GIS modeling using fuzzy logic approach in mineral prospecting

GIS modeling using fuzzy logic approach in mineral prospecting based on geophysical data Harman Setyadi, Lilik Eko Widodo, Sudarto Notosiswoyo, Putri Saptawati, Arief Ismanto, and Iip Hardjana Citation: AIP Conference Proceedings 1711, 070002 (2016); doi: 10.1063/1.4941643 View online: http://dx.doi.org/10.1063/1.4941643 View Table of Contents: http://scitation.aip.org/content/aip/proceeding/aipcp/1711?ver=pdfcov Published by the AIP Publishing Articles you may be interested in Search and selection hotel system in Surabaya based on geographic information system (GIS) with fuzzy logic AIP Conf. Proc. 1718, 110003 (2016); 10.1063/1.4943350 Stock and option portfolio using fuzzy logic approach AIP Conf. Proc. 1589, 504 (2014); 10.1063/1.4868854 GOLD predictivity mapping in French Guiana using an expert‐guided data‐driven approach based on a regional‐ scale GIS AIP Conf. Proc. 1009, 52 (2008); 10.1063/1.2937300 Using GIS and Fuzzy Logic for Wastewater Treatment Processes Site Selection: The Case of Rodopi Prefecture AIP Conf. Proc. 963, 851 (2007); 10.1063/1.2836227 Using FUZZY logic to clean unattended blast noise monitor data J. Acoust. Soc. Am. 101, 3057 (1997); 10.1121/1.418657

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GIS Modeling Using Fuzzy Logic Approach in Mineral Prospecting Based on Geophysical Data Harman Setyadi1,a), Lilik Eko Widodo2, Sudarto Notosiswoyo2, Putri Saptawati3, Arief Ismanto4, Iip Hardjana4 1

Doctorate Student, Mine Engineering Study Program – Faculty of Mine and Petroleum Engineering, Institute Technology Bandung, Jl. Ganesa 10, Bandung 40132 - INDONESIA 2 Earth Resources Exploration Group - Faculty of Mine and Petroleum Engineering, Institute Technology Bandung, Jl. Ganesa 10, Bandung 40132 – INDONESIA 3 Informatics– STIE, Institute Technology Bandung, Jl. Ganesa 10, Bandung 40132 - INDONESIA 4 J. Resources Nusantara, Equity Tower, 48th floor, SCBD. Jl. Jendral Sudirman Kav 52-53 Jakarta 12190. a)

Corresponding author: [email protected]

Abstract. The geophysical exploration method is the superior over the project area due to the dense of vegetation and thick soil so very limited geological outcrops. Contrast of physical properties of every different rock type should be able to be distinguished by the geophysical data. Fuzzy logic approach and weight of evidence were used for geophysical data modeling. Posterior probability was used to calculate the weight of evidence (WofE) of every fuzzy map memberships. By combining each rock type model, the model provides better result compared from the model from mixed rock type on the data training. This method is able to eliminate the potential interference of different geophysical signature. So that, the understanding the geological feature of the area is key success for the mineral prosperity modeling. We verified the model by site visiting and drilling and it is estimated about 90% confident.

INTRODUCTION Seruyung is a gold mine located within the ENE to NE trending in a small hill of volcanic dominated host rock surrounded by mangrove swamp. The andesitic volcanic is controlled by the Sembakung lineament and intruded the mid-Eocene to Miocene Sedimentary rock of Tidung Basin [1]. The Geology of Seruyung deposit comprised of porphyritic andesite unconformable covered by pyroclastic – tuff. Seruyung High Sulphidation Epithermal (HSE) gold system is characterized by vuggy silica altered rocks cantered on structurally controlled sulphide-rich hydrothermal breccia lenses and surrounded by alunite – argillic and advance argillic alteration [1, 2]. In order of importance, Seruyung mineralization is north structural controlled hydrothermal breccia gold rich, lithology of hydrothermal breccia associate with vuggy silica and elevation [2]. Exploration campaign was carried out to map the potential gold mineralization over the area comprised north – south grid line with 100 meter spacing. Auger soil sampling was collected by 50 meter sample interval, ground magnetic survey was recorded every 5 meter interval and IP-Resistivity taken every 50 meter dipole-dipole survey. A total 51.5 km drilling from a total 443 holes was conducted for resource definition from this area. At the end of the drilling campaign in 2014, it was reported to have resources of 14.3 million tons ore with averaging 0.92 g/t Au with total gold content is 422 Koz. An additional drilling plan should be proposed in order to increase the resources by ore deposit delineation and/or discovery. The discovered ore body by previous drilling program is commonly over the near surface and/or with clearly-prominent surface anomalies ore body. The challenging additional drilling program is to extent on the marginal area, which possibly lies in the sub- surface with less clearly weak – moderate discrete surface anomaly.

International Symposium on Frontier of Applied Physics (ISFAP) 2015 AIP Conf. Proc. 1711, 070002-1–070002-6; doi: 10.1063/1.4941643 © 2016 AIP Publishing LLC 978-0-7354-1358-0/$30.00

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This paper is part of my doctorate study for the mineral prospective modeling. The objective of the geophysical data modeling is to predict the potential extent of the existing resource by using the ground physical data. The final product should be used to assist of the exploration – delineation drilling program during 2015.

