prospectivity mapping in gis: integrate geochemistry · pdf fileprospectivity mapping in gis:...

W O R K S H O P

Prospectivity mapping in GIS: integrate geochemistry data with

geophysics and geology

Prospectivity mapping in GIS: integrate geochemistry data with geophysics

and geology

Workshop, 21 August 2011

25th International Applied Geochemistry Symposium 201122-26 August 2011 Rovaniemi, Finland

Vesa Nykänen

Publisher: Vuorimiesyhdistys - Finnish Association of Mining and Metallurgical

Engineers, Serie B, Nro B92-5, Rovaniemi 2011

Nykänen, V. 2011. Prospectivity mapping in GIS: integrate geochemistry data with geophysics and geology. Workshop in the 25th International Applied Geochemistry Symposium 2011 22-26 August 2011 Rovani-emi, Finland. Vuorimiesyhdistys, B92-5, 88 pages.

Layout:Cover – Irma Varrio

ISBN 978-952-9618-72-9 (Printed) ISBN 978-952-9618-73-6 (Pdf)ISSN 0783-1331

© Vuorimiesyhdistys

This volume is available from:Vuorimiesyhdistys ry.Kaskilaaksontie 3 D 10802360 ESPOO

Electronic version:http://www.iags2011.fi or http://www.vuorimiesyhdistys.fi/julkaisut.php

Printed in:Painatuskeskus Finland Oy, Rovaniemi

Prospectivity mapping in GIS: integrate geochemistry data with geophysics and geology

Vesa NykänenGeological Survey of FinlandP.O. Box 77, FIN-96101 Rovaniemi, FinlandEmail: [email protected]

Abstract

Prospectivity mapping is used to define areas favourable for mineral exploration. It can be applied in various scales from global to local scale exploration targeting. Geographical information systems (GIS) provide a flexible and powerful platform to apply spatial data analysis techniques as a tool for prospectivity mapping by in-tegrating geochemical, geophyscical and geological data. These techniques include e.g. weights of evidence, logistic regression, fuzzy logic and neural networks. Definition of the exploration model can be based on a genetic ore deposit model or a mineral system model. This gives the framework for the data used for creating a predictive prospectivity analysis for mineral exploration targeting. Es-sential part of the procedure is the pre-processing of the raw data into meaningful map patterns for the given task. These pre-processing techniques include data in-terpolation, classification, clustering, rescaling, filtering, image processing, raster calculation etc. A common expression for describing these methods in GIS platform is geoprocessing. After creating the map patterns indicating the vectors towards a mineral deposit type we can apply various data integration techniques in GIS to create a single prospectivity map delineating positive areas for mineral exploration. Application of these techniques using ArcGIS software and add-on applications will be discussed and demonstrated during this one day workshop. The examples pre-sented in this workshop give insights into the use of the techniques for exploration of orogenic gold, IOCG and magmatic Ni-Cu deposits.

6

Workshop Program Sunday, 21 August 2011, Hotel Santa Claus, Rovaniemi

8.30-9.00 Registration

9.00-10.15 Introduction and case histories • Aim of the workshop • Orogenic Au prospectivity • IOCG prospectivity • Ni prospectivity

10.15-10.30 Coffee/tea

10.30-12 Lab 1-3: Geoprosessing tools: ArcToolbox ja SDM Toolbox • Introduction to GIS • Demo 1 • Spatial analysis (buffers, proximity, density, interpolation…) • Demo 2 • Demo 3 12-13 Lunch

13-14 Lab 4-6: Weights of evidence • Training sites, calculating weights, creating a prospectivity map • Demo 4 • Demo 5 • Demo 6

14-15 Lab 7: Fuzzy logic • Fuzzy set theory • Fuzzification functions • Fuzzy membership • Demo 7

15-15.15 Coffee/tea

15.15-16 Lab 8: Neural networks • The Principles of Neural Networks in Prospectivity Mapping • Demo 8

1

Prospectivity mapping in GIS:

integrate geochemistry data with geophysics and geology

Presentation includes slides modified from

Warick Brown Graham Bonham-Carter

Gary RainesStephen Gardoll

David Groves

Juhani Ojala

Vesa NykänenVesa Nykänen

Outline9-10.15 Introduction and case histories

• Aim of the workshop• Orogenic Au prospectivity

• IOCG prospectivity

• Ni prospectivity

10.15-10.30 Coffee/tea

10.30-12 Lab 1-3: Geoprosessing tools: ArcToolbox ja SDM Toolbox

• Introduction to GIS• Demo 1

• Spatial analysis (buffers, proximity, density, interpolation…)

• Demo 2• Demo 3

12-13 Lunch

Outline

13-14 Lab 4-6: Weights of evidence• Training sites, calculating weights, creating a prospectivity map

• Demo 4

• Demo 5• Demo 6

14-15 Lab 7: Fuzzy logic• Fuzzy set theory

• Fuzzification functions

• Fuzzy membership• Demo 7

15-15.15 Coffee/tea

15.15-16 Lab 8: Neural networks

• The Principles of Neural Networks in Prospectivity Mapping

• Demo 8

2

Acknowledgements:

• Gary Raines (USGS, University of Nevada, Reno)

• Graeme Bonham-Carter (GSC)

• David Groves (University of Western Australia)

• Stephen Gardoll (Redstone Resources)

• Juhani Ojala (Store Norske)

• Pasi Eilu (GTK)

• Etc.

Course Aim

Extract Key Features

3

Create Derived Layer

Define Association to Deposits


4

Define Association to Deposits for Several Layers

Combine all Association into Prospectivity Map

Spatial modelling

• Spatial model is a generalization of real world

• Geologic map is a model

• Spatial model:

– Classifies geographic areas based on certain criteria (attributes)

– Makes predictions of phenomena

– Can be used in decicion making process to help undestanding real world systems

5

Data mining

• Automated prosesses finding patterns in data (‘feature space’)

• Aiming to find unexpected correlations from various data

• Spatial data mining is applied on spatially referenced data

•y

•x1

•x3

•x2

Mineral potential or prospectivity

mapping• Traditionally based on expert opinions on potential areas for a

certain deposit type

• Digital maps allow quantitative analysis of data and numerical

modelling for prospectivity mapping (->exploration targeting)

Main types existing mineral prospectivity methods

• Data driven (empirical)

example: weights of evidence, neural networks

• Knowledge driven (conceptual)

example: fuzzy systems, index overlay

6

Spatial data analysis as a tool for

mineral prospectivity mapping

Vesa M. Nykänen

Examples from gold exploration in the Northern Fennoscandian Shield, Finland

Geological Survey of Finland, Bedrock and Mineral Resources,

Rovaniemi, Finland

ground acquisition target selectionProspectivity

Maps

INTEGRATION OF CRITICAL PARAMETERS INTEGRATION OF CRITICAL PARAMETERS INTO PROSPECTIVITY MAPINTO PROSPECTIVITY MAP

Regional Mineral Exploration Data

GIS

??Analyse

Combine

Remote SensingGeophysicsGeochemistryGeology

GIS raster layers target output

1

Input feature vector

[3, 8, 33, 800]

FUZZY LOGICNEURAL NETWORKS

((

(

((

(

((((

(

((

(

((

((

(

(

((

(

(

(

(((((((((

(((

(

(

(

(

(

(

((

(

((((

(

(

(

(

((

((

(

(

( (

(

(

(

(((((((((

(((

(

(

(

(

(

(

((

(

((((

(

(

(

(

((

((

(

(

( (

(

(

(

(((((((((

(((

(

(

(

(

(

(

((

(

((((

(

(

(

(

((

(

((

(

( (

(

(

(

(((((((((

(((

(

(

(

Till Fe

(

(

(

((

(

((((

(

(

(

(

((

((

(

(

((

(

(

(

(((((((((

(((

(

(

(

Till As

((

(

((

(

((((

(

(

(

(

((

(

((

(

((

(

(

(

(((((((((

(((

(

(

(

Till Te

(

(

(

((

(

((((

(

(

(

(

((

(

((

(

((

(

(

(

(((((((((

(((

(

(

(

Till Ni

(

(

(

((

(

((((

(

(

(

(

((

(

((

(

( (

(

(

(

(((((((((

(((

(

(

(

AM

(

(

(

((

(

((((

(

(

(

(

((

((

(

(

((

(

(

(

(((((((((

(((

(

(

(

EM

(

(

(

((

(

((((

(

(

(

(

((

(

((

(

( (

(

(

(

(((((((((

(((

(

(

(

Till Cu

(

(

(

((

(

((((

(

(

(

(

((

(((

(

( (

(

(

(

(((((((((

(((

(

(

(

Gravity

(

(

(

((

(

((((

(

(

(

(

((

((

(

(

((

(

(

(

(((((((((

(((

(

(

(

Till Au

(

(

(

((

(

((((

(

(

(

(

((

((

(

(

((

(

(

(

(((((((((

(((

(

(

(

Fuzzy Gammaγ=0.90

Fuzzy AndFuzzy And

Fuzzy Or

Fuzzy membership

Very low ( 0 - 0.2)

