3D quantitative mineral potential targeting in the Kristineberg mining area, Sweden

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1 3D quantitative mineral potential targeting in the Kristineberg mining area, Sweden Tobias Hermansson, PhD Structural Geology Specialist Field Exploration North Fennoscandia Boliden Mines

2 Outline Introduction to the project area Data compilation and conditioning Modelling Data interrogation and targeting Interpreting the Mineral Potential Index Dealing with clustered data, bias towards drill holes and circular reasoning Conclusions

3 The Kristineberg deposit, discovered in 1918 The Skellefte District The Boliden deposit, discovered in 1924 Palaeoproterozoic in age Mainly submarine bimodal volcanic rocks (Skellefte Group) and syn-volcanic intrusives overlain by a sedimentary sequence (Vargfors Group) Actively explored by Boliden and its precursor companies since 1915 More than 80 known deposits in the district (mainly VMS but also gold and porphyry copper deposits)

4 The Kristineberg area One active VHMS mine, the Kristineberg mine (29.1 Mt mined to date), three closed mines and several known, sub-economic deposits The ore zones are typically hosted in the transition zone between the Skellefte Group volcanic sequence and the overlying Vargfors Group Graphitic shales, and close to inverted syn-volcanic faults Skellefte Group volcanic sequence Skyttä et al. (2013) Vargfors Group sediments 10 km Graphitic shale Syn-volcanic, inverted faults

5 Integrating 100 years worth of exploration data During 2015 and 2016 Boliden has applied the Common Earth Modelling approach to the Kristineberg mining area in collaboration with Mira Geoscience Model area Skyttä et al. (2013) 10 km Roughly 11 x 8 x 2.5 Km

6 Data compilation and conditioning Summary of the different types of information available from the Kristineberg area Lithology/stratigraphy (drillhole and surface data) Alteration (drillhole and surface data) Mineralogy (drillhole and surface data) Structural data (surface and scarce drillhole data) Geological interpretations (maps, cross-sections and 3D-models) Grades (drillhole data) Whole rock lithogeochemistry (drillhole and surface data) Model area Digital elevation data Airborne magnetic data Ground magnetic data Ground gravity data EM34 (surface data) Borehole EM Susceptibility data (some drillhole data) IP data (surface data) Reflection seismics (4 lines) 10 km Roughly 11 x 8 x 2.5 Km

7 Geological map draped on DEM model Maps, cross-sections and digital elevation data Cross-sections at various scales

8 5680 drill holes with some type of digital information (assays, lithogeochemistry, geological logs) Data and information from drill holes Logged rock type More than 7000 drill holes! Logged alteration intercepts Logged mineral intercepts Assays Whole rock lithogeochemistry 4239 drill holes with digital geological logs Quality control and conditioning of data Very time consuming!

9 Simplification of rock type logs 4239 drill holes in total 156 reclassified by hand

10 (VOI app. 11 x 8 x 2.5 km) First pass Geological model - Vector format Fault network and key stratigraphic horizons in vector format

11 Key stratigraphic units in raster format (block model) First pass Geological model - Rasterized

12 Unconstrained regional scale geophysical modelling Regional scale 3D heterogeneity of density Regional scale 3D heterogeneity of susceptibility from airborne magnetic data

13 3D heterogeneous density model Constrained geophysical inversion - Gravity and Magnetics 3D heterogeneous magnetic susceptibility model

14 3D heterogeneous density model 3D heterogeneous magnetic susceptibility model

15 25 x 25 x 25 m cell size Exploration criteria models - Distance to alteration and 3D interpolation of alteration (Both drill hole and surface data) 25 x 25 x 25 m cell size Defined as distance in meters to values above a defined cut-off in the whole rock lithogeochemistry data Other examples of exploration criteria are distance to fault, distance to logged alteration intercept, distance to element concentration cut-off, etc. In total 70 criteria

16 The Common Earth Model All individual 3D models combined into one GOCAD project Objective Deposit model Exploration criteria Data 3D Models Geology Geochemistry Geophysics Structures Distances Target List X Y Z

17 Data interrogation and qualitative targeting Interpolated Tl concentration painted on the Graphitic Shale region to investigate the Tl distribution in the hanging wall relative to the known ore deposits Geology Objective Deposit model Exploration criteria Data Geochemistry Geophysics Structures Distances Target List X Y Z D Models

18 Quantitative mineral potential targeting Data driven approach (SVM) w. training cells (green) from interesting intersections in the one of the ore horizons.

