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

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• 作者：Eun-Jung Holden^a ; ^{eun-jung.holden@uwa.edu.au" class="auth_mail" title="E-mail the corresponding author} ; Jason C. Wong^a ; Daniel Wedge^a ; Michael Martis^a ; Mark Lindsay^a ; Klaus Gessner^b
• 关键词：Data analytics ; Magnetic data interpretation ; Geological structure detection ; Automated lineament detection
• 刊名：Computers & Geosciences
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：87
• 期：Complete
• 页码：101-111
• 全文大小：11061 K

文摘

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 rapid and objective analysis, selection and geological classification of the detected lineaments 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 region in northern Western Australia to demonstrate the use of automated analysis outputs to provide: quantitative measures of data evidence on interpreted structures, and graphical evaluation of interpretation quality.