Automatic identification of responses from porphyry intrusive systems within magnetic data using image analysis

Holden, Eun‐Jung; Fu, Shih Ching; Kovesi, Peter; Dentith, Mike; Bourne, Barry; Hope, Matthew

doi:10.1016/j.jappgeo.2011.06.016

Cited by 52 publications

(16 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A model footprint for these systems appears as an elevated sub-circular feature with surrounding annular lows (Holden et al 2011). In this exercise, we identified a significant between-interpreter variation in target detection rate; and a correlation between a systematic data observation involving a sequential searching of neighbouring…”

Section: Introductionmentioning

confidence: 72%

“…To quantitatively measure the target spotting accuracy, we required a ground-truthed data set. We used the known porphyry deposits in the region that contains magnetic footprints that are similar to the model described in (Holden et al 2011) (Figure 2). Note that a couple of known deposits did not show a conventional magnetic signature.…”

Section: Exercise 1 -Porphyry Detection On Magnetic Imagementioning

confidence: 99%

See 1 more Smart Citation

Analysing Variability in Geophysical Data Interpretation by Monitoring Eye Gaze Movement

Sivarajah

Holden

Togneri

et al. 2012

ASEG Extended Abstracts

Self Cite

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 72%

Section: Exercise 1 -Porphyry Detection On Magnetic Imagementioning

confidence: 99%

Analysing Variability in Geophysical Data Interpretation by Monitoring Eye Gaze Movement

Sivarajah

Holden

Togneri

et al. 2012

ASEG Extended Abstracts

Self Cite

View full text Add to dashboard Cite

“…Within magnetic data, a typical footprint for these mineral systems appears as an elevated subcircular feature with surrounding annular lows [6] (see Fig. 1).…”

Section: A Datamentioning

confidence: 96%

Studying the Impact of Feature Saliency for Geoscientific Target Detection Using Electroencephalography (EEG)

Sivarajah

Holden

Togneri

et al. 2012

2012 International Conference on Digital Image Computing Techniques and Applications (DICTA)

Self Cite

View full text Add to dashboard Cite

Accuracy and efficiency in geoscientific data interpretation is critically important for the resource industry, as based on these interpretations, significant financial decisions are made for exploration and extraction of resources. This study aims to understand image characteristics that impact interpreters' ability to detect geological targets within magnetic geophysics images. We use the Brain Computer Interface (BCI) approach to profile interpreter's neurological responses to geophysical target images using electroencephalography (EEG). These results are compared with image saliency analysis outputs, specifically the spatial distribution of visual attention peaks. Our experimental results showed that images with a wider dispersion of saliency lead to a lower rate of target detection based on EEG activities of interpreters.

show abstract

“…The interpreter visual attention maps were captured through an experiment requiring participants to recognize targets that have characteristics suggestive of gold-copper-rich porphyry systems. The relevant magnetic anomalies have a distinctive "Mexican-hat"-like character comprising subcircular magnetic highs with surrounding annular lows (Holden et al, 2011;Hoschke, 2011) as shown in Figure 5a-1. All the interpreters who participated in this study were trained geophysicists or geologists with experience in magnetic data interpretation and had normal or corrected-to-normal vision (i.e., using contact lenses).…”

Section: Interpreter Visual Attention Versus Image Saliencymentioning

confidence: 99%

Visual saliency and potential field data enhancements: Where is your attention drawn?

et al. 2014

Self Cite

View full text Add to dashboard Cite

Interpretation of gravity and magnetic data for exploration applications may be based on pattern recognition in which geophysical signatures of geologic features associated with localized characteristics are sought within data. A crucial control on what comprises noticeable and comparable characteristics in a data set is how images displaying those data are enhanced. Interpreters are provided with various image enhancement and display tools to assist their interpretation, although the effectiveness of these tools to improve geologic feature detection is difficult to measure. We addressed this challenge by analyzing how image enhancement methods impact the interpreter’s visual attention when interpreting the data because features that are more salient to the human visual system are more likely to be noticed. We used geologic target-spotting exercises within images generated from magnetic data to assess commonly used magnetic data visualization methods for their visual saliency. Our aim was achieved in two stages. In the first stage, we identified a suitable saliency detection algorithm that can computationally predict visual attention of magnetic data interpreters. The computer vision community has developed various image saliency detection algorithms, and we assessed which algorithm best matches the interpreter’s data observation patterns for magnetic target-spotting exercises. In the second stage, we applied this saliency detection algorithm to understand potential visual biases for commonly used magnetic data enhancement methods. We developed a guide to choosing image enhancement methods, based on saliency maps that minimize unintended visual biases in magnetic data interpretation, and some recommendations for identifying exploration targets in different types of magnetic data.

show abstract

Automatic identification of responses from porphyry intrusive systems within magnetic data using image analysis

Cited by 52 publications

References 28 publications

Analysing Variability in Geophysical Data Interpretation by Monitoring Eye Gaze Movement

Analysing Variability in Geophysical Data Interpretation by Monitoring Eye Gaze Movement

Studying the Impact of Feature Saliency for Geoscientific Target Detection Using Electroencephalography (EEG)

Visual saliency and potential field data enhancements: Where is your attention drawn?

Contact Info

Product

Resources

About