Application and Challenges of Artificial Intelligence in Exploration

Transcription

1 Application and Challenges of Artificial Intelligence in Exploration XPLOR 2017 John McGaughey 2017 Mira Geoscience Ltd.

2 Artificial Intelligence Artificial Intelligence is colossally hyped these days, but the dirty little secret is that is still has a long, long way to go. New York Times, July 29, 2017 Artificial Intelligence, or machine learning, is a tool for discerning complex statistical correlations in large data sets.

3 The modern exploration context under weathering or cover, at depth, complex brownfields settings ore system footprint recognition rather than anomaly recognition multi-disciplinary interpretation in model space rather than data space courtesy Geological Survey of Queensland

4 The modern exploration context targeting in model space, not data space courtesy Geological Survey of Queensland

5 The modern exploration context Common Earth Model: a single, shared, consistent earth model a working hypothesis that can be queried, tested, modified ore system footprint recognition in multi-disciplinary data Glencore, Ribago VMS district

6 Mineral Potential Mapping exhalite proximity fault proximity lithology potassium enrichment sodium depletion mineral potential index

7 Targeting Workflow planning data management modelling targeting

8 Targeting target (x, y, z) = f (rock type, alteration, structure, chemistry, mineralogy, ) Glencore, Ribago VMS district

9 Data Table

10 Machine Learning Weights-of-Evidence sub-divides space of m variables into 2 m regions using binary thresholds chosen by user each region weighted by relative proportion of training data

11 Machine Learning Decision Trees recursive binary splitting of the variable space Random Forests are averages of multiple Decision Trees run on an ensemble of random training data subsets

12 Machine Learning HyperCube brute force search through the variable space returns multiple rules

13 Northing ( m ) 7,580,000 7,620,000 7,660,000 7,700,000 Targeting Case Study Mount Dore, Queensland, Australia courtesy Queensland Geological Survey 410, , ,000 SRTM ( m ) Easting (m) Landsat Image

14 Targeting Case Study Gravity Analytical Signal Gravity 1VD Apparent Density Geophysics Magnetic Analytical Signal Magnetic 1VD Apparent Susceptibility courtesy Queensland Geological Survey

15 Targeting Case Study Modelling courtesy Queensland Geological Survey

16 Targeting Case Study Distance to Au g/t Distance to Cu g/t Potassium (ppm) Geochemistry and Radiometrics Uranium (ppm) Thorium (ppm) U / Th courtesy Queensland Geological Survey

17 Targeting Case Study Distance to Fault Crossing Distance to Fault Network Distance to High Curvature Geology and Structure Pseudo Lithology Surface Geology Distance to conductor courtesy Queensland Geological Survey

18 Setting Things Up Training data 11 Grid-based data converted to a data matrix 18 variables 750,000 entries (rows) 33 known mineralized regions (training sites) Observations , 16, 17 8,

19 Challenges variable resolution unknown accuracy on location interpreted data derived variables from user interpretation Continuous (Low Resolution) Apparent Density ( 1 km ) Derived Variable Distance to Cu Anomaly Discrete Variable Surface Geology Continuous (High Resolution) Radiometric data ( 100 m )

20 Weights of Evidence areas of high probability trend along known mineralization

21 7,580,000 7,620,000 7,660,000 7,700 Northing ( m ) HyperCube multivariate HC rules are interpretable in geological terms better than WofE in assigning high scores to known occurrences , 16, 17 8, Post-probability

22 HyperCube Rule Selection Rule ID# Property Lower Bounds Upper Bounds Site # Covered Pseudo Lithology Dist_2_CDI Distance_to_CuGt GRAV_effective_density TMI_Analytic_Signal Distance_to_CuGt Distance_to_high_curvature_along _faults , 16, Uppm Distance_to_CuGt GRAV_effective_density U/Th GRAV_effective_density Gravity_Analytic_Signal distance_to_fault_network , 32 10

23 % Training Cells % Training Cells Efficiency of Classification" WofE HC Post-probability Percentile Post-probability Percentile

24 7,580,000 7,620,000 7,660,000 7,700, Northing ( m ) 7,580,000 7,620,000 7,660,000 7,700 Northing ( m ) Weights of Evidence Hypercube Hypercube , 16, 17 12, 16, , , 13 Post-probability Post-probability

25 Conclusion Artificial Intelligence (or machine learning) is advanced pattern recognition, not cognition or understanding Decisions on inputs are subjective; actual inputs are largely interpretive Application to poorly modelled data or a small number of input variables yields little benefit over traditional methods (such as map interpretation) Application of new methods of artificial intelligence to well-modelled data sets with large numbers of variables can yield significant insight, identifying associations between input variables and targets