Database Management and Quality Assurance is Key of Success in Exploration

Database Management and Quality Assurance is Key of Success in Exploration

Harman Setyadi1, Komang Anggayana2

1Doctorate Student, Mine Engineering Study Program – Faculty of Mine and Petroleum Engineering,

Institute Technology Bandung, Jl. Ganesa 10, Bandung 40132 – INDONESIA

e-mail: [email protected]; [email protected]

2 Earth Resources Exploration Group – Faculty of Mine and Petroleum Engineering,

Jl. Ganesa 10, Bandung 40132

e-mail: [email protected]

Abstract

Exploration is the risky part in the mining business stage. The final goal of exploration is to discover an economic deposit in the certain area; generally by using the three main stages of exploration which are to find, to prove and then to evaluate the presence of minerals deposit. All the three tasks are required the exploration database which is commonly consisting of spatial information of raster, vector and point data. The different type of data is required different data handling as well as data storage system.

To reduce the risk, exploration should be done phase by phase. Commonly every single phase of exploration have different propose, method and data collected. Understanding of the exploration business processes, activities and what kind of the data collected and/or required for every stage is important to design and improve the database system itself. In addition, the growing of information technology and computers science, and the increase of data exploration itself; the exploration evaluation will rely more on digital data processing. Database management and quality assurance awareness is critical to build the solid and correct exploration information for further analysis and evaluation of every exploration stages. The database is the primary input to create decision for further exploration stages

Key word: Exploration, Database, Quality Assurance

INTRODUCTION

Exploration business is different from the common business. The goal of exploration is to discover the economic natural resources deposit thorough out in the several stages to reduce the business risk. The natural resources typically is not renewable that mean limited of volume and time for the exploitation.

With increasing the number of natural resources which have been discovered and mined out, to discover new natural resources will increasingly difficult. Generally the natural resources which are located on the near surface as well as which are have high economic value had discovered and mined. The challenging of current and future mineral exploration is to discover new economic mineral deposits which are not well exposed and only have weak anomaly signatures.

The increasing natural resources demand is as the consequence of the global economic and population growing which more hungering of the natural resources need. The increasing demand and resource depletion will drive up the price of resource commodities, that mean the natural resources which had evaluated was not economic should be considered economic. This phenomena also threating of term of the sustainable mining (Tilton, 2010; Graham 2008)

The improving technology especially for computer and information technology have been employed to assisting the exploration data processing evaluation. Data analog such as maps, photo and other information which commonly have big size previously have problem for data storage and retrieving, currently have no significant issues.

EXPLORATION STAGES

To reduce the potential risk, exploration should be done phase by phase. Generally the early stage is using the broad exploration method to cover the larger area with lower cost. To the further stage, exploration work should be more focused to the specific of interest area. Exploration work should be more intensive with increasing sample density, detailed geological observation and study. The advanced sampling method to improve the sample quality is required more sampling cost. For the example rock grab sampling from outcrop is cheaper comparing the continuous channel or trench sampling. Channel or trench sample may require digging the outcrop before sampling which mean more time.

Basically mineral exploration is the process to collect the data and/or information related to the mineral occurrence in the surface. Exploration evaluation is the process to compile, interpretation and analysis various geospatial data set which commonly using the Geographic Information System (GIS) technique to generate the mineral occurrence potential maps (Majoribank, 2010). In the past (before 2000) commonly the data are recorded and plotted in the paper maps which take more time for compile and less combination data used. Different data set combination to use for analysis should be different resolution of the mineral potential map for exploration target.

Exploration data is required for evaluation, to assess the potential economic of the natural resource occurrence. In the process exploration data integration conversion into exploration knowledge is required the meaning or usefulness of data different type particular data set to be compared. (Majoribank,2010). Understanding the process data collection also the important think to access the potential data error.

Table 1: Mineral Resource Classification Related to the Exploration Works.

Table 1 presented the resource/reserve classification based on JORC-2004 and SNI-13-6011-199 related to the exploration phase and the common exploration methods used.

