary kriging and universal kriging) were used based on the phenomena being studied. Variography is first done to determine which mathematical method is best.

1.5 Relevance of research
The unavailability of estimated ore reserve grades and quantities prior to mining have led to the failure of mining projects and unnecessary degradation of biodiversity even where no ore exist (Dimitrakopoule, 2012). The results of this effort would facilitate decisions on deposit viabilities at different locations in terms of quality and quantity.

1.6 Structure of Thesis
This research is compiled into 5 chapters.

An introduction presented in chapter one includes background, problem statement, objectives, scope of current work, and relevance of research.

In Chapter 2, a review of ore deposit classifications in terms of mineral burden, geostatistical techniques and use in mineral prediction and exploration are made. This chapter further includes challenges of interpolation methods and their relative strengths and weakness.

The material and methodology employed in the current study is presented in chapter

The results of current effort are in chapter 4. The chapter also includes a discussion of these results and deduction made from these analyses.

The conclusions drawn from the study are presented in chapter 5 together with some recommendations for further studies

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Item Type: Ghanaian Topic  |  Size: 73 pages  |  Chapters: 1-5
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