Low (0.21 - 0.4)

Moderate (0.41 - 0.6)

High (0.61 - 0.8)

Very high (0.81 - 1)

Conductive

alteration

zones

Sulphide

sources

Prospectivity

map

LOWBROAD REGIONAL

PREDICTION

At smaller scales, the Targeting Process (Prediction)gives way to Direct Detection

EXPLORATION

May 1999 From Bel-574.cdr

HIGH

PROSPECT SCALE

SCALE

RE

LA

TIV

EE

FF

EC

TIV

EN

ES

S

DETECTION

Source: Jon Hronsky

Scale dependency

7

MINERALS SYSTEM CONCEPTMINERALS SYSTEM CONCEPT

OUTFLOW ZONE Alteration; Geochemical Dispersion

��

TRAP / CAP Structure; Reactive & Impermeable Rocks

��

TRANSPORT Faults/Shears; Permeability Zones��

FLUID/METAL SOURCE Fluid Reservoir; Metal Source; Leaching

Source: Lesley Wyborn et al. (1994)

SIMPLE MINERAL SYSTEM MODEL AT DISTRICT SCALE: SIMPLE MINERAL SYSTEM MODEL AT DISTRICT SCALE: ARCHAEAN OROGENIC GOLD DEPOSITSARCHAEAN OROGENIC GOLD DEPOSITS

Distal

Magmatic

Fluid

Fluid from Subcreted

Oceanic Crust

Metamorphic Fluid

Metamorphic Fluid

SOURCESOURCESOURCESOURCE

FLUID PATHWAYFLUID PATHWAYFLUID PATHWAYFLUID PATHWAY

TRAPTRAPTRAPTRAP

SEALSEALSEALSEAL

Granulite

Amphibolite

Mid -

Greenschist

Sub –

Greenschist

σ1σ1

Volcanic Rock

Dolerite

Sedimentary Sequence

Granite I

Granite

II

Source:

David Groves

TYPES OF GIS ANALYSISTYPES OF GIS ANALYSIS

1. Association

: lithology, metamorphic domain, alteration domain

2. Proximity

: crustal- to regional-scale faults : lithological contacts (rheology

indexed!) : granitiod contacts : anticlinal hinges/uplifted zones

3. Trend : all faults and contacts (jogs, orientation for reactivation)

4. Abundance : all faults and contacts (intersections)

: porphyry-lamprophyre dykes

5. Complexity : granitiod contacts (heterogeneous stress)

8

•I Empirical (data driven) approach

•Suitable for mature ’brown fields’ exploration terrains with abundant data available

•II Conceptual (knowledge driven) approach

•Suitable for ’green fields’ exploration terrains with limited number of deposits available for statistical assessment

Methods of spatial data analysis used for prospectivity mapping


•Known mineral occurrences as ‘training points’ are used for examining spatial relationships between known occurrences and particular geological, geochemical and geophysical key features

•Identified relationships are quantified and integrated into a single prospectivity map


•Re-formulation of knowledge about deposit formation into mappable criteria (i.e. threshold values in geochemistry and geophysics etc., certain structures or formations in the geological maps…)

•Areas that fulfill the majority of these criteria are highlighted as being the most prospective

•Methods of spatial data analysis used for prospectivity mapping


•Neural networks (RBFLN, PNN etc.)

•Weights of evidence, logistic regression


•Boolean logic

•Index overlay (binary or multi-class maps)

•Evidential belief function

•Dempster-Shafer model

•Decision tree approach

•Fuzzy logic

•Expert Weights of Evidence

•Methods of spatial data analysis used for prospectivity mapping

9

1. Selection of the

relevant data basedon the exploration

model

2. Input pattern

generation

3. Spatial analysis

4. Evaluation

Data integration methodology

•Data preprocessing philosophy

•Remote Sensing

•Geophysics

•Geochemistry

•Geology

•Garbage In,

•Garbage Out

• Prospectivity Maps

•GIS

• Analyse / Combine

• Prospectivity Maps

•GIS

• Analyse / Combine

•Good Data In, Good

•Resource Appraisal Out

•Remote Sensing

•Geophysics

•Geochemistry

•Geology

•Challenges with existing spatial modelling approaches

•Empirical (data driven) approach

•Conceptual (knowledge driven) approach

•=> can’t use in poorly-explored areas

•=> dependent on training sites

•=> difficult to reproduce

•=> how to define the prospective

• clusters?

•• subjective judgement

• unsupervised

•• statistically-based

• supervised

10

Orogenic vs. other gold deposit stylesOrogenic vs. other gold deposit styles

Tectonic setting of orogenic goldTectonic setting of orogenic gold

Gravity Airborne geophysics•magnetics•electro-magnetics•gamma radiationBedrock geologyGeochemistry SoilSatellite imagesModellingDigital elevationmodel and basemapsDigital map data

11

Study area

•Study Area: ~20 000 km2

•Northern FennoscandianShield•Located 100 km north from Arctic Circle•Excellent infrastructure•Green field exploration•35 orogenic Au occurrences•prior probability 0.0009

Study Area:Study Area:Central LaplandCentral LaplandGreenstoneGreenstoneBeltBelt

Suurikuusikko24.3 Mt @ 4.6 ppm Aures. 120t Au (3.7 MOz)

Pahtavaara2 Mt @ 2.5 ppm Aures. 10t Auproduction 1996-2000: 4.6t Au

Saattopora0.68 Mt @ 3.6 ppm Auproduction 1988-1995: 6.3t Au

Exploration Model

• Early Proterozoic greenstone hosted orogenic Au deposits

• Symptoms:

– Alteration zones with low magnetic and low resistivity signature,

– large scale crustal structures represented by horizontal gradient highs of regional gravity

– Anomalies in till indicating presence of sulphides and Au

12

Weights of Evidence orogenic gold

model, Pure Empirical WofE

Bedrock geology

Gravity: horizontal gradient

Airborne electromagnetics: apparent resistivity

Airborne magnetics: magnetic field total intensity

Combined till geochemistry: As, Au, Cu, Fe, Ni and Te

Evid

en

ce

laye

rs

Weights of Evidence orogenic gold

model, Pure Empirical WofE

•High gravity gradient

•Aero-geophysics: AM low, Resistivity low•Anomalous till geochemistry for As, Au, Cu, Fe, Ni and Te

Weights of Evidence orogenic gold model, Combined Empirical/Conceptual WofE

Bedrock geology





Density of contacts: Lithodiversity

Proximity to greenstone/sedimentary contact

Distance from granite midpoints:

zones of convergent/divergent flow

Palaeostress model: zones of dilation

Evid

en

ce

laye

rs

Poor correlation

13

Weights of Evidence orogenic gold model, Combined Empirical/Conceptual WofE

•High gravity gradient

•Aero-geophysics: AM low, Resistivity low•Anomalous till geochemistry for As, Au, Cu, Fe, Ni and Te

•Density of lithological contacts•Proximity to Sirkka Shear zone or divergent/convergent stress regimes

•Proximity to greenstone/sedimentary contacts•Paleostress modelling

Evidence Method Score W+ W- Contrast* Confidence

Gravity gradient CD 33 0.7757 -2.9342 3.7099 3.6544

Sirkka shear or zones of convergernt/divergent flow CD 30 0.7912 -1.6293 2.4205 4.5447

Apparent resistivity CA 19 1.5889 -0.6972 2.2860 6.5973

Proximity to sediment greenstone contacts CA 32 0.2710 -1.5683 1.8394 2.5227

Anomalous till geochemistry CD 27 0.5920 -1.0019 1.5939 3.7547

Aeromagnetic lows CA 9 1.3483 -0.2363 1.5846 4.0601

Paleosterss modeling CD 3 1.4148 -0.0462 1.4610 1.9942

Density of lithological contacts CD 28 0.1455 -0.4898 0.6353 1.4109

Total no of training sites = 34

Total area = 18642 km2

Prior probability = 0.0018

CD = weights calculated by cumulative descending method (i.e. from highest to lowest class)

CA = weights calculated by cumulative ascending method (i.e. from lowest to highest class)

Score = number of training sites within the 'inside' pattern

Contrast = W+ - W-

Confidence = Contrast/Std(Contrast)

Weights of Evidence

Positive evidence

Negative evidence

Summary of the weights calculation. The evidence is sorted by contrast values in descending order, the ’best’ layer on the top.

Artificial neural networks

y

x1

x3

x2

Supervised:

RBFLN, PNN, Fuzzy NN

Unsupervised:

SOM

14

Artificial Neural Network (RBFLN) model

Bedrock geology





Density of contacts: Lithodiversity

Proximity to greenstone/sedimentary contact

Distance from granite midpoints:

zones of convergent/divergent flow

Palaeostress model: zones of dilation

Evid

en

ce

laye

rs

Poor correlation

Weighted training sites

••Deposit data from Eilu (2007). FinGOLD http://arkisto.gtk.fi/tr/tr166.pdfDeposit data from Eilu (2007). FinGOLD http://arkisto.gtk.fi/tr/tr166.pdf

•42

•Vesa Nykänen, 23.3.2011

Neural network model: RBFLNNeural network model: RBFLN

•(a) Training sites treated equally important -> no weighting applied

•(b) Training sites weighted based on their total in situ gold

15

II Conceptual approach

• Step 1: Select data sets based on the exploration model

• Step 2: Assign fuzzy membership alues i.e. rescale all data into a common scale from 0 -> 1

(e.g. not favorable -> favorable)

• Step 3: Combine all the evidence data by using various fuzzy operators (like Fuzzy OR, Fuzzy AND, Fuzzy Sum, Fuzzy Product, Fuzzy Gamma etc.)