19 Interpreting the Mineral Potential Index A colourful 3D model that display hot and cold volumes with respect to the calculated Mineral Potential Index will always be produced. However, if the data, input parameters and uncertainties are not well understood, the high mineral potential volumes may not at all represent actual mineral potential Remember that questionable data quality will create uncertainties in all related evidence layers Uneven spacing, or large gaps between stations, in i.e. the gravity data set may cause false density anomalies in the gravity inversion models Clustering of geochemistry data (i.e. high density of data in a few areas and large areas with scarce or no data) creates uncertainties in interpolations Clustered data will always bias the predictions towards the volumes with high data density Be aware of circular reasoning! The model predicts high mineral potential in the areas where real data exist (and lower away from these areas), but data exist in these areas because of a reason; they have already been considered interesting from a mineral potential point of view and sampled

20 How to deal with clustered data, bias towards drill holes and subsequent circular reasoning? Creative scoring Highest score for highest concentrations Highest score for concentrations in the interval ppm Lowest score for concentrations > 1000 ppm (considered to represent already discovered ore) This approach upgrades the contribution of samples in a concentration range that normally would be considered interesting from an exploration point of view and downgrades the contribution of concentrations that most probably represents already discovered ore

21 How to deal with clustered data, bias towards drill holes and subsequent circular reasoning? Other things to be considered: Evidence layer with distance to drill hole and reversed scoring (higher score with increased distance) This may potentially be effective if there is high quality geophysical data with good spatial coverage, since it would upgrade geophysical anomalies away from already explored areas. In cases where most of the MPI relies on drill hole data, it is not very effective. High quality geophysical data sets accompanied by spatially well-distributed petrophysical information If the geophysical data is well understood it may, potentially, be used to map out e.g. important stratigraphic units, alteration envelopes or directly detect ore. Since the sampling of geophysical data most often is done in grids or lines, the effect on the MPI of clustering in other data sets is reduced Spatially well-distributed geochemistry sampling More spatially well-distributed sampling of whole rock multi-element geochemistry is probably the only way to really move away from the bias towards data clusters. It is also very important in mature mining camps where the normal metal background concentrations are in the order of what would be exploration targets elsewhere. In these areas, it should be considered whether metal concentrations should be included at all in the quantitative targeting approach. Perhaps focus instead should lie on finding vectors among the trace elements?

22 Conclusions Running this integrated interpretation project has forced the project team to really get to know our data in-depth and, furthermore, made us painfully aware of a range of weaknesses in the company s data management system Furthermore, the Common Earth Modelling approach have helped to identify the need for data types not previously collected and, thus, guide future sampling campaigns The Common Earth Model itself is a very powerful tool, and is used for visual data interrogation and both qualitative and quantitative target generation and analyses Unless circular reasoning can be avoided, the quantitative approach will only return targets that are already known and will not add much value to the exploration efforts The quantitative targeting approach should be used with care (a note to managers): The importance of knowing the data properly, understanding the workflow, the uncertainties, what data has gone into the targeting process and how it has been manipulated, weighted and scored cannot be stressed enough! It is very difficult to draw correct conclusions from a 3D mineral potential model without having this understanding

23 Thanks to The project group at Boliden Mac Fjellerad Persson Erik Nordfeldt Mohsen Banaei Fard Johan Magnusson Katri Vaittinen Hannu Kaaretkoski Janne Kaukolinna Peter Wallström Eva Lundquist The team at Mira Geoscience Jeff Witter Jennifer Levett Chrissy Williston Thomas Campagne Gervais Perron Dianne Mitchinson Pablo Mejia

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