Which of the following represents a concern associated with the exploration stage?

15.10.7 Geostatistical Applications in Mineral Exploration

Mineral exploration requires high investment, sustained cash inflow, and considerable time with inherent high risk. Exploration drilling is planned in a grid pattern to be conducted in sequence to facilitate midterm assessment of quality, quantity, and reliability of the estimates. Traditional procedures are unable to provide the degree of reliability/confidence limits. The application of mathematical models can quantify global precision and bring forth decision-making criteria at the end of each stage. These techniques evaluate sequential exploration data to optimize sampling for specific objectives. It helps in decision-making to continue or to keep the project in abeyance (Haldar, 2007). The standard available procedures are: theory of probability distribution, frequency, mean, variance, standard deviation, trend surface, semivariogram, and kriging. Drill samples share a major part of investment during exploration and justify critical analysis at every phase. The ongoing drilling program is modified accordingly. Statistical/geostatistical methods are equally appropriate during mine production and at the time of mine closure.

1.4.8 Royalties and Taxation

The fiscal policy related to application and registration fees, royalty, income tax, compensation to land owners, rehabilitation cost, and annual and dead rent applicable to holders of mineral exploration and mining rights varies between countries and even states. Mineral royalties are exclusively state/provincial/territorial earnings and constitute a significant revenue source for the state/provincial/territorial government. Mineral-rich states like Queensland, Western Australia, California, Ontario, Northern Province of South Africa, and Rajasthan receive more than 10% revenue from the mineral sector. Neither the rates nor the methods of calculating royalty are uniform. The rate of royalty in respect of copper, zinc, and gold ore, removed or consumed by the leaseholder or his/her agent, is compared in Table 1.7. The royalty and taxation rates vary as amended by the federal government from time to time.

Table 1.7. Summary of Comparative Royalty Rates in Various Countries

Abstract

Mineral exploration is a complex process involving rigorous field work to collect data and conceptualization to fit the data into a plausible theory. Mineral exploration proceeds step by step taking into account aerial photographs, satellite imagery, geological maps, geophysical maps, and geochemical maps to delineate areas of interest. Data generated by these techniques are stored in a thematic and textual database using suitable software. Various techniques of combining and compressing these data have developed over the years, where computers play an important part. These data can be utilized for 2D or 3D modeling or for data mining. The data can be selectively combined to obtain information for likely locating of ore deposits.

Different techniques of data modeling and data interpretation that have recently developed are discussed and how and where they have been used has been described briefly.

Introduction

Mineral exploration aims to discover deposits of minerals and rocks that can be used to meet the resource needs of society. It encompasses the search for industrial raw materials (e.g., clay, limestone, sulphur, salts, and fertilizer minerals and rocks), ores from which metals are extracted (e.g., iron, copper, and zinc ores), and gemstones (diamonds, sapphires, and opals), and includes the search for solid fuels (coal, oil shale, and uranium) but not liquid or gaseous fuels (petroleum and natural gas). Mineral exploration can be as basic as prospecting, using elementary techniques such as panning for gold, or it can be very sophisticated, involving the use of complex technology for data gathering and interpretation. This article considers our need for mineral resources, why different groups explore for minerals and the strategies they adopt and the tools they employ, what success and failure mean in the industry, the role of governments, and the future of exploration.

4.1 Definition

Mineral exploration is a complete sequence of activities. It ranges between searching for a new mineral prospect (reconnaissance) and evaluation of the property for economic mining (feasibility study). It also includes augmentation of additional ore reserves and resources in the mine and total mining district. Various exploration techniques have been followed over the centuries. Exploration is conducted by one or a combination of multiple available global techniques and depends on the demand of the commodity being searched for, convenience of infrastructures, funds from the exploration institution, size and complexity of the deposit, price of end products, government policy, good will, and tax and royalty structures. Programs include multidisciplinary data generation in sequence. In addition to technical inputs, activities encompass collection of information about the infrastructure around the area, such as accessibility (road, rail, nearest rail-head, airport, and sea port), average rainfall, availability of potable and industrial water, power grid and supply system, local community, living conditions, health care, security, forests, and environmental issues. Background information about agencies from federal and state/regional/provincial governments and the public and private sectors, including multinational companies engaged in any mineral exploration program in the area, will be beneficial.

7.3 Mineral Exploration

Mineral exploration can be technically defined as “all the activities and evaluation necessary before an intelligent decision can be made establishing size, initial flow sheet, and annual output of new extractive operation.” The purpose of mineral exploration is the discovery and acquisition of new mineral deposit amenable to economic extractive operations now or in future. The prime objective of mineral exploration is to find and acquire a maximum number of such economic mineral deposits at a minimum cost and within minimum time.

