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

Sivarajah, Yathunanthan; Holden, Eun‐Jung; Togneri, Roberto; Dentith, Mike; Lindsay, Mark

doi:10.1190/int-2013-0199.1

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…Using visual saliency, we can accomplish this task and make interpreters job relatively easy. Sivarajah et al [15] studied var- ious saliency detection algorithms and observed which one closely mimics the interpreters visual attention when interpreting the gravity and magnetic data for exploration applications. The study concludes that saliency maps can be used to develop new techniques to compensate or augment biases and guide the interpreter's attention to important areas in images.…”

Section: Introductionmentioning

confidence: 99%

Saliency detection for seismic applications using multi-dimensional spectral projections and directional comparisons

Shafiq

Long

Alshawi

et al. 2017

2017 IEEE International Conference on Image Processing (ICIP)

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In this paper, we propose a novel approach for saliency detection for seismic applications using 3D-FFT local spectra and multi-dimensional plane projections. We develop a projection scheme by dividing a 3D-FFT local spectrum of a data volume into three distinct components, each depicting changes along a different dimension of the data. The saliency detection results obtained using each projected component are then combined to yield a saliency map. To accommodate the directional nature of seismic data, in this work, we modify the center-surround model, proven to be biologically plausible for visual attention, to incorporate directional comparisons around each voxel in a 3D volume. Experimental results on real seismic dataset from the F3 block in Netherlands offshore in the North Sea prove that the proposed algorithm is effective, efficient, and scalable. Furthermore, a subjective comparison of the results shows that it outperforms the stateof-the-art methods for saliency detection.

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Section: Introductionmentioning

confidence: 99%

Saliency detection for seismic applications using multi-dimensional spectral projections and directional comparisons

Shafiq

Long

Alshawi

et al. 2017

2017 IEEE International Conference on Image Processing (ICIP)

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Improving assessment of geological structure interpretation of magnetic data: An advanced data analytics approach

Holden

Wong

Wedge

et al. 2016

Computers & Geosciences

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Geological structures are recognisable as discontinuities within magnetic geophysical surveys, typically as linear features. However, their interpretation is a challenging task in a dataset with abundant complex geophysical signatures representing subsurface geology, leading to significant variations in interpretation outcomes amongst, and within, individual interpreters. Previously, numerous computational methods were developed to enhance and delineate lineaments as indicators for geological structures. While these methods provide 18 rapid and objective analysis, selection and geological classification of the detected lineaments 19 for structure mapping is in the hands of interpreters through a time consuming process. This paper presents new ways of assisting magnetic data interpretation, with a specific aim to improve the confidence of structural interpretation through feature evidence provided by automated lineament detection. The proposed methods produce quantitative measures of feature evidence on interpreted structures and interactive visualisation to quickly assess and modify structural mapping. Automated lineament detection algorithms find the feature strengths of ridges, valleys and edges within data by analysing their local frequencies. Ridges and valleys are positive and negative line-like features detected by the phase symmetry algorithm which finds locations where local frequency components are at their extremum, the most symmetric point in their cycle. Edge features are detected by the phase congruency algorithm which finds locations where local frequency components are in phase. Their outputs are used as feature evidence through interactive visualisation to drive data evidenced interpretation. Our experiment uses magnetic data and structural interpretation from the west Kimberley 33 region in northern Western Australia to demonstrate the use of automated analysis outputs to quantitatively measure the data evidence on interpreted structures and graphically evaluate the quality of interpretation.

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A novel attention model for salient structure detection in seismic volumes

Shafiq¹,

Long²,

Di³

et al. 2021

ACI

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<abstract><p>A new approach to seismic interpretation is proposed to leverage visual perception and human visual system modeling. Specifically, a saliency detection algorithm based on a novel attention model is proposed for identifying subsurface structures within seismic data volumes. The algorithm employs 3D-FFT and a multi-dimensional spectral projection, which decomposes local spectra into three distinct components, each depicting variations along different dimensions of the data. Subsequently, a novel directional center-surround attention model is proposed to incorporate directional comparisons around each voxel for saliency detection within each projected dimension. Next, the resulting saliency maps along each dimension are combined adaptively to yield a consolidated saliency map, which highlights various structures characterized by subtle variations and relative motion with respect to their neighboring sections. A priori information about the seismic data can be either embedded into the proposed attention model in the directional comparisons, or incorporated into the algorithm by specifying a template when combining saliency maps adaptively. Experimental results on two real seismic datasets from the North Sea, Netherlands and Great South Basin, New Zealand demonstrate the effectiveness of the proposed algorithm for detecting salient seismic structures of different natures and appearances in one shot, which differs significantly from traditional seismic interpretation algorithms. The results further demonstrate that the proposed method outperforms comparable state-of-the-art saliency detection algorithms for natural images and videos, which are inadequate for seismic imaging data.</p></abstract>

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

Cited by 3 publications

References 31 publications

Saliency detection for seismic applications using multi-dimensional spectral projections and directional comparisons

Saliency detection for seismic applications using multi-dimensional spectral projections and directional comparisons

Improving assessment of geological structure interpretation of magnetic data: An advanced data analytics approach

A novel attention model for salient structure detection in seismic volumes

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