GEOPHYSICAL DATA Three type of geophysical data set was used for the mineral prosperity modeling over the Seruyung project area. Seruyung geophysical data processing and anomaly feature are summarized as follows. Ground magnetic survey recorded the total magnetic intensity (TMI) over the area which picked up the different of magnetic responses of the different geological unit (lithology/ alteration). Magnetic anomalies from magnetic bodies presented as dipole anomaly which is very different at different magnetic latitude [3]. As Seruyung mine is located on the low latitude, data was processed to create Reduction to the Pole (RTP). A prominent low RTP anomaly occurs in the center part related to silica ore body. Low RTP anomaly is correlated with the strongly silica – phyilic alteration and interpreted as the destruction magnetic zone. The prominent high RTP anomaly in the NE corner is related to the propylitic andesite (Fig. 1). The further detailed interpretation is difficult and may be misleading due the location on the low latitude [4, 5, 6]. To reproduce the vertical magnetization or to remove the inclination effect as well as to free out of the remanent magnetic analytical signal (ANS), magnetic anomaly should be processed [7, 8]. Analytical signal magnetic anomaly of Seruyung mainly has similarity with RTP anomaly. Several small discrete ANS anomaly occurs on the low RTP anomaly and interpreted as the remnant magnetic body. The multiple magnetic bodies over Seruyung mine suggested that Seruyung ore bodies was deposited by several phase of intrusion and mineralization. Resistivity commonly controlled by physical rock properties such as porosity and salinity and mineralogy composition. Clay mineral, graphite and sulphide base metal (except sphalerite) are a good conductor and can reduce rock resistivity [3]. In general, Hoschke in [9] summarized the characteristic of geophysical signature of High Sulphidation Epithermal (HSE) Au deposits are high resistivity over silica body, conductive clay alteration, magnetic destruction alteration and disseminated sulphide which relate to the high IP-chargeability. Seruyung resistivity anomaly consist of prominent east-west high resistivity over the main ridge related to the main ore body. Small discreet week-moderate anomaly observed around the main anomaly. Obscured N-S lineament observed interpreted as possible structural control (Fig. 1). The effect of electric current flow in the rock mass can become electrical polarization. Strong polarization will occur at the conductor minerals such as graphite and metallic sulphide and very low in sand stone and quite in clay. IP respond will depend on the surface area of the conductive mineral grain rather than connectivity. Pyrite – chalcopyrite which most often occurs as the disseminated sulphide on the rock will result a good sensitive IP anomaly and no to very low resistive [3]. IP method has limitation due to the electromagnetic coupling contamination over the geologic layering and limitation. Although the main silica ore body also contained pyrite, Seruyung high IP anomaly is not represent the ore body itself. The main prominent IP anomaly occurs on the edge of south western part of ore body and trending to North West.

DATA MODELING The mineral predictive modeling used the combination of Fuzzy logic and Weight of Evidence (WofE) approach. Basically Fuzzy logic is the knowledge-based data integration while WofE is the data-driven based. Fuzzy is the common method used by the researcher for mineral prosperity modeling. Fuzzy was wide to apply and develop to simplify for mineral prosperity modeling [10]. In this study, the geophysical data was processed using the geophysical software to produce Reduce to the Pole (RTP) and analytical signal data by grid size is 10 x 10 m. IP and resistivity data also was processed by same grid size and spatial location. All data grid include the training data as preprocessing data are put on the SQL database. The SQL script then designed to enable data retrieval in MS Excel in which the data is automatically ready for modeling process. The general data flow shows as Fig. 1a, figured the relationship between the pre-processing data storage and MS Excel as modeling machine. Final model should be further integrated using other data using standard GIS software.


(a)

(b)

FIGURE 1. Seruyung RTP (a) magnetic anomaly map and (b) resistivity anomaly map.

In general, fuzzy modeling procedure is divided by three modules which are fuzzifier (encoder), inference system (machine) and defuzzier (decoder). In the common study, fuzzy membership was assigned by knowledge driven of expert. The general statistical approach was taken due to the geophysical dataset has ambiguous meaning. In this study, data encoding which is convert the continue data to the crisp fuzzy membership was used the statistical approach. Data was divided in the ten class from 0.1 to 1.0 with 10 percentiles intervals. The result was tabulated as the fuzzy encoding rule. Figure 1B is the example of encoding rule table for assigning the RTP fuzzy membership. For instance, the high magnetic RTP anomaly should be related to the importance of the ore magnetic body such as magnetite-skarn, potassic K-feldspar – magnetite alteration on the Au-Cu mineralization. On the other hand low magnetic RTP anomaly should be related to the importance of the destruction magnetite – phyllic argillic alteration to delineate the epithermal deposit. That mean high anomaly on the RTP has more importance or less importance relative to the target should be defined. Conditional probability is measurement of the favorable probability ore deposit to the specific area as N{D}. Data training N{T} is the critical layer to build the predictive map especially for the data-driven method [11, 12, 13]. Without data, training mineral potential mapping is constrained to the expert-system approach [11]. In this study, data training was created from the known selected area of deposit which represent the specific deposit type (domain) from drilling data set and surface geological mapping. In the first pass modeling, it was created from three different data training. Based on the model evaluation and verification, this area should have seven different types of rock (domain). New data training set were created and used for remodeling the data. The final model was improved by using new data training, which is better to represent the geological domain. It means that the understanding of geological feature of this area is very important and part of key success to generate prosperity model map. Posterior Probability is the weight of evidence (WofE) method, which is based on the common technique to combine data set to estimate the relative importance of evidence. WofE was used to indicate the degree of correlation between known mineral deposits (data training) to the related factors of each fuzzy membership [11, 13, 14, 15]. The Bayesian rule is expressed as: ܲ ሼ ‫ ܤ‬ȁ‫ ܦ‬ሽ ൌ

