Articles | Volume 10, issue 2
https://doi.org/10.5194/se-10-487-2019
https://doi.org/10.5194/se-10-487-2019
Research article
 | 
11 Apr 2019
Research article |  | 11 Apr 2019

How do we see fractures? Quantifying subjective bias in fracture data collection

Billy J. Andrews, Jennifer J. Roberts, Zoe K. Shipton, Sabina Bigi, M. Chiara Tartarello, and Gareth Johnson

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Cited articles

Aggarwal, I. and Woolley, A. W.: Do you see what I see? The effect of members' cognitive styles on team processes and errors in task execution, Organ. Behav. Hum. Dec., 122, 92–99, https://doi.org/10.1016/j.obhdp.2013.04.003, 2013. 
Agosta, F., Alessandroni, M., Antonellini, M., Tondi, E., and Giorgioni, M.: From fractures to flow: A field-based quantitative analysis of an outcropping carbonate reservoir, Tectonophysics, 490, 197–213, https://doi.org/10.1016/j.tecto.2010.05.005, 2010. 
Andrews, B. J., Roberts, J. J., Shipton, Z. K., Bigi, S., Tartarello, M. C., and Johnson, G.: Supplementary information, https://doi.org/10.15129/d3b26853-7236-4066-846f-7a6abb8d91bf, 2019. 
Armstrong, S. J., Cools, E., and Sadler-Smith, E.: Role of Cognitive Styles in Business and Management: Reviewing 40 Years of Research, Int. J. Manag. Rev., 14, 238–262, https://doi.org/10.1111/j.1468-2370.2011.00315.x, 2012. 
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Short summary
Rocks often contain fracture networks, which can strongly affect subsurface fluid flow and the strength of a rock mass. Through fieldwork and workshops we show that people report a different number of fractures from the same sample area of a fracture network. This variability results in significant differences in derived fracture statistics, which are often used as inputs for geological models. We suggest protocols to recognise, understand, and limit this effect on fracture data collection.
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