EXPLORATION DATABASE

Figure 1 presented the suggestion of the exploration database model related to the mining business processes, which is triggered by the mineral resource demand. Exploration is the process to find, to prove and to measure the mineral deposit which is suggested to follow the exploration stages to reduce the exploration business risks (Anggayana, 2011).

Exploration, especially for mineral exploration is the process to gathered the geo science data and/or information using specific method which will be recorded in the special data type to able all information plot/view in the two or three dimension map. Geo Science information which is commonly consist of geological, geographical, geophysical and geochemical information are collect phase by phase. Data density per unit area, data quality and data type will increasing follow the maturity of the exploration stage.

The most complex, important and advance of exploration data is the drilling data. This information is definitely requested to prove then to measure how much the volume and what grade the resource was discovered. Drilling data and other surface spatial data is used to create the geological model. This model itself should be a dynamic from simple to complex following the data complexity.

Exploration database is the one of data source for the mineral resources modeling and calculation. The advance exploration data also recorded the geotechnical and geohydrology aspect, which it is collected during the exploration drilling campaign. This kind of information is required for the mine design and mine operation.

Figure 1: An example of Drill Core Sample which containing Geological Information and Recorded Relative Spatial Location.

Figure 1 is show the example of drill core photo which showing lithology variation, different of alteration, mineral composition and it distribution as well as relationship of different mineral occurrence. All of this information should be observed and logging by geologist then all information containing in this drill core should be translate and recorded in the standard database format. The core depth also recorded to preserve the spatial location, which will use for calculation the coordinates of each sample using the down hole survey information.

Drill core is not only containing the geological information but also the geotechnical aspect such as joint or fractures frequency, condition and type, rock strength. The most important is the metal containing in the drill core. Drill core should be analysis to get the grade information as well as what the other metal contained.

Although all the data already recorded in the database properly, the core photo should be storage in the proper system, so this information is easily to access. All the information contained in the drill core sample may or not may all have been observed and record properly. So in the future, if any doubt regarding the existing information recorded in the database should be recheck and validate in the core photo information. It is an example of the important thing to understand what exploration data should be transfer and recorded in the exploration database system.

Figure 1: Simplified the Exploration Database Model belongs in the Mine Business Processes.

During the mine operation, some additional geo-science information which were not intersected by the exploration drilling should be added and/or confirmed. Grade control sample during the mine operation should be comparing to the original geological model and/or resource model to modify the previous model. In advance the additional infill drilling should be required to full fill the ‘gap’ information from the previous model. The modify model must be run from the combined old drilling data and the additional data.

EXPLORATION KNOWLEDGE DATA DISCOVERY

Knowledge Data Discovery (KDD) or data mining is the process of analyzing data from different perspectives and summarizing it into useful information; the process of finding correlations or patterns among different in a large data set. The overall goal of the data mining process is to extract knowledge from an existing data set and transform it into a human-understandable structure for further use (Anderson, 2012, Wikipedia 2012, Saptawati 2011, Jiawei 2006).

In general the goal of Knowledge Data Discovery (KDD) itself it is not much different with the mineral and/or resources exploration which is to discover the new mineral deposit. The principle of the mineral exploration is the integrated and analyses several different data set to find the new pattern or anomalous which maybe represent the mineral deposit on the subsurface.

Figure 2: The Suggested Exploration Data Mining and Business Intelligence (modified from Saptawati 2011).

The suggested Exploration Business Intelligent modified from Saptawati (2011), is presented as Figure 2. The base on the exploration database system is the data source organization and administration including data repository from the exploration field campaign which is commonly collect the data and information of geological, geochemical and geophysical feature using exploration method and tool.

Business intelligence (BI) is defined as the ability for an organization to take all its capabilities and convert them into knowledge. This produces large amounts of information which can lead to the development of new opportunities for the organization. BI technologies provide historical, current and predictive views of business operations (Wikipedia, 2012). This technology is required for digging the exploration database to evaluate what have been done in the past regarding the data evaluation as well as what the exploration program have been taken. In some case, the first evaluation may not correct and/or required specific exploration approach to define potential deposit.

Data Source Administration

The common data sources of the mineral exploration are geological data, geophysical data and geochemical data. This data commonly storage as the point data which contain sample location with Easting and Northing ± Elevation and the sample description ± sample analytical results.