• Step 4: Validate your model

• Step 5: Refine your model if needed and repeat!

Step 1 Step 2 Step 3 Step 4

Step 5

Fuzzy membership: Favorable for Au?

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 50 100 150 200 250 300 350 400

Pathfinder element (concentration)

Fu

zzy M

em

bers

hip

No

Yes

Missing Data or Uncertain

Probably

No

Probably

Yes


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 50 100 150 200 250 300 350 400


Fu

zzy M

em

bers

hip

No

Yes


Probably

No

Probably

Yes

16

Fuzzy Logic

gold prospectivity model

Fuzzy Logic

gold prospectivity model

27°E

27°E

26°E

26°E

25°E

25°E

24°E

24°E

68

°N

68°N

67°3

0'N

67°3

0'N

25

Km

Gold deposits and occurrences

Orogenic Gold Prospectivity Model

Favorability

Very Low (0 - 0.2)

Low (0.2 - 0.4)

Moderate (0.4 - 0.6)

High (0.6 - 0.8)

Very high (0.8 - 1)

´

Suurikuusikko

Validation of the modeling1. Statistical validation

– ’leave one out’– ROC curves

2. Validation sites– seven previously known gold prospects which

were not used as training sites

3. Field testing– sampling of high prospectivity targets

4. Resampling of existing drill core– over 100 drill holes without Au assays

intersecting high prospectivity areas

17

Statistical validation: ’LEAVE ONE OUT’

The validity of the modeling was tested by calculating 23 successive

models and leaving out each of thedeposit respectively.

The posterior probabilityvalue of each of the model

was associated with the depositpoint left out from the model.

WofE finds 25 of 35 training sites(posterior probability > prior

probability)

Validation: ROC (Receiver Operator Curve)

Brismar, 1991, American Roentgen Ray Society: v 157, p. 1119-1121.

Sites

“Not”

Sites

ANN models Wofe, LR, Fuzzy models

Field validation

18

Field validation: Sampling outcrop

0.10.1--0.3 ppm Au0.3 ppm Au

Field validation: Re-sampling drill core

0.1 ppm Au

2.1 ppm Te

Field validation: Sites not used for training

Trench Length vs Au g/t

M10/2001 6.0 m @ 4.8 g/t

M11/2001 2.0 m @ 1.4 g/t

M2/2004 7.0 m @ 13.7 g/t

M3/2004 3.0 m @ 3.1 g/t

Drill hole Intersection

R310 2.0 m @ 3.3 g/t

R311 1.0 m @ 4.8 g/t

R256 1.5 m @ 2.8 g/t

R258 1.5 m @3.8 g/t

R259 1.0 m @ 4.8 g/t

19


0.3 ppm Au0.3 ppm Au

>0.1 ppm Au


>0.1-2.4 ppm Au

2.4 ppm Au2.4 ppm Au

IOCG FUZZY MODEL20 evidence layers

20

•AM high & U-radiation•proximity to craton margin

and suture•proximity to 1.7-1.9 Ga

granitoids•proximity to hematite showings

•incompatible and compatible elements in till geochemistry

•sulphides in till•Gravity high•Density of linear features

IOCG FUZZY MODEL

Ni prospectivity in Central

Lapland

Vesa Nykänen and Ilkka Lahti

•De

pth

[m

]•B

ou

gu

er

[mG

al] •Gravity profile

21

•High pass filter (cut off wavelength 5km)

•Gravity profile

•[m

Ga

l]•D

ep

th [m

]•B

ou

gu

er

[mG

al]

27°E

27°E

26°E

26°E

25°E

25°E

68°N

68°

N

67°5

0'N

67°5

0'N

67°4

0'N

67°40

'N

67°3

0'N

67°3

0'N

0 105Km

30°E

30°E

25°E

25°E

20°E

20°E

69°N

69

°N

66°N

66°N

63°N

63

°N

60°N

60°N

Mining concession

Claim (Ni-Cu)

Claim application

Reservation

Magnetic field total intensity

High : 17280

Low : -3014

•Magnetic field total intensity

•Lithologicalboundaries

27°E

27°E

26°E

26°E

25°E

25°E

68°N

68°

N

67°5

0'N

67°5

0'N

67°4

0'N

67°40

'N

67°3

0'N

67°3

0'N

0 105Km

30°E

30°E

25°E

25°E

20°E

20°E

69°N

69

°N

66°N

66°N

63°N

63

°N

60°N

60°N

Mining concession

Claim (Ni-Cu)

Claim application

Reservation

High pass AM

High : 17365.5

Low : -2847.5

•Magnetic field totalintensity

•High pass filterusing median value over 2 km radius neighborhood

22

27°E

27°E

26°E

26°E

25°E

25°E

68°N

68°

N

67°5

0'N

67°5

0'N

67°4

0'N

67°40

'N

67°3

0'N

67°3

0'N

0 105Km

30°E

30°E

25°E

25°E

20°E

20°E

69°N

69

°N

66°N

66°N

63°N

63

°N

60°N

60°N

Mining concession

Claim (Ni-Cu)

Claim application

Reservation

Gravity

Bouguer anomaly

High

Low

•Regional gravity

•Bouguer anomaly

27°E

27°E

26°E

26°E

25°E

25°E

68°N

68°

N

67°5

0'N

67°5

0'N

67°4

0'N

67°40

'N

67°3

0'N

67°3

0'N

0 105Km

30°E

30°E

25°E

25°E

20°E

20°E

69°N

69

°N

66°N

66°N

63°N

63

°N

60°N

60°N

Mining concession

Claim (Ni-Cu)

Claim application

Reservation

FML Gravity

Value

High : 0.997404

Low : 2.41361e-013

•Regional gravity

•High pass filter using median value over 2 km radiusneighborhood

27°E

27°E

26°E

26°E

25°E

25°E

68°N

68°

N

67°5

0'N

67°5

0'N

67°4

0'N

67°40

'N

67°3

0'N

67°3

0'N

0 105Km

30°E

30°E

25°E

25°E

20°E

20°E

69°N

69

°N

66°N

66°N

63°N

63

°N

60°N

60°N

Mining concession

Claim (Ni-Cu)

Claim application

Reservation

Ni in till

High

Low

•Ni in till

•Interpolated using ’Inverse DistanceWeighting’ method

23

27°E

27°E

26°E

26°E

25°E

25°E

68°N

68°

N

67°5

0'N

67°5

0'N

67°4

0'N

67°40

'N

67°3

0'N

67°3

0'N

0 105Km

30°E

30°E

25°E

25°E

20°E

20°E

69°N

69

°N

66°N

66°N

63°N

63

°N

60°N

60°N

Mining concession

Claim (Ni-Cu)

Claim application

Reservation

Ni in till (resid 6k)

High

Low

•Ni in till

•High pass filter using median value over 6 km radius neighborhood

27°E

27°E

26°E

26°E

25°E

25°E

68°N

68°

N

67°5

0'N

67°5

0'N

67°4

0'N

67°40

'N

67°3

0'N

67°3

0'N

0 105Km

30°E

30°E

25°E

25°E

20°E

20°E

69°N

69

°N

66°N

66°N

63°N

63

°N

60°N

60°N

Mining concession

Claim (Ni-Cu)

Claim application

Reservation

And till (Ni, Cu, Co)

High

Low

•Combined till geochemistry(Ni+Cu+Co)

27°E

27°E

26°E

26°E

25°E

25°E

68°N

68°

N

67°5

0'N

67°5

0'N

67°4

0'N

67°40

'N

67°3

0'N

67°3

0'N

0 105Km

30°E

30°E

25°E

25°E

20°E

20°E

69°N

69

°N

66°N

66°N

63°N

63

°N

60°N

60°N

Mining concession

Claim (Ni-Cu)

Claim application

Reservation

Ni prospectivity

Very low

Low

Moderate

High

Very high

•69•Vesa Nykänen, 27.4.2011

•Prospectivity map combining AM,gravity and till geochemistry

24

Summary

• This prospectivity model combined till geochemistry (Ni, Cu and Co) with airborne magnetics and regional

gravity

• The data was filtered using a high-pass filtering

technique, where long wave length signal is removed from the data resulting local anomalies

• Resulting prospectivity map identifies more than 40 targets areas favorable for Ni-Cu deposits

• Most of these areas are under active exploration

Coffee/Tea break

LAB 1

Navigating in ArcGISDisplaying data in a GIS

25

geology(1-of-n coded) magnetic anomalygamma-ray

channelsto tal countdistance to nearest faultK

ThU

input layerhiddenlayer

internal biases =

Map

1 9 sandstone Cambrian

2 6 rhyodacite E. Triassic

3 7 granite E. Triassic

4 4 leucogranite L. Triassic

Polygon Class Rock Type AgeAttributes

GIS software links the location

(map) data and the attribute data

N

WHAT IS GIS

Query

Analyze

Store Display

Capture

Output

What a GIS Does

Introducing ArcMAP

• Starting a ArcGIS Map Document

– From the start menu Programs->ArcGIS->ArcMAP

– Then select the document to open from the File Menu

26

ArcMAP Layout

Table ofcontents(layers)

Map area

Cursor location

Toolbars

Menuitems

Map scale

Click the Add Data button ( ) and navigate to the Directory with the data.