Mineral exploration acceleration is due to (1) increasing demand for metals that were not sought earlier, (2) growth of industrial output, (3) new ore types, and (4) greatly improved geological knowledge and exploration technology. Global demand for industrial commodities has doubled every 20 years. This growth is likely to continue. Global reserves and resources for some commodities are sufficient for several decades. In major producing districts, the reserve and resource grades are declining owing to depletion of high-grade surface deposits. Additional resources will be augmented by ongoing “greenfield exploration” in frontier countries and also by deeper discoveries from known mineral districts. These discoveries will become a major new source of future mineral supply (Woodall and Duncan, 1993). Despite some of the countries have the capacity to support production, it is probable that might be undermined by a few risk factors.

Mineral exploration represents the highest risk of all—risk of failure is great and the cost is high. Even the successes must be followed by capital-intensive long-term, high-risk operating investments. Investments to bring exploration discoveries into production may range in the tens of million dollars. Large mines require from hundreds of million dollars to billion dollar investment. The typical lead time span, from inception of exploration through the investment in mine/mill facilities that will return this investment out of production, is 12–15 or more years. To receive a profitable return on investment commensurate with the risk requires a minimum investment longevity of an additional 10 years, for a total of 20–30 years.

Since most of the surface or near-surface resources have been explored, the search for new mineral resources has to rely on more sophisticated prospecting/exploration techniques. Future ore bodies will probably be more costly to find, mine, and process than those in operation and production now, because most of them will either be of lower grade or found at greater depths. The chances of a mineral occurrence being developed as a mine are low (may be 1 in 1000). Lesser number of identified resources does not imply low mineral potential in an area. It might be due to many reasons like, incorrect geological theories, poor infrastructure and policy of the Government. A typical example is the “Diamond discoveries” in Northwest Territories in Canada. There were no known occurrence of “kimberlite pipe” (host of diamonds) although the geology was known to be favorable. But once the kimberlite pipes were discovered, the area attracted many exploration teams and led to significant resources.

Most exploration projects will not advance to mines. In areas with good potential for discovery, the competition is usually high. Invariably an interdisciplinary team of geologists, geophysicists, and geochemists searches for mineral deposits in prospective terrains. The key to successful exploration programme is to know, when and where to drill, when to “hold the properties” and when to “walkout.”

10.6.1 Modeling: A Holistic Dynamic Approach

Mineral exploration flow diagram is sequentially synthesized to evaluate property at the end of each stage for economic significance and opens two alternative paths suggesting either to “level pass” and continue successive exploration activity or to store conditionally on the shelf for the future (Fig. 10.11). The objectives of search and preparation requirements are defined at the beginning. It is proposed to analyze demand–supply scenario of a mineral or a group of minerals at the national and global levels for prioritizing long-term investment policy. The common key parameters are discussed with linkage if they exist. Understanding the stratigraphic horizon is essential to define a broad target area.

The modeling approach guides the search for a favorable host environment. A preliminary field check along with some spot geochemical samples from probable host rocks may indicate the significance of an area for submission of “Reconnaissance Permit,” sequentially followed by Prospection and a Mining Lease.

15.9.7 Geostatistical Applications in Mineral Exploration

Mineral exploration requires high investment, sustained cash inflow and large time with inherent high element of risk. Exploration drilling is usually planned in grid pattern to be conducted in sequential and dynamic manner. The mechanism in such campaign is midterm assessment of quality, quantity and reliability of estimates. The traditional procedures are unable to provide the degree of reliability. Application of mathematical models can quantify the global precision and bring forth decision-making criteria at the end of each stage. Various statistical and geostatistical procedures are discussed in Chapter 9. These techniques can be used to evaluate sequential exploration data with an aim to optimize sampling for specific objectives. It helps in decision making to continue or to keep the project in abeyance (Haldar, 2007 [33]). The standard procedures are the following: Theory of Probability Distribution, Frequency, Mean, Variance, Standard Deviation, Trend surface, Semi-variogram and Kriging. Drill samples share major part of the investment during exploration and justify critical analysis at every phase. The ongoing drilling program can be modified accordingly. Statistical-geostatistical methods are equally appropriate during mine production and at the time of mine closure.

