GIS Modelling for Mineral Prosperity,

Can Data Speak Them Selves?

by

HARMAN SETYADI

Mine Engineering Program, Faculty of Mine and Petroleum Engineering

Institute Technology Bandung

November 2014

Background

Mineral Exploration is a high risk and uncertain business and prone to fluctuation of the metal price.

exploration decision commonly is taken only based on the qualitative interpretation based on partial information rather than quantitative analysis.

How to reduce the exploration risk and to imrpove the disovery rate?.

Mineral exploration model with quaintitative analysis and GIS technology was designed to improve the exploration risk and accelerate the discovery rate.

Exploration properity model commonly is designed on the Reconnaissance/ to find stage.

Another exploration stage which is high cost and uncertainty is on the drilling project.

How is the GIS modelling for mineral properity work on this stage?

Prosperity Modelling Approach

Data Driven :

(Pros) Quantitative, Objective, consistent, based on the designed formula.

Fuzzy Logic : score 0 – 1 in every anomaly class,

Binary – tertiary : true or false,

Weight of Evidence (WofE)

Data mining, mathematical approach

(Cons): Not all of anomaly is Lanier, in the one of prospect area should be occurs some different ore deposit.

In general use to indicate the ore deposit which have similar type and its anomaly.

Knowledge Driven:

Subjective, based on the expert judgment, not consistent. Based on the expert experiences and knowledge.

Need more time

Commonly use over the area with limited ore deposit discovered.

Exploration Stages

GIS modelling for

Mineral Properity

commonly uses on

the Recconaissance

& Prospecting

Could the Model

uses on this stages?

Need quicker

decision,

More costly,

Sub surface Target -

uncertainty

Contoh Model Prospeksi Endpan Mineral

Peneletian dengan menggunakan data regional, untuk

menemukan potensi endapan berdasarkan tambang yang

sudah diketemukan.

Domain geologi diabaikan, keberadaan lokasi tambang tidak

seslalu pada kelas tertinggi

Model Prospek Au-Cu porfiri di Iran dengan metoda

Interval Valued Fuzzy, Rad, dkk (2011)

Model Prospek Au di Gold Field, Australia dengan

metoda Stastitical Data Mining, Barnett, dkk (2006)

Case Study: Seruyung & Bakan

J. Resources is the Junior Mine company, which is very agresive the

ore reserve discovery is almost double from 2012 to 2014.

Assumption

Gold deposit concentration is very low (<10 gr/ton),

not able to indicate directly. No contrast physical

properties

Gold Ore deposit has correlation with the physical

properties (magnetic, resistivity, induction) which is

controlled by the mineralization type.

Gold ore deposit is controlled by the geological

processes and generate the distinctive anomaly

signatures.

Bakan Mine – North SulawesiReserve Bakan (April 2014)

42,2 MT @ 0.79 ppm Au, 1,060 Koz Au & 6,770 Koz Ag

High Sulphidation Au deposit, multiple site. Oxide ore

Seruyung Mine – North Kalimantan

No directly corelate with specific Lithology Mapped

Ore Body is related to the low RTP anomaly but not all RTP is ore body

Ore Body is related to the high Resistivity anomaly but not as Linier

corelation

Ore Body is related to the IP – Chargebility anomaly but not as Linier

corelation

Ore Body is related to the high Soil anomaly but not all anomaly is ore

body and part of ore body is not represented by Soil Au Anomaly

Soil Anomaly Statistics

Soil Anomaly – What Element Indicators?

Geology Domain

• Memerlukan Pemahan geology yang matang (knowledge driven).

• Kombinasi feature geologi, geokimia, geofisika,

• Menggambarkan Genesa dan kedudukan endapan mineral

Exploration & Mine Data Hierarchy

Data Visualization

2D, 3D GIS Analysis and Modeling

System Management & QA

Validation, Consolidation, Integration, Data Warehouse –Data knowledge, Processing -

Gridding

Data Source Administration

Field Data Gathering: type of data required, data density, data orientation

Decision

Making

CONCLUSION

On the detailed project for drilling, Data cannot

uses directly for GIS modelling. Realized only on

the data model will miss leading.

Understanding the spatial anomaly signature and

its correlation to the geological model is very

important.

Geological Domain is required to optimize the

Mineral Prosperity GIS model.

However the data driven also important to

understanding of the relationalship of anomaly,

especially for the geochemical multielement

data.

Terima kasih

Thank You

Syukron