Processing Stream Sediment Geochemical Data for Geology and Dilution: An Example from Northern Vancouver Island

Transcription

1 Processing Stream Sediment Geochemical Data for Geology and Dilution: An Example from Northern Vancouver Island Dennis Arne, PGeo (BC), RPGeo (AIG), Principal Consultant Geochemistry, CSA Global Yao Cui, PGeo (BC), Senior Geomatics Geoscientist, BCGS Rob Mackie, PGeo (MB), Principal Consultant Geology, CSA Global Olivia Brown, Associate Database Consultant, CSA Global Geoscience BC Day, October 8,

2 Outline Statement of problem Background to catchment analysis Results from northern Vancouver Island Conclusions 2

3 Statement of Problem The presence of regionally extensive basalts of the Karmutsen Formation having elevated Cu makes it difficult to detect Cu anomalies associated with porphyry Cu and other Cu bearing mineral deposits on northern Vancouver Island. How can we filter out the effects of high regional background Cu and so add value to re analyzed regional geochemical survey (RGS) samples and new stream sediment data? 3

4 Importance of Lithology Raw Cu Data Mt Hall Gabbro? Geology is interpreted in terms of lithology, rather than map units. The distribution of Cu is clearly influenced by the Karmutsen basalt. 4

5 Principal Component Analysis Data Cloud in 2D From Peters, 1987 The most important principal components from regional stream sediment geochemical data usually reflect bedrock lithology PC1 from northern Vancouver Island defines mafic lithologies 5

6 PC1 vs Raw Cu Data Raw Cu Data Inverse PC1 PC1 provides a better discriminator for the Karmutsen basalt 6

7 Drainage Geochemistry Affected by: Catchment size Controls dilution Catchment geology Introduces variable geochemical background and differential erosion Variable relief Controls erosion rates in different parts of the catchment Scavenging of metals by various sample constituents Adsorption onto secondary Fe and/or Mn hydroxides, clays or organics Stream water ph & Eh Controls solubility of metals and secondary hydroxides Effects of exposed mineralization Introduces anomalous data populations Sample representativeness Historical RGS Au data is not very reproducible 7

8 Data Processing Approaches Raw data Residuals following regression against control elements Data levelled by dominant catchment lithology Data levelled by presence/absence of a particular unit Data levelled by catchment weighted background value Multiple regression analysis using catchment geology Residuals following regression against principal components Productivity analysis Weighted sums model Weighted sums model adjusted for catchment area size 8

9 North Vancouver Island Catchments Data compiled for 1835 samples in total Catchment basins delineated for 1725 samples 9

10 Sample Validation/Catchments Entire catchment area delineated for each sample (nested) Locations validated against original maps and adjusted to TRIM hydrology 10

11 Relationship Between Cu and Al Cu Residuals Cu shows a positive correlation with Al Regression against Al removes some of the lithological effects 11

12 Levelling by Dominant Lithology Raw Data Levelled Data Many elements show lithological control Statistical outliers are preserved during Z score levelling 12

13 Levelling Cu Data for Lithology Levelled by Dominant Lithology Levelled by presence/absence basalt Levelling by dominant lithology or presence/absence of basalt both reduce lithological effects 13

14 Weighted Background Values Z score Cu Residuals Average background values have been estimated for most lithologies These have been used to Z score level the Cu data 14

15 Regression Against PC1 Cu can be regressed against PC1 (lithological control) High positive residuals represent Cu above background 15

16 Weighted Sums Model Garrett & Grunsky, 2001 The user assigns importance rankings to selected variables based on a priori knowledge of the mineralogy/chemistry of a target deposit type Positive rankings used for those elements that are expected to be high (greater than median) in the target deposit type Negative rankings used for elements expected to be low (less than median) in the target deposit type For porphyry Cu example, using levelled data: Cu Mo Ag Au*Zn *raw data

17 Dilution Effects Sampling Effectiveness Regional Background Geochemistry in catchment areas >10 km 2 mainly reflects regional background Catchments > 10 km 2 have not been effectively sampled 17

18 Productivity Analysis Introduced to account for the downstream dilution of the geochemical signature from a mineral deposit exposed within a catchment. Me m A m = (Me a Me b )A a + Me b A m where Me a = the metal content at the mouth of catchment A a = the total area of the catchment Me m = the metal content of the eroding mineral deposit A m = the area of the target exposed at surface Me b= background metal content in the catchment (From Rose et al., 1979) WBV Cu Productivity However, for a small deposit size relative to the area of the catchment: Me m A m= (Me a Me b )A a This is also known as the Productivity Productivity analysis takes into account the effects of dilution 18

19 Method Validation Cu Occurrences No Cu Occurrences >90 th Percentile <90 th Percentile True Positives False Negatives False Positives True Negatives Single Element Methods Cu Productivity Weighted Sums Model WSM * Catchment Area Data processing methods were validated against known occurrences 19

20 Conclusions The effects of elevated background Cu are easily filtered using a variety of methods of varying sophistication. Processing methods using several commodity and pathfinder elements weighted for the effects of dilution work best. Existing Cu occurrences & deposits on NVI are more readily apparent and new targets evident in the processed data. An empirical assessment of sampling effectiveness indicates there are still exploration opportunities in under sampled areas. There is still much value to be obtained from the interpretation of RGS data, especially where archived material is re analyzed by ICP MS. Our approach to geochemical data should be more like our approach to geophysical data where filtering and data enhancements are routine. 20

21 Final Products 41 page report Compiled data file with geochemistry Shapefile of catchments attributed with derived values PDF copies of report figures and catchment maps Porphyry Cu WSM * Area Available from Example of thematic catchment basin map 21