௉ሼ஻‫ת‬஽ሽ

(1)

௉ሼ஽ሽ

where P{B∩D} is the number intersected of fuzzy member population as binary map {B} with the ore deposit {D}. The higher number means a better predictor. Figure 2b is a table that combined the fuzzy encoder and fuzzy decoder as the accepted posterior probability value ranging from 0 to 0.9. The highest value is the best evidence. Only P{B|D} value above 80% percentile (anomaly) is recommended to be used for data modeling.


(a) Fuzzy 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

Reduce to the Pole N{B∩D} Min Max 40493.1 40988.0 0 40412.0 40493.1 0 40372.1 40412.0 10 40345.0 40372.1 24 40319.6 40345.0 41 40296.1 40319.6 41 40277.7 40296.1 0 40246.9 40277.7 0 40194.2 40246.9 0 39994.1 40194.2 0

(c) P{B|D} P_RTP 0.086 0.207 0.353 0.353 -

COMBINATION (SUM)

Map Weight PO_RTP 0.5 PO_AS 1 PO_IP 1 PO_RES 1

(b)

(d)

FIGURE 2. GIS geophysical predictive modeling frame work, calculation and data model. (a) Geophysical data modeling flow chart, (b) Fuzzy membership assignment table, (c) An example of fuzzy data model, and (d) Model combination and weighting tool.

The combination of anomaly called as the model. Figure 2c is an example of Main Silica model, which represent the combination of geophysical responses. Based on the model, Main Silica was interpreted as rock with high resistivity that reflected the intense silica alteration. Moderate to high IP is the response of the pyrite content and low RTP and ANS magnetic anomaly represent the destruction of magnetic zone. The model characterization was matched with the field geological observation. Model validation was the user intervention to adjust the anomaly level to create map predictor closer to the expectation. The final selection of P{B|D} value (Fig. 2c) was used to create the posterior probability (PO) maps (PO_RTP, PO_ANS, PO_IP and PO_RES). Model was created by combining the PO maps, which was done by implementing map algebra of SUM (Fig. 2d). Model data validation was taken by comparing the model to the foot print of ore deposit outline. Model optimization was taken by adjusting the lower cut of model values as well as the lower cut of the posterior probability – weighing values. The model was considered correct if it matches well with the known deposit. The extent of the model must lead to the geological reasonable drilling target. On the other hand the low prospectivit y area should be safely dumped without loss of opportunity to find the deposit.


(a)

(b)

FIGURE 3. (a) Model was calculated based on the SUM function of all the binary posterior probability map resulted 89.5% matching and 73.5% expected success ratio. (b) Final Model Plot and integrated to the other data set to create the drilling Plan. The higher (strong red) prospective area was completed drilled.

The model comparison between the direct SUM function for all the probability and the SUM function from the intersected anomaly is very small. However, the direct SUM is simpler and better. Figure 3b show the prediction of ore body delineation. The red color in the center is the existing ore body. The potential extension occurs surrounding the main ore but it has lower value and discrete pattern. It means the potential of ore body extension is on the marginal area with expected lower tonnage and grade comparing to the main ore. Drilling in 2015 (up to August 2015) proved that the map prediction matches the drilling result. By using this mineral prospecting map, geologist may be able to create better drilling plan to test all the potential area by budget constraints.

CONCLUSION x x x x x

The comprehensive modeling for predicting the mineral ore bodies using multiple layers of geophysical anomaly is better and can reduce the ambiguity of the single layer geophysical anomaly. On the mineral ore body district, it is possible to differentiate geophysical signature due the differences of the physical properties during ore deposition (metal / alteration zonation). Understanding the geological feature of the area is important to create data training to represent each mineral domain. The assignment of mineral domain will impact on the model prediction accuracy. The best geophysical mineral prosperity model should be generated by combining the model from each domain rather than created from the mixed domain. Spatial geophysical mineral prosperity modeling is work very well in Excel, using Excel is more flexible in the data calculation and easier to generate the model with interactive module with the user.

ACKNOWLEDGMENTS I would like to extend my gratitude to J Resources Nusantara and PT Sago Prima Pratama managements which have given me the opportunities to review and evaluate as well as to publish their data for this study case. Appreciations are also to the anonymous reviewers for their constructive comments.


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