Twenty Second XXII Annual Meeting Tuesday Morning Session

Twenty Second (XXII) Annual Meeting Tuesday Morning Session 2

Fábio Paiva L. Pereira 2

The Piece-wise Linear model of Regionalization Unconditional Realizations Piece-wise linear transformation of the realizations Parametrize the PLMR model Mix the transformed realizations Generate the PLMR variograms Condition the realizations of the model 3

The Piece-wise Linear model of Regionalization 4

Sampling a multivariate Gaussian distribution 5

Steve Mancell 6

Boundary Modeling using Indicator Interpolation Thresholding NN-Model Indicator Estimate z = Fi* (1 -Vnn/Vtotal) i*>z, inside U = z +/- 0. 15 7

Single Domain Porphyry Case Study • • • Comprised of 3, 276 DH data RBF for indicator interpolation NN model volume of 55, 898 blocks: • • • Indicator NN Threshold Base Case with DH Data Indicator Threshold with Uncertainty Th. NN = 1 -(VNN/Vtotal) Th. NN =0. 88; Fi*(Th. NN) = z. NN = 0. 52 Uncertainty thresholding for +/-0. 15 of NN model threshold: • • zeroded= 0. 52 + 0. 15 zdilated = 0. 52 – 0. 15 • Compared to SDF modeling: Indicator Threshold with Uncertainty SDF with Uncertainty 8

Vein Case Study • • Single vein with data from 14 drill holes Interpolated indicators with anisotropy modeled within Leapfrog software • NN model volume threshold (yellow) • Uncertainty thresholding for vein with +/0. 10 of NN model threshold: • zeroded= 0. 281 + 0. 10 (cyan) • zdilated = 0. 281 – 0. 10 (red) • Compared to SDF surface used in Leapfrog (no uncertainty) Drillhole Data Indicator Threshold with Uncertainty 9

Haoze Zhang 10

Haoze Zhang MSc, strong mathematical background and coding skills Like learning new technologies Determination to accomplish challenging tasks Prior to CCG: Two years Geophysics research experience at the University of Calgary APEGA Meeting Coordinator 11

Haoze Zhang Research focusing on data analysis Missing data map Tools for exploring missingness nature Tools for measuring the spikiness of data Tools for understanding below detection dependence Cluster analysis workflow for geostatistical data Multiple transformations for cluster analysis Tools for visually examining high-dimensional clusters 12

Discussion Time 13