Select the layer to add or shift click to select multiple layers

Adding Data to ArcMAP

Click Add to add the layers to the cuurent data frame

Point layer

Line layer

Polygon layer

Raster layer

Zoom In – Zooming around using either a single click or dragging a rectangle over an area you wish to zoom into

Zoom Out – Zooming out using either a single click or dragging out an area you wishto zoom out from.

Fixed Zoom In – Zooms using a fixed rate in (50% centre of page)

Fixed Zoom Out – Zooms using a fixed rate out (50% centre of page)

Pan – Moves around on the map without changing the zoom scale

Full Extents – Zooms to the full extents of all the data on your map

Go back to Previous extents – Allows you to return to your last zoom

Go to Next Extents – Allows you go to your previous zoom

Select Features – Used for selecting a feature (feature in a layer)

Select Element – Used for selecting an element (text element on the map)

Identify – Use this to click on an item and display the layer attributes

Find – To find a specific piece of data

Measure – Measure distances between objects on the map

ArcMAP Navigation

27

Changing a Layers Display

Changing a Layers Properties

Right click on a layer in the Table of Contents and select properties

Demo 1

Introducing ArcMAPHow to Load, Display and Investigate Data

28

Demo 11. Start ArcMAP and open the Map Document

c:\HY2010\Mapdocuments\LAB1

2. Add all of the data in c:\HY2010\Rawdata (note the subfolders)

3. The data provided is over Finland, please investigate the data by

a. Paning, zooming in and out,

b. Turning layers on and off,

c. Changing the order of the display, and

d. Change the display parameters for the layers

4. Change the properties for the several layers to improve their display

Table 1: Raw Data

Raw data C:\HY2010\Rawdata

Layer name Description

mask Study area grid

am Airborne magnetic field total intesity

till_geochem Atlas till geochemistry (1 sample / 300 km2)

lito_geochem Litogeochemistry

struli Faults, thrusts etc.

Lithpo97_b A 1:1M scale bedrock map

lithli97

Boundaries of geological units in a 1:1M

scale bedrock map

FINNPGE PGE deposit database

FINZINC Zinc deposits database

FINNICKEL Nickel deposits database

FINGOLD Gold deposits database

FINCOPPER Cu deposits database

LAB 2

Geoprocessing EnvironmentsArcGIS 9.3 Setup

29

Introduction to ArcToolbox

• Environment

– Tools/Options

• ArcToolbox

• Model Building

Introduction

• Environment

– Tools/Options

• ArcToolbox

• Model Building

Tools/Options: Raster tab

• Raster Attribute Table

– Number of unique

conditions or records in the raster attribute table

limited to 65,536

– For Neural Network and

Logistic Regression tools, may need a larger value.

30

Tools/Options: Geoprocessing tab

• General

– Check “Overwrite the

outputs…”

• My Toolboxes– C:\Documents and Settings\graines\Application

Data\ESRI\ArcToolbox\My Toolboxes

• Environment Settings

– These are independent of

Spatial Analyst Options and settings are required

for most SDM Tools

Environment

• General Settings

• Raster Analysis

Settings

Environments: General Settings

• Current Workspace

• Scratch Workspace

• Extent

All three must be set for WofE, Logistic Regression, Neural Network.

Maybe best to always set all three.

31

Environment: Raster Analysis

Settings

• Cell Size

• Mask – The study area

Both must be set for

WofE, Logistic

Regression.

Maybe best to always set

both.


• Environment

– Tools/Options

• ArcToolbox

– Environment - Right click ArcToolbox

– Save Settings

– Load Settings

• Model Building


• Environment

– Tools/Options

• ArcToolbox

– Environment - Right click ArcToolbox

– Save Settings

– Load Settings

• Model Building

– Add a new Toolbox

• Rename

32

Demo 2Geoprocessing Evironments

Setting the Parameters for all Subsequent GIS Processing

Demo 2• Start arcmap and open the Map Document

c:\HY2010\MapDocuments\Lab2

• Set the enviroment parameters

– The current workspace to c:\HY2010\Workspace

– The scratch workspace to c:\HY2010\Workspace\Temp

– Set the extent to c:\HY2010\RawData\mask

• What is the Mask data set?

• Define the following properties of the mask layer:

– Cell size?

– Total cells?

– Total area?

– Define the extent

• Min Easting?

• Max Easting?

• Min Northing?

• Max Northing?

LAB 3

Geoprocessing ToolsCreating Derived GIS Layers

33

Derived Layer Philosophy

Remote Sensing

Geophysics

Geochemistry

Geology

Garbage In,Garbage Out

Prospectivity Maps

GIS

Analyse / Combine

Prospectivity Maps

GIS

Analyse / Combine

Good Data In, Good Resource Appraisal Out

Remote Sensing

Geophysics

Geochemistry

Geology

Discrete and continuous data

• Discrete features– Distinct boundaries

– Stored as integer values

– Land use, zoning, vegetation,

– lakes, roads, rivers

• Continuous phenomena– Continuously changing values

– Stored as floating point values

– Elevation, noise pollution, rainfall,

– Slope and temperature

GRIDS

Cells, rows, and columns

0 1 2 3

3

2

1

0

Row

Column

Cell (2,3)

• Grid themes are an organized matrix of cells

• Cells are organized into rows and columns

• Rows and columns have an index position number

• Top left cell is at the 0,0 position

GRIDS

34

Value Attribute Table

Value attribute Table

Is a table associated with

Integer grids that contains

related information

GRIDS

Grid Layer Properties

• Cell size: Dimension,

Units

• Extent Rows, Columns

• Type: Boolean, Integer,

Floating

• Mask: No data

GRIDSArithmetic Operators

35

GRIDSEuclidean Feature

Spatial processing of grid cells, allow for distance, direction and

allocation of proximity

Euclidean FeatureGRIDS

Euclidean_distanceReturns distance to nearest feature

Euclidean_directionReturns direction to nearest feature

Euclidean_allocationReturns value of nearest feature

Inverse Distance Weighting

• Determines cell values using a weighted

combination of a set of sample points,

with the weight a function of inverse

distance.

• The further an input point is from the

output cell location, the less importance it

has in the calculation of the output value.

GRIDS

36

GRIDSGrid Processing Functions

Cell by cell

Neighborhood

Some of the layers that can be generatedHost rock

• Type• Young's modulus, CRC

• Young's modulus, AMC• Compressive Strength

• Tensile Strength

• Fracture toughness• Type

• Fe/(Fe+Mg+Ca)• Fe(wt%)* Fe/(Fe+Mg+Ca)

• Fe2O3/(Fe2O3+FeO)• Fe2O3/FeO

• Fe2+/Fe3+

Contact Type• distance to

• density• Strike

• Contrast Young's modulus, • CRC Contrast Young's modulus,

• AMC Contrast Compressive Strength

• Contrast Tensile Strength• Contrast Fracture toughness

• Contrast Chemical ratios• Contrast Fe/(Fe+Mg+Ca)

• Contrast Fe(wt%)* Fe/(Fe+Mg+Ca)• Contrast Fe2O3/(Fe2O3+FeO)

• Contrast Fe2O3/FeO

• Contrast Fe2+/Fe3+

Fault• Distance to• Distance of Regional NNW fault• Distance of Regional NE fault• Distance of Regional E fault• density• Strike• Strike of Regional NNW fault• Strike of Regional NE fault- Strike of Regional E fault• Strike of Regional NNW fault• Strike of Regional NE fault• Strike of Regional E fault• Distance to nearest intersectionPorphyritic felsic intrusionField strength data• +ve mag. Anomaly• -ve mag. Anomaly- average by Host Rock- Variation from average- Slope- Variation- Magnetic intensity (RTP)Bouguer gravity anomalySynclinal axes • distance to• Strike

…

etc

ArcToolbox

Training Aid

SDM Tools

37

Spatial Data

Modeller (SDM) toolbox

Geoprocessing tools for integration of spatial data to predict the location to any features

(i.e. mineral deposits, animal habitat,

disease outbreaks … etc).