Geological information commonly recorded the sample point as spatial data geological observation: lithological, alteration, mineralization, and structure. Sample location in the spatial information with Easting and northing location may recorded by GPS reading and/or plot in the field map which should be transferring to the digital data. Some geological information such as structure, lithology and it boundary may not record as point data type but should be recorded as the vectors data. Vectors data should record and storage using ACAD and/or GIS software as a map. The vectors data can’t put in the same data base

Geophysical data is commonly collected using the continuous geophysical survey to detect the potential distinctive physical properties below the surface. Geophysical data generally collected as point sample but report and interpret after converted to the raster and/or vector data.

Geochemical sampling is commonly collected during and following by geological mapping. Geochemical sample should be analysis in the commercial lab using specific wet chemical method such as Fire Assay, Atomic Absorption Spectral (AAS), Inductively Coupled Plasma Mass Spectrometry (ICP-MS) etc. outside of the exploration area. Sample administration database is the important task to track the sample from field until the final assay has received and uploaded to the database. Sample administration should be aware in matching between the sample identification from field and laboratory. Any discrepancy should be solving immediately with the sampler person.

Understanding the sampling procedure, sampling dispatching and the laboratory analytical knowledge is important to design the sample administration database system included for system validation and control. Geochemical data is the important data for the mineral exploration. This data will use for exploration evaluation from beginning until the mine closure. This data should be able to be audited from the sampling procedure, laboratory analysis procedure and data storage.

Similar with geophysical data, geochemical data are collected as point data; as spotted sample or systematic sampling. Data interpretation should be visualized as point data, vector or raster or both type information to get better resolution and able to define anomalies.

Remote sensing data collected using satellite or airborne survey. Remote sensing data commonly presented as image or raster data.

So, in the mineral exploration business they are mainly two different databases to store the different data format. However for data evaluation the data should be integrated together in one system to visualize all the existing data and information to get the comprehensive integrated knowledge of the exploration target area.

Figure 3 shows the simplified exploration database flow. Data source is generated during the exploration field campaign. Exploration field mapping is initially by the field mapping plan based on the existing information to focus to the exploration target area. They are two main works in the field which are to collect the geological information and to collect sample for geological and geochemical analysis.

Figure 3: Simplified Exploration Database Model for Geological mapping and sampling

System Management & Quality Assurance

Exploration database is the long-life data; the oldest exploration data should be having value during the exploration and mining life. Figure 1 also explains that the exploration database which mainly use for the exploration purpose use for data evaluation to discover new deposit, also will use for mining support during the mining life. The exploration database should be put in the accessible long term database without data archiving policy. With this characteristic, of course data quality assurance and awareness is important to develop the big view of exploration database design.

Exploration database management not only for the database development but also to involved the data quality and validation, data tracking and self-audit. Standard Operating Procedure (SOP) should in place to ensure the consistency of how data collected in the field, how data captured in database and how data pre-processing inside the database system

To design the exploration database, suggested the database developer should have good understanding and/or knowledge regarding the exploration business and its aspects. If the database was initiated from the first stage exploration (reconnaissance program) database should have capacity to growing up in term about the data size as well as the database complexity.

In the early exploration phase is not important to use the sophisticated and complex database system. The important thing in this stage is to design a simple database which able to migrate in easy way to the future advances system, understand the exploration business risk which potentially growing rapidly or may fail to discover the potential deposit in the initial stage. The potential risk using the advance system in the early stage of exploration is if fail to find the potential deposit in the interesting area. Figure 3 also presented the potential database growing following the maturity of the exploration stage (Table 1).

Part of the System management and Quality assurance are the data validation, consolidation and reporting. Database developer should be work closely with field geologist/ explores to identify the potential data error for validation. In other hand field geologist should have awareness the impact of the data error and how should prevented. In the first time may be the field crews have inconvenience with several validation systems.

In general the potential data error in Exploration database is consisting of: 1) Field data collecting and field sampling. The common errors are the mistype in the field, transcription error from field book by data entry and data duplication. 2) Analytical error which commonly from the sample analysis and sample preparation.

