Eun-Jung Holden, Yathunanthan Sivarajah, Peter Kovesi Centre for - - PowerPoint PPT Presentation

HUMAN AND MACHINE INSPECTIONS

OF GEOSCIENTIFIC DATA

Towards improving accuracy and efficiency of exploration data interpretation

Eun-Jung Holden, Yathunanthan Sivarajah, Peter Kovesi

Centre for Exploration Targeting, School of Earth & Environment, The University of Western Australia

BACKGROUND

• Low exploration success rates & increasing need

for challenging undercover exploration

• To improve exploration success, we address the

challenge of improving data interpretation, based

• n which exploration decisions are made.
• Exploration data interpretation is a challenging

task

– Recognition & complex synthesis of patterns within single/multiple dataset – Subjective and inconsistent

• Bond et al. (2007): seismic data interpretation accuracies (21%

for tectonic setting detection; 23% for fault detection)

OUR STUDY

• 1. Can algorithm-based data inspection be used as

‘first-pass’ analysis to minimise the impact of human interpretation inconsistencies?

Compare and analyse human and machine data inspections for porphyry detection task within magnetic data

• 2. Can we identify data display and interrogation

methods that are effective for human interpretation?

Examining human data interactions using quantitative monitoring of eye gaze and brain waves of interpreter

PORPHYRY DETECTION

• Porphyry anomalies within magnetic data have

specific shape characteristics

• Abundance, shape variations, noisy surroundings

etc

Clark et al. (2008) AMIRA P700 Final Report

1 km

Property of Barrick Gold

AUTOMATED METHOD

• 1. Circular peak detection using Radial

Symmetry Transform (Loy & Zelinsky, 2003)

• 2. Boundary enhancement using their

amplitude contrast to the surrounding

• 3. Boundary tracing to provide approximate

shape and size of the deposits

1 km

Property of Barrick Gold

Holden et al., 2011, Automatic identification of responses from porphyry intrusive systems within magnetic data using image analysis, Journal of Applied Geophysics, 74, pp.255-262

• Supported by Barrick Gold
• Automatically isolates magnetic anomalies that are likely to

be Cu-Au porphyry systems

SOFTWARE: CET Porphyry Analysis Extension (for Geosoft Oasis Montaj)

http://www.geosoft.com/products/software-extensions/cet-porphyry-detection Developed within CET, licensed and marketed by Geosoft

HUMAN INSPECTION

• Five interpreters were asked to search for

porphyry anomalies within a magnetic data (5 mins)

• Eye tracker recorded the eye gaze pattern;

Electroencephalography (EEG) profiled brain activities

• Interpreters responded with a button click when

spotting a target - the target locations identified using button click & eye gaze

• Advantage: Eye tracking and EEG responses can be

used for understanding of the interpretation process rather than just the outcome

EXPERIMENTS

• Target detection from original display for 5 mins
• Break (seismic interpretation, other EEG experiments)
• Target detection from ‘180 degrees’ rotated display for 5 mins

Property of Barrick Gold

HUMAN INSPECTION

0.30 0.40 0.50 0.60 0.70 0.80 0.90 1 2 3 4 5 Recall Subject Number Original Image Inverted Image Combined Responses

Recall = (No. of ground truth targets that are identified by the subject) / (No. of ground truth targets)

HUMAN INSPECTION at BEST (Human; Ground truth)

Recall = (No. of ground truth targets that are identified by the subject) / (No. of ground truth targets)

Total number of targets G/T 21 Human 25 G/T & Human 18 Recall Rate 86% (18/21) False ? Positives 7

MACHINE INSPECTION (Machine; Ground truth)

Total number of targets G/T 21 Machine 29 G/T & Human 16 Recall Rate 76% (16/21) False ? Positives 13 Human at Best Recall Rate: 86%

HUMAN vs MACHINE

(Human at best; machine; ground truth)

False ? Positives Human 7/25 Machine 13/29 Human & Machine 6 Human at Best Recall Rate: 86% Machine Recall Rate: 76%

DATA OBSERVATION PATTERNS

Better performers have shorter path lengths between targets - more systematic search pattern

IMAGE CHACTERISTICS

– Use of EEG target detection responses (ERP P300) to assess image characteristics that affect human target detection – Dispersion of visual attention over the image affects target detection

Target Non Target ERP P300

SUMMARY

Human inspection has large variability amongst interpreters, and often inconsistent within individuals

• 1. Can algorithm-based data inspection be used as ‘first-pass’

to minimise human interpretation inconsistency? Machine inspection can provide the result almost equivalent to human at best, but consistently

• 2. Can we identify effective data display and interrogation

methods for human interpretation? Methodical data search; viewing data in different angles Importance of scale in data display and enhancement assessed by localised visual attention

ACKNOWLEDGEMENTS

• Barrick Gold for their research support for the

porphyry detection project and the permission to use their data for the human data interaction study

• Contributors: Mike Dentith, Roberto Togneri,

Tele Tan, Greg Price, Jason Wong