SDM is available at the ESRI ArcScripts site :

http://arcscripts.esri.com/details.asp?dbid=15341

or at the University of Campinas site:

http://www.ige.unicamp.br/sdm/default_e.htm

Creating a New Toolbox

• Right click on Toolbox and select from the drop down menu New Toolbox

• A new toolset called ToolBox will be added

• To rename right click and select rename

• Use toolsets to store your processing models

Creating Models• Create a model in your tools right

click and add New Model

• Right click on your new model to:

– Rename

– Edit

– Properties

– Edit Documentation

• Help

– Open

38

Creating a Simple Model

• Drag Reclassify to your model

• Right click the Reclassify tool and select Open.

• Blue – Input data

• Orange – Process

• Green – Output data

Reclassify Tool

Right click open

or double click

Model Button Commands

Save

Print

Cut

Copy

Paste

Add L

aye

r

Auto

Layo

ut

Full

Exte

nt

Zoom

in

Zoom

Out

Mag

nify

Tool

Pan

Continuo

us Z

oom

Navig

ate

Tool (?

??)

Sele

ct

Add C

onnectio

n

Run

39

Sharing Models

• User Toolboxes

– C:\Documents and Settings\graines\Application

Data\ESRI\ArcToolbox\My Toolboxes

– Location defined in Tools->Options

• Relative versus absolute paths

– In ArcMap

– In Models

• %Workspace%

• %ScratchWorkspace%

• Iteration for example Large%i%

Documentation of Models

• Documentation is useful to remind you about the model or to tell an associate

how to use your model.

– Right click the model and select Edit

Documentation

• Model Reports

Demo 3Creating Distance to Thrust Faults

Generate a distance grid to thrust fault and reclassify into ten classes

40

Demo 31. Start arcmap and open the Map Document


2. Create a new toolbox called MyTools

3. Generate a distance to Faults Layer, by follow these steps:

– Add a new model to MyTools and rename it to Dist_to_Faults

– Edit the new model and add the layer Struli97 (drag and drop)

– Add the Euclidean Distance tool (drag and drop) which is located in the Spatial Analyst toolbox and the Distance toolset

– Use the Add Connection tool to link the Struli97 to the Euclidean Distance in the model window

– Double click on either the Euclidean Distance or Output Distance to definethe Output Distance Raster, set the new name to beC:\HY2010\Workspace\Temp\Dist_faults

– Right click the Output Distance model and select Add to Display

– Save the model

– Run the Model

– Set the properties for the new layer to be red close to the faults and blueaway from the faults

Demo 34. Create a new model called Distance_to_GeologyLines (using the

same approach as question 3) that will generate a distance grid calledDist_geolines.

5. Create a new model call Density_of_Geolines that will be used to examine the association of gold minerisation to density of geologycontacts. In this model you need to load the Geology Lines layer and use Line Density tool in the Density Toolset located in the SpatialAnalyst Toolbox. Set the search radius to 2000m.

6. Create a new model call Density_of_Faults that will be used to examine the association of gold minerisation to areas of high faultintersections. In this model you need to load the Faults layer and useLine Density tool in the Density Toolset located in the Spatial AnalystToolbox. Set the search radius to 2000m.

7. Use the Till geochemisty, and the IDW tool in the Interpolation toolsetlocated in the Spatial Analyst Toolbox to generate a continuoussurface of the Cu chemistry _(set the Z value to Cu_312P). Use the default values for IDW

Table 1: Raw Data

Raw data C:\HY2010\Rawdata


mask Study area grid

am Airborne magnetic field total intesity

till_geochem Atlas till geochemistry (1 sample / 300 km2)

lito_geochem Litogeochemistry

struli Faults, thrusts etc.

Lithpo97_b A 1:1M scale bedrock map

lithli97

Boundaries of geological units in a 1:1M

scale bedrock map

FINNPGE PGE deposit database

FINZINC Zinc deposits database

FINNICKEL Nickel deposits database

FINGOLD Gold deposits database

FINCOPPER Cu deposits database

41

Lunch

LAB 4

Weights of Evidence: trainingDefining sites to measure spatial associations

Weights-of-Evidence Method

• Originally developed as a medical diagnosis system

– relationships between symptoms and disease

evaluated from a large patient database

– each symptom either present/absent

– weight for present/weight for absent (W+/W-)

• Apply weighting scheme to new patient

– add the weights together to get result

42

Weights of Evidence - WofE

• Data driven technique

– Requires training sites

• Statistical calculations are used to derive

the weights based upon training sites.

• Evidence (maps) are generally reclassified

into binary patterns.

• Deposit location (binary grid)

• Deposit location > 1000 kg

Au (binary grid)

• Gold production (historic)

• Total contained gold (TCG)

Types of Deposit Layers

PAHTAVAARA

Central Lapland

Train Set Layer

• If Cell size is too small then

deposits resource layer has to be

subdivided.

• Cell size is too large deposit

associations have to be merged

Cell Size Problem

43

Resource Layer

Frequency

All Deposits Large Deposits

> 1000 TCG

Small Deposits

< 5000 kg TCG

Problems: Sparse data ClusteringNoise,Clustering

Medium size

deposits> 1000 & 50000

Kg TCG

Sparse data

Prior Probability

Area (A) = 10,000

N(deposits) = 10

= 10

10,000= 0.001

N(deposits)

Total AreaP(deposit) =



2. Calculate the number of deposits or N(D) in the Ni deposits layer

3. Calculate the area (A) of the mask layer, which defines the studyarea.

4. Calculate the prior probability using the following formula:

5. If 5 deposits new deposits were found last week and were added to the database would the prior probability increase or decrease?

= N(D)

AP(deposit) =

44

N(D) = 5

Conditional Probability

N(A) = 1,000

P(D | A) =

A

A ∩ D

A ∩ D

D N(total) = n = 10,000

A ∩ D

greenstone deposits

Definition of conditional probability

P(D ∩∩∩∩ A)

P(A)

greenstone

A ∩ D

A ∩ D

N(D ∩∩∩∩ A) / n

N(A) / n= =

N(D ∩∩∩∩ A) = 5

5

1000= 0.005

(in greenstone)A ∩ D

Deposit cells = 5

Posterior Probability

Greenstone cells = 1,000

P(deposit | greenstone present) = P(deposit) x F+, where A+ > 1

P(deposit | greenstone absent) = P(deposit) x F-, where A+ < 1

prior

Posterior

Conditional Probability and Bayes’ Rule

Bayes’ Rule

P(D | A) = P(D ∩∩∩∩ A)

P(A)

P(A | D )

P(A)P(D | A) = P(D)

priorposterior (conditional)

probability

update factor

P(A | D )

P(A)P(D | A) = P(D) Case where favourable

pattern is absent

45

Weights-of-Evidence Terms

• Weights for patterns

W+ - weight for inside the pattern

W- - Weight for outside the pattern

0 - Weights for areas of no data

• Contrast - a measure of the spatial

association of pattern with sites

• Studentized Contrast - a measure of the

significance of the contrast

Weights of Evidence

• Binary maps to define favorable areas

–Can use multi-layer patterns

• Measurements

–Area of study

–Area of Pattern

–Number of training sites

–Number of training sites inside the pattern

Application to Binary Evidence

1 2

1 50 8 0.8/0.5=1.6 ln(1.6)= +0.47

2 50 2 0.2/0.5=0.4 ln(0.4)= -0.92

Total 100 10

Class Area #sites Relative density Weight

46

Expected Values of Weights

• If sites occur randomly,

–Relative density (RD) = 1.0

–Weight (W) = ln(RD) = 0.0

• If sites occur more frequently than chance

–RD > 1.0, W is positive

• If sites occur less frequently than chance

–RD < 1.0, W is negative

The Following Slides Will use this Example

T = Total area = 1000km2

B = Favourable area = 500km2

B = Unfavourable area = not B = 500km2

Statistics names:

W+ = Positive weight

W- = Negative weight

C = W+ - W- = Contrast

B Not B

Bonham-Carter, personal comm. 2002

Example – More Points Than Chance

N(T) = 1000 unit cells (area of study region)

N(B) = 500 unit cells (area of theme B present)

N(B&D) = 20 (count of number of training points on B)

N(D) = 30 (count of total number of training points)

W+ = 0.2980 W- = -0.4157 C = 0.7138

More points on theme than would be expected due to chance

B Not B


47

Example – Many More Points





W+ = 0.6513 W- = -2.0414 C = 2.6927

Many more points on theme than would be expected due to chance

B Not B


Example – Equal Pattern and Points





W+ = 0.0 W- = -0.0 C = 0.0

Number of points on theme equals that expected due to chance

B Not B


Example – Small Pattern and Many Points





W+ = 1.0338 W- = -0.8280 C = 1.8617

Many more points on theme than would be expected due to chance

B Not B


48

Example - Weights Undefined





W+ = inf W- = -inf C = inf

Undefined: practical solution--assign fraction of point to (not B)

B Not B


Multi-class Themes

• Maps (themes) with unordered classes

(categorical) e.g. geological map. Calculate

weights for each class and then group classes

(reclassify) as needed.

• Maps (themes) with ordered classes (contour

maps e.g. geochemical or geophysical field

variables). Usually calculate weights based on

successive contour levels, cumulatively. Then

reclassify.