Data consolidation, aggregation should be part or use for the reporting system. Consolidation and aggregation should be used to create the effective querying tool for extract the exploration data summary report such as the total sample, total survey and significant assay intercept reported during the certain period. Tracking trend information, such as the sample dispatching should be interesting to monitor the progress changed. The example of the data consolidation and aggregation shows as Figure 4, which is present sample volume variation during one year for different type of samples.

Figure 4: Sample Volume Trend Chart Generate from the Database Aggregation tool, is an example from PT. Freeport Indonesia.

Tracking database also is the important task. It is to ensure that all data from field were recorded properly in the timely manner. The field data commonly consist of the geological mapping, surveying and sampling. Geological sampling is the most important to track, which is susceptible to mismatch between field sample collected in the field, recorded on the map, recorded in the database as well as analytical report from laboratory.

Data Visualization

Data visualization is important task in the resource calculation, modeling and evaluation in the exploration process business. Exploration data visualization is proposed to review and assess all the existing exploration data to evaluate the potential mineral resource occurrence on the exploration area. Data evaluation should be included the data distribution covered on the exploration area, with regard of the sample representative and focus in the interesting target. Data evaluation, especially of the geochemical sample analysis result should be using statistical and/or geo-statistical studies to determine the anomalies level of specific sample and/or geochemical elements.

In the early stage exploration commonly using the integrated two dimensional data visualization which is combining different type of data format (point, raster, image) from the different data type (geological, geophysical and geophysical) on the Geographic Information System (GIS).

The advance exploration stage, to measure the potential deposit, commonly drilling methods was used. Drilling method is take continues representative sample intersected on the mineral deposit. Drilling is the most expensive method to get the deposit information. Drill core data is the most important data sources to calculate and evaluate the economic deposit. Exploration drilling database is used for build the three dimensional evaluation of the deposit include but not limited for the resource grade and volume model as well as the descriptive model (Cox, 2009).

RECOMMENDATION

Exploration database is not only use during the exploration campaign but also will use during mining activity and potentially for the mine closure to reconcile the exploration resource modeling comparing to the actual resource mined as well as the potential environmental hazard during the mining closure.

Improving the understanding and awareness of exploration database by the exploration team member and management is important to improving the quality and government of the exploration database. The structured exploration database will easily to access and to develop to the advance system in the mature exploration stages.

CONCLUSION

1. Exploration database system is become more important to provide the information for exploration evaluation, resource-reserve modeling and calculation, as well as for mine operation support.
2. Understanding the exploration processes, data type and characteristic of each information and the data relation is important for design the database system and/or maintenance.
3. It is the real exploration world, before able to discover the new deposit should able to explore the existing database.

ACKNOWLEDGEMENTS

Thank you for PT Freeport Indonesia Management, especially the Geo Service Division which give an opportunity to research their exploration database concept and published to this paper.

REFERENCES

Anggayana, Komang, Mineral Deposits Evaluation Lecturing, Mine Engineering Department, Institute Technology Bandung, 2011

Dennis P. Cox, Paul B. Barton and Donald A. Singer, Mineral Deposit Model, US Geological Survey Bulletin 1693, 2009. https://pubs.usgs.gov/bul/b1693/Intro.pdf

Marjoribank, Roger, 2010, Geological Method in Mineral Exploration and Mining, Second Edition, Springer, Australia (e-book)

Saptawati, Putri, Data Mining; Concepts and Techniques, Institute Technology Bandung, 2011

Tilton, John E, Is Mineral Depletion a threat to sustainability, SEG Newsletter No 82 (2010)

Turner, Graham, A Comparison of the Limits to Growth with Thirty Years of Reality, CSIRO Sustainable Ecosystem, 2008

https://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm downloaded June 17th, 2012.

https://en.wikipedia.org/wiki/Data_mining downloaded June 17th, 2012.

Han, Jiawei and Kamber, Micheline; Data Mining: Concepts and Techniques, Second Edition, University of Illinois at Urbana-Champaign, Elsevier 2006

downloaded June 17th, 2012.

https://en.wikipedia.org/wiki/Business_intelligence, downloaded June 20th, 2012.