8.2 What is the exact issue for mineral extraction in SAR data?

Mineral exploration algorithms and procedures are totally absent in SAR data. Most of the geologists misunderstand the nature of radar coherence systems that are different from optical remote sensing based mainly on spectral signature detection of different materials. Main indices can detect from radar data are linear features such as lineaments, which are achieved by using an edge detection algorithm such as the Canny algorithm. In most investigations, the easier approach to implement SAR image in mineral exploration just fuse it with spectral signature data of visible-near infrared, VNIR, and shortwave Infrared, SWIR using Principal Component Analysis, Band Ration, and Independent Component Analysis (ICA). In this perspective, SAR system has the longest wavelengths than VNIR and SWIR, which leads to different of the resolutions. In this view, quantifying the reflected radiation in three bands between 0.52 and 0.86 μm (visible-near infrared, VNIR) with 15-m resolution, and six bands from 1.6 to 2.43 μm (shortwave Infrared, SWIR) with 30-m resolution must be different with radar system resolution ranges between 1 and 12 m. In addition, SAR images as explained early are suffered from layover and foreshortening and shadow. Such issues impossible to be solved by PCA and ICA techniques. The acquisition time differences between optical remote-sensing data and orbital SAR data does not allow accurate matching between both data. In other words, SAR image is sensitive to dielectric variation of the same acquisition zones owing to soil moisture fluctuations over the time. Consequently, If surficial material is assessed based on soil moisture, steep incidence angles are preferred to minimize the backscatter associated with soil roughness. If surficial materials are assessed based on soil surface roughness, shallow incidence angles are better suited. However, geologists misunderstand the mechanism of the SAR incidence angle for geological feature views when they are implementing PCA technique with SWIR and VNIR (Mura et al., 2007; Barnard, 2010).

Although geologists implemented the spectral libraries, for instance, from such platforms as ENVI, the output results are not accurate. In fact, such spectral libraries are collected from specific areas under different circumstances such as weather that is not in many other global zones. The question is: How inaccurate can spectral images fuse with SAR data using PCA? It can be demonstrated that areas A and B in Landsat TM data are different from the same areas in PCA merged image of ERS-2 and Landsat TM. For instance, area B in PCA image tend to sharp identifications to such drainage features. However, the shadows owing to SAR look-direction between drainage features such as overlapping false information are caused by false color that is acquired by the PCA procedure (Fig. 8.3).

Lastly, geologists practice PCA for lineament and other geological structures extractions. The most important to identify geological structure is edge boundary, which is required standard edge detection algorithm such as the Canny algorithm, not PCA technique. This can lead to uncertainties in SAR data interpretation for mineral explorations. Moreover, using directional filter technique is just caused numerous ambiguities in edge boundary detection in SAR images and artifacts Fig. 8.4.

The ambiguities cause by PCA and directional filter are reveals in differences between manually lineament extraction and one derived by directional filter. In this regard, it could be the short lineament features derived by directional filter owing to impact of artifacts produced by shadows and layover. In addition, directional filter sensitivity to the noise, in the detection of the edges and their orientations. The increase in the noise to the image in such speckle SAR data will eventually degrade the magnitude of the edges. The major disadvantage is the inaccuracy, as the gradient magnitude of the edges decreases. This why directional filter create the shortest edge boundaries as compared to manually extracted lineament from SAR images (Huadong et al., 1993; Abdelsalam et al., 2000; Barnard, 2010).

1.1.5.2 Mineral exploration—target evaluation

Mineral exploration is the second stage of the key exploration activities to locate surface mineralization and any old workings in a smaller area of identified geological, geochemical, and geophysical anomalies. It is therefore the follow-up of geological, geochemical, and geophysical anomalies from a reconnaissance survey. Again in this stage geological, geochemical, and geophysical methods are employed to follow up the anomalies or targets.

At this stage, pitting, trenching, sampling, and pit–trench wall logging program are conducted to delineate the bedrock geology of the area and obtain geochemical samples in order to confirm the source of geochemical, geological and geophysical anomalies that were found in the reconnaissance survey and determine accurate grade estimates of the deposit. A hand drill or an auger may be used for shallow drilling to understand the geology of the anomaly and ascertain the position of the deposit.

Soil and rock geochemical sampling, analysis, and interpretation are conducted to ascertain or confirm the presence of mineral deposit. The area is gridded and soil samples are taken at fixed intervals to offer a more clear shape and structure of the ore body. At this stage, ground geophysical surveys may be undertaken for conductive ore bodies like sulfide and oxides mineralization. The geophysical methods may include gradient array induced polarization, resistivity, magnetic, and gamma-ray spectrometry (Gadallah and Fisher, 2009).