Multi-class – Categorical Classes


N(A) = 250 , N(B) = 500, N(C) = 250,

N(A&D) = 23, N(B&D) = 4, N(C&D) = 3,


W1 = 1.1866 W2 = -1.3442 W3 =-0.9347 Cmax =2.5308

Three classes, e.g. rock types (categorical scale of measurement)

A B C


Inside

Pattern

Outside

Pattern

49

Example – Association to Thrusts

Ordered Classes - Cumulative

B1 B

2B3

B2 B3 B4 B5 B6 B7 B8 B9

0 50 100 150 200 250 300 350 400


Inside

Pattern

Outside

Pattern

N(Bi) 100 100 100 100 100 100 100 100 100

Cum 100 200 300 400 500 600 700 800 900

N(D) 12 11 7 5 1 1 1 1 1

Cum 12 23 30 35 36 37 38 39 40

W+ 1.08 1.03 0.87 0.72 0.51 0.35 0.21 0.10 --

W- -0.25 -0.63 -1.01 -1.53 -1.53 -1.53 -1.53 -1.53 --

C 1.33 1.66 1.88 2.25 2.04 1.88 1.74 1.64 --

Distance to Fault

Convert maps to binary: Continuous variables

Apply 1400m as binary threshold

Distance to anticlinal axes (m)

Prospective Unprospective

50

Thrusts Association to Deposits

1. Create new weights model

2. Add (drag and drop)

Calculate Weights tool into

new model

3. Add distance to thrusts

layer

Example – Create Model

1. Set Type

• Cumulative Ascending weights

(from lowest to highest value),

• Cumulative Descending

weights (from highest to lowest

value), or

• Categorical (unique for each

class)

2. Set Output Weights Table

(generates a dbf file of weights)

3. Set Confidence Level of

Studentized Contrast. The default

value is 2 (>98% confidence)

Example – Create Model

51

Example – Checking the Results

1. Check the peak CONTRAST which defines the limit of the association

2. Check the Student Contrast (STUD_CNT) is greater then 2 and

therefore has a confidence higher than 98%

3. GEN_CLASS summaries the association, where 2 is favourable and 1

is unfavourable

Peak contrast and the cut off between what is favourable and what is unfavourable for gold mineralisation

If >2 and then confidence in the results is > 98%

Example - Join Weights to Layer

1. Use the Join Data command to

append the newly created weights

table to the GIS layer

2. Join the Value field in the GIS layer

to CLASS field in the weights table

3. Display the new layer using the

GEN_CLASS field

• A value of 2 is prospective

• A value of 1 is unprospective

Examination of Results

Red prospective

Blue unprospective

52

Demo 5Calculating Weights

Calculate the Weights from the Distance to Thrust Layer



2. Create a new toolbox called MyTools

3. Create a new tool Struct_Weights, and

a. Add (drag and drop) the Calculate Weights tool in the Weights of Evidence toolset located in the SDM toolbox,

b. Add density of structures layer (rcdnsstruli) to the model and link to the Calculate Weights tool using the add connection

c. Double click the calculate weights tool in the model to set the training points field to Au deposits (orogenic_gold)

d. Set the association type to be descending (as the deposits occur in areas with high density of faults)

e. Set the output weights table to be rcdnsstruli_CD.dbf

f. Leave the other input fields to the default settings

g. Click apply and OK

h. Run the model

Demo 54. Load the rcdst2struli_CD.dbf which is located in the

HY2010\workspace and open the table and answer the following questions (Note: the table may already be loaded if you have set the tools properties to add to display) :

a. Which class has the highest Contrast (see the CONTRAST field)?

b. What is the significance of the highest contrast value?

c. How many deposits occur in the favourable region (see the NO_POINTS field).

d. What is the total area in the favourable region?

e. If the student contrast (STUD_CNT) is a measure of the statistical confidence of a result, Using Table 1, provided on the next slide, to calculate the statistical confidence of the highest contrast?

f. Add the table c:\HY2010\Derived\ rcdnsstruli to load the distance values assigned with each class. Exam this table and comment on what is the critical distance away from a thrust that controls the location of the deposits. Note the CLASS field in rcdnsstruli_CD.dbf matches the ROWID in rcdnsstruli table

53

Table 1: Student T Values

Confidence T Value

99.5% 2.576

99% 2.326

97.5% 1.96

95% 1.645

90% 1.282

80% 0.842

70% 0.542

60% 0.253

Demo 54. Create a new model, as document in question 3, to investigate

the domain boundary layer (rcdst2domli) and determine the following:

a. Which class has the highest Contrast (see the CONTRAST field)?

b. What is the significance of the highest contrast value?

c. How many deposits occur in the favourable region (see the NO_POINTS field).

d. What is the total area in the favourable region?

e. What is the statistical confidence of the highest contrast?

f. Add the associated weights table (see table 2) and detereminewhat is the critical distance away from a fault?

g. Which is the better layer for controlling the distribution of the deposits, the ’Struli’ layer or the ’domli’ layer?

LAB 6

Generate Prospectivity MapCombine the Weights of Evidence Layers into a

Prospectivity Map

54

• Boolean

• Index overlay

• Fuzzy logic

• Weights of evidence (Bayesian

statistics)

Integration Methods:

BOOLEAN

0

1

1

02

1

1

0

1

1

OR =

01

1

AND

0

1

1

0

1

1

=


UNION

INTERSECTION

BOOLEAN EXAMPLE

AND OR


55

INDEX OVERLAY

• Areas given value based on importance e.g. 0 = low; 1 = moderate; 2 = high

• Maps combined and sum calculated for

each cell

• Not as restrictive as Boolean ‘AND’ and

not as permissive as Boolean ‘OR’


INDEX OVERLAY

0

1

1

2

2

4

0

1

2

0

1

2+ =


Layer 1: rock type Layer 2: +ve magnetic

anomaly

INDEX OVERLAY


56

Generate Weights Prospectivity

• Add the weights tables to the Table of Contents

• Create a new model

• Load the input layers into the

model (click and drag)

• Add the Calculate Response

tool located on the SDM

toolbox and the Weights of Evidence toolset

Calculate Response Tool

• Add the evidence Raster layers, either loading them from

the Calculate Response menu

or using the Add Connection Tool

• Load the weights tables, ensure

that the order is the same as the evidence layers.

• Set the Input Training Sites

Feature Class to be the

trainings sites.

• Output_Post_Prob_Raster: The posterior probabilty response raster created from the sum of the weights.

• Output_Prob_Std_Dev_Raster: Standard deviation due to the weights. If there is no missing data, this will also be the total standard deviation for the response raster.

• Output_MD_Variance_Raster: Variance due to missing data. This raster will only be calculated when missing data are expllicitly defined in at least one evidence layer.

• Output_Total_Std_Dev_Raster: Total standard deviation due to the weights and missing data. This raster will only be provided when there is explicitly defined missing data in at least one evidence layer.

• Output_Confidence_Raster: A raster showing the confidence that the reported posterior probability is not zero. This is the posterior probability divided by the total standard deviation, an approximite Student T test.

Calculate Response Tool

57

Results

Weights of Evidence• Simple model

– Weights

– Generalization of Evidence– Calculate Response– Model Symbolization

– Model Validation

• Generalization Rule (maximum contrast)

• Model Symbolization– Cumulative Area – Posterior Probability (CAPP)

Curves

• Conditional Independence Test– Agterberg-Cheng CI Test Tool

• Model Validation– Area Frequency Table Tool

Cumulative Area – Posterior Probability

CAPP Curve

0.00000

0.00000

0.00001

0.00010

0.00100

0.01000

0.10000

1.00000

0 20 40 60 80 100

Cumulative Area (%)

Favo

rab

ilit

y

Nonpermissive

Permissive

Favorable

Prior Probability

58

Demo 6Calculate Prospectivity Map

Calculate the Posterior Probabilty Using the Calulate Response Tool



2. Located in the Labtools toolbox are several tools, in thisexercise you will create several prospectivity maps using the Weights of evidence model

Open the Weights of evidence model tool, examine the modeland answer the following question

– How may evidence layers (or input layers) are used in the model?

– The thrust/fault layer (rcdnsstrulist) is an ascending or descendingassociation type in the calculate weights tool and why?

– Why is the till geochemisty (rc_till_cu) the opposite association typeto the thrust layer?

– What is the name of the prospectivity map generated?

– What is confidence value (the default value) used in the calculations of the weights?

– What is the name of the confidence map generated?

– Values above 2 in the confidence map are reliable or unreliable?

Demo 63. Delete two of the layers, which you regard as poor layers, from the

Weights of evidence model. You will also have to delete the associated calculate weights tools that were connected to the input layers you have deleted. When you have finnished editting the model then run. Running the model will take 5-10 minutes.

This will take between 5-10 minutes please use the time to have a coffee break

4. After the weights prospectivity map has been generated use the properties tool to recolour and better present your results.

5. Load the area frequency table, and investigate the data. This table documents how effect you prospective map is. How many deposits were captaured in the top 5% of the map?

6. Re-run the model with different input layers and investigate the results, which group of layers produce the best results.

59

LAB 7

Fuzzy LogicHow to Create Fuzzy Layers and the Principles of Fuzzy Logic

Example: Distance to Thrusts

Example: Distance to Thrusts

60

Example: Distance to ThrustsBinary LayerBinary Layer


Objective Fuzzy Membership Layer

1.0

0.1

0 0.467P(deposit)

61

• “stream sediment gold concentration is anomalous”

variable µ fuzzy set

• fuzzy membership function, µ [0, 1]

• value membership degree

< 2 ppb 0.0

2 - 5 ppb 0.5

> 5 ppb 1.0

• m/ship values assigned subjectively

Fuzzy Logic


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 50 100 150 200 250 300 350 400


Fu

zzy M

em

bers

hip

No

Yes


Probably

No

Probably

Yes


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 50 100 150 200 250 300 350 400


Fu

zzy M

em

bers

hip

No

Yes


Probably

No

Probably

Yes

62

FUZZY INDEX OVERLAY

• Areas given value in range [0,1]

• based on degree of membership of fuzzy set:

importance

– 0 => definitely NOT favourable

– intermediate values => degrees of favourability

– 1 => definitely favourable

• Maps combined by using the Raster Calculator


FUZZY INDEX OVERLAY

+ =


0.5

0

1

0 0

1 1 1

0.5 0.5 1 1 1

1 1 1

1 1 2 2 2

1.5 1.5 1.5

1 1 1

Fuzzy membership values in range [0,1] ; added

Weighted fuzzy index overlay

(Map1 x weight 1) + (Map2 x weight 2) = Output map

0.5

0

1

0 0

1 1 1

0.5 0.5 1 1 1

1 1 1

1 1

x 4 x 1

+ =2

0

4

0 0

1 1 1

1 1 1

1 1 1

4 4 5 5 5

3 3 3

1 1 1

2 2

63

Fuzzy Weighted Index Overlay Prospectivity Map

1) Min-operator,

2) Max-operator,

3) Algebraic-product operator,

4) Algebraic-sum operator, and

5) Gamma-operator.

• Five operators most frequently applied for

combining various exploration data sets are:

Fuzzy Logic Operators

1) Min-operator,

2) Max-operator,

3) Algebraic-product

operator,

4) Algebraic-sum

operator, and

5) Gamma-operator.

• Equivalent to the Boolean AND operation.

• Is the intersection of two or more maps.

• Formula for the min operator:

• The maximum fuzzy membership values

are ignored and the minimum values are

chosen as the result.

Map A = 0.75 Map B = 0.50

AND = 0.50

µµµµ (x) = min [µµµµ (x), µµµµ (x), µµµµ (x),.....]N B CA

Fuzzy Min (AND)

64

EXAMPLE: Fuzzy Min (AND)

• Equivalent to the Boolean OR operation.

• Is the union of two or more maps.

• Formula for the max operator:

• The maximum fuzzy membership

values control the output map

occurring at each location and the

minimum values are ignored.

Map A = 0.75 Map B = 0.50

OR = 0.75

µ (x) = max [µ (x), µ (x), µ (x),.....]N B CA

Fuzzy Max (OR)

1) Min-operator,

2) Max-operator,


operator,

4) Algebraic-sum

operator, and

5) Gamma-operator.

EXAMPLE: Fuzzy Max (OR)

65

• Formula for the algebraic-

product operator:

• Algebraic-product is decreasive as the

output values will always be smaller

than, or equal to, the smallest

contributing membership value.

Map A = 0.75 Map B = 0.75

PRODUCT = 0.375

µ (x) = Π µ (x)N i = 1

n

i

Fuzzy Algebraic Product (decreasive)

1) Min-operator,

2) Max-operator,


operator,

4) Algebraic-sum

operator, and

5) Gamma-operator.

EXAMPLE: Fuzzy Algebraic Product (decreasive)

• Algebraic-sum is increasive and

assumes full compensation. The

membership function is always larger

than, or equal to, the largest

contributing membership value.

Map A = 0.75 Map B = 0.75

SUM = 0.875

µ (x) = 1 - Π [1 - µ (x)]N i = 1

n

i

• Formula for the algebraic-sum

operator:

Fuzzy Algebraic Sum (increasive)

1) Min-operator,

2) Max-operator,


operator,

4) Algebraic-sum

operator, and

5) Gamma-operator.

66

EXAMPLE: Fuzzy Algebraic Sum (increasive)

• γγγγ = 1: the membership function equals

the algebraic-sum, γγγγ = 0: the member-ship

function equals the algebraic-product.

Map A = 0.75 Map B = 0.75

γγγγ = 0.95 fuzzy gamma = 0.839

γγγγ = 0.10 fuzzy gamma = 0.408

• Formula for the gamma operator:

• Is the combination of the algebraic-sum

and algebraic-product.

µµµµ (x) = [ΠΠΠΠ µµµµ (x)](1-γγγγ) *{1 - ΠΠΠΠ [1 - µµµµ (x)]}γγγγN i = 1

n

ii = 1

n

i

• The degree of compensation between

membership values depends on the choice of γγγγ:

Fuzzy Gamma Operator

1) Min-operator,

2) Max-operator,


operator,

4) Algebraic-sum

operator, and

5) Gamma-operator.

Gamma Function

= ( F. A. Sum) ×××× (Fuzzy Algebraic Product)

“increasive”

γγγγ in range [0,1]

γ 1- γ

“decreasive”

γγγγ = 0.90 - 0.98 gives best results

67

EXAMPLE: Fuzzy Gamma Operator

The Fuzzy Functions in SDM

Tools used to assignFuzzy membership values

Tools used to combine fuzzy membership layers

Creating the Fuzzy Layers in SDM

Min

imu

m

Ma

xim

um

Input Grid Values

Example using the Linear function

68

Using Fuzzy Operators in SDM

Layers to combine usingthe gamma functions

The gamma value used

Tools used to combine fuzzy membership layers

Example using the Gamma function

Demo 7Fuzzy Logic

Calculate Fuzzy Layer and Generate Fuzzy Prospectivity Maps

Demo 71. Start arcmap and open the Map Document c:\HY2010\MapDocuments\Lab7

2. Experiment with the fuzzy membership to generate fuzzy layers for the faultdensity and contact density layers.

3. Table 1, on the next slide, lists all of the fuzzy layers available, use the RasterCalculate to add several layers together (i.e. ”FM_tillcu + FM_tillas + … + FM_amres2”). The Raster calculator is located on the drop down menu of the Spatial Analyst tool bar. The resulting layer will be an index overlay map. Examine the result and comment on 1. how effective the result is? 2. What is the highest value in the map generated?

4. Run the Fuzzification 1 tool in the LabTools Toolbox and examine the results. What are the pros and cons of each of the fuzzy layers generated (you will haveto investigate the model to know what files are being generated)?

5. Create your own Gamma Funtion model and experiment with changing the gamma value. What happens when you increase or decrease the gamma value?

69

Table 1: Layers used in this Exercise


FM_tillcu Interpolated (IDW) surface of Cu

FM_tillas Interpolated (IDW) surface of As

FM_dnsstruli Density of faults

FM_lithodiv Lithodiversity

FM_amres2

Residual grid after subtracting median value

over 4 km radius neighborhood of airborne

magnetics

Layers are located in C:\HY2010\Derived\Fuzzy

Coffee/tea

LAB 8

Neural NetworkThe Principles of Neural Networks in Prospectivity Mapping

70

What are artificial neural networks?

• adaptive computer systems

• can learn from data

• can generalise to new data

NN applications: industrial and commercial

Commercial

• credit card applications and detection of fraud• prediction of stock prices

• real estate valuation

Industrial

• automated face-detection• speech recognition

• automated recognition of hand-written post-codes

• industrial process control - e.g. sheet metal mill• control of unmanned aircraft - helicopters

• reconfigurable flight control systems - compensate for unknown damage

• recognition of features in MRI images of heart valves• EEG-based diagnosis of neurological and psychiatric disorders

• signal processing - filters to recover clean tone bursts from time signals

NN applications: exploration

• porosity and permiability prediction from wire-line logs

• lithology classifiaction from wire-line logs

• seismic facies classification

• prediction of oil and gas recovery

Petroleum exploration

• interpretation of three component downhole TEM data

• identification of anomalies - SOM, Neural Mining Solutions

• clustering stream sediment geochemical data

• classification of deposits (deposit models - ore mineralogy)

Mineral exploration

71

Why use neural networks?

• uncertain, noisy data

• outliers

• non-linear relationships

• multiple interdependent parameters

• multiple populations

• mixed data sets - categorical & ratio

• pattern recognition

Advantages over statistical methods:

Feature space

•A feature space is an abstract space

where each pattern sample is represented as a point in n-dimensional space whose dimension is determined

by the number of features used to describe the patterns

•A feature vector is an n-dimensional vector of numerical features that

represent some object

•When dealing with grid data the feature vectors are created by combining all the

evidence maps into a single unique conditions grid

Combine• Combines multiple rasters so a unique

output value is assigned to each unique combination of input values -> feature vectors in n-dimensional space

• No more than 20 rasters can be used as input

• Works on integer values and their associated attribute table. If the values on the input raster are floating point, they will be automatically truncated, tested for uniqueness with the other input, and sent to the output attribute table.

• If a cell location contains NoData on any of the input rasters, that location will be assigned NoData on the output.

• The output raster is always of integer type

72

Limitations in using GeoXplorer

• Max number of unique conditions (i.e. feature vectors) is ~200 000– Workarounds:

• Decrease the size of your study area -> tedious if resulting into lots of sub-areas to be combined

• Increase the cell size of your evidence -> loss of information

• Generalize your evidence data by classifying into a limited number of classes -> disturbs the original clustering in the data

-> all solutions have some sort of disturbance

Limitations in using GeoXplorer

• Max number of random sampling size in fuzzy clustering is 1000 (500 + 500)

– Workarounds:

• No real workaround except decrease the size of your study area -> tedious if resulting into lots of

sub-areas to be combined

– When less than 1000 unique conditions use

the same number of random sampling sites

as the number of unique conditons

Architecture of an MLP neural network

Input layer

hidden layer

output layer

y

x1

x3

x2

weight

73



ThU


internal biases =

0

0

0

0

0

0

0

0

0

18-2-1 Multilayer Feedforward Neural Network

Processing steps in a single neuron

from Hassoun, 1995



ThU


internal biases =

x1

x2

xn-1

xn

w1

w2

wn-1

wn

y0

1

net =Σ x w y = f (net)f

Activation function

Feature Vector

Result

Converting GIS layers to feature vectorstargetoutput

1

Input feature vector

[3, 8, 33, 800]

GIS raster layers

Rocktype

EM

Gravity

Gradient

Distance to

Thrusts

Training sites

Deposits

74

Desired Output d

Input Vector

geology

magneticfault distance

10

50004

600

ActualOutput

y

Training a MLP Neural Network

NN

Desired Output

(Target)

d1

0.36

Adaptation of weights

from Zaknich 2003

Error = d - y

Σ

-

+

radiometric

Architecture of Radial Basis Functional Link Net

1) Use of added random noise to increase

number of deposit patterns available for training

2) Use of fuzzy membership layers to

combine subjective and empirical data

3) Self-Organizing Maps SOM (Kohonen

Neural Networks)

Getting More Out of Neural Networks

75

Use of added random noise to increase number

of deposit patterns available for training

Example

Problem: Lack of deposits limits training set size

1,254,000 cells

120 deposits

barren patterns

train

stop

test

deposit patterns

Creating feature vectors and addition of noise

76

Use of fuzzy membership layers to combine

subjective and empirical data

Example

Fuzzy-Neural Network Processing

Self-Organizing Maps SOM

(Kohonen Neural Networks)

Getting More Out of Neural Networks

77

Contrasting training strategies: SOM & PNN

Self-Organizing Map (SOM)MLP and Probabilistic Neural Networks

Supervised Training Unsupervised Training

target, t

y1

x1 x2 x3 x4

y2

x1 x2 x3 x4

y1 y2 y3

Class 0.3 0.7 Clusterson off off

Input pattern Input pattern

Trained SOM preserves topological ordering

Output is a topologically ordered map

BMU for green cluster

y

x

z

yx Z

A Neural Network Model in SDM

78

Considerations for Neural Networks in SDM

• Training sites are point shapefiles used to train the neural network.

• There must be two training site shapefiles, Deposits and “Not” Deposits.

• For RBFLN and PNN, the training points are examples of

what is desired and not desired.

– Can assign fuzzy memberships to these sites.

– RBFLN sensitive to “Nots”

– PNN insensitive to “Nots”.

WHY USE NEURAL NETWORKS?WHY USE NEURAL NETWORKS?

uncertain, noisy data

outliers

non-linear relationships

multiple interdependent parameters

multiple populations

mixed data sets

� ratio, interval, ordinal nominal scales of data

measurements

Neural network techniques can deal with:

TYPES OF NEURAL NETWORKTYPES OF NEURAL NETWORK

•• SupervisedSupervisedRequires known deposit and nonRequires known deposit and non--deposit deposit

sites for trainingsites for training

Probabilistic Neural Networks (PNN)Probabilistic Neural Networks (PNN)

Radial Basis Functional Link Net (RBFLN)Radial Basis Functional Link Net (RBFLN)

•• UnsupervisedUnsupervisedAlgorithms that can find the natural Algorithms that can find the natural clustering within the dataclustering within the data

Self Organising Maps (SOM)Self Organising Maps (SOM)•• Trains on complete dataTrains on complete data

Fuzzy Neural Nets (FNN)Fuzzy Neural Nets (FNN)•• Trains on a random sample of dataTrains on a random sample of data

79

Demo 8Neural Network

How to Setup and Run a Neural Network using SDM in ArcGIS

Demo 81. Start Arcmap and open the Map Document c:\HY2010\MapDocuments\Lab8

2. Edit NN Tool 1 in the LabTools Toolbox and examine the inputs.

3. Run Neural Network Input Files tool. Notice that Model Builder will first runCombine and Band Collection Statistics tools.

4. Run GeoExplorer

5. Run RBFLN in GeoExplorer and follow the instructions given

6. When RBFLN has been completed EXIT GeoExplorer

7. Edit NN Tool 1 output files in the LabTool Toolbox. Run the entire model. The resulting grid should appear on map. Examine the results.

Demo 88. Edit NN Tool 2 in the LabTools Toolbox and examine the inputs. The difference

with the previous NN Tool 1 is that here we use two set of random points as ”training” and ”not training” sites and we will perfor a Fuzzy Clustering NN.

9. Run Neural Network Input Files tool. Notice that Model Builder will first runCombine and Band Collection Statistics tools.

10. Run GeoExplorer

11. Run Fuzzy Clustering in GeoExplorer and follow the instructions given

12. When Fuzzy Clustering has been completed EXIT GeoExplorer

13. Edit NN Tool 2 output files in the LabTool Toolbox. Run the entire model. The resulting grid should appear on map. Examine the results.

14. Compare visually the two NN models.

80

Table 1: Layers used in this Exercise

Layers are located in C:\HY2010\Derived\Fuzzy


rc_till_cu Interpolated (IDW) surface of Cu

rc_till_as Interpolated (IDW) surface of As

rc_dnsstruli Density of faults

rc_lithodiv Lithodiversity

rc_amres2

Residual grid after subtracting median value

over 4 km radius neighborhood of airborne

magnetics

Comparison and Validation

Model Testing

Measures to compare, describe, and validate models

Simple Carlin Model Example

Measures of Models

• Correlation measures to compare models– Kappa for ranked models

– Pearson’s for raw models

• Fragstats: Measure the texture or appearance of the model. Does the model look geologic?

• Efficiency of Classification– Training sites

– Not-Training sites: What should “Nots” be?

• Efficiency of Prediction (Validation)– Sites not used for training

• ROC curves

• Jack-knife

81

PRC: Efficiency of Prediction

SRC: Efficiency of Classification• Intersect points with response grid.

• Frequency of points.• Join frequency of points with counts in response grid.

• Summation– Sort response value descending

– Cumulative area from high to low response value.– Cumulative number of points from high to low response value.

• Plot Cumulative area versus cumulative number of points

• Calculate area under the curve.– Area under the curve for sites should be greater than 50% of total area,

then have a positive association with points.

– Area under the curve for “Not” sites should be less than 50% of total area, then have a positive association with points

– If area under the curve, then have a random association with the evidence. Evidence provides no better information than guessing.

• Point in curve where goes from steep slope to flat slope is an optimal break between predicted sites and not sites.

Chung and Fabbri, 2003, Validation of spatial prediction models for landslide hazard

mapping: Natural Hazards, v. 20, p.451-472

ROC Terminology

• Intersect points with response grid to get probability at points.

• Frequency of points.

• Summations with data sorted from highest to lowest response values.

Positive Negative

Predicted

Positive

TP FP

Predicted

Negative

FN TN

Sensitivity = TP/(TP + FN)

TP + FN = Total number of sites

1- Sensitivity = Type II errors (Errors of Omission)

Specificity = TN/(TN+FP)

TN + FP = Total number of “Not” or negative sites

1- Specificity = Type I errors (Errors of Commission)

Measures are free from prevalence (rare events) and thresholds.

How to define the negative sites (“Nots”)?

Processing Steps

Validation: ROC

Receiver Operator Curve


Sites

“Not”

Sites

82

Validation: ROC (Receiver Operator Curve)


Sites

“Not”

Sites

ANN models Wofe, LR, Fuzzy models

Statistical validation: ’LEAVE ONE OUT’

The validity of the modeling was tested by calculating 23 successive

models and leaving out each of thedeposit respectively.

The posterior probabilityvalue of each of the model

was associated with the depositpoint left out from the model.

WofE finds 25 of 35 training sites(posterior probability > prior

probability)

ISBN 978-952-9618-72-9 (Printed) ISBN 978-952-9618-73-6 (Pdf)ISSN 0783-1331