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Solid Earth An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/se-2018-135
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-2018-135
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 08 Jan 2019

Research article | 08 Jan 2019

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Solid Earth (SE).

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

Billy J. Andrews1, Jennifer J. Roberts1, Zoe K. Shipton1, Sabina Bigi2, Maria C. Tartarello2, and Gareth O. Johnson1,3 Billy J. Andrews et al.
  • 1Department of Civil and Environmental Engineering, Univ ersity of Strathclyde, Glasgow, G11XJ, Scotland
  • 2Department of Earth Science, Sapienza – University of Rome, P.le Aldo Moro, 5, 00185 Rome, Italy
  • 3School of GeoSciences, University of Edinburgh, Edinburgh, EH93FE, Scotland

Abstract. The characterisation of natural fracture networks using outcrop analogues is important in understanding sub-surface fluid flow and rock mass characteristics in fractured lithologies. It is well known from decision-sciences that subjective bias significantly impacts the way data is gathered and interpreted. This study investigates the impact of subjective bias on fracture data collected using four commonly used approaches (linear scanlines, circular scanlines, topology sampling and window sampling) both in the field and in workshops using field photographs. Considerable variability is observed between each participant's interpretation of the same scanline, and this variability is seen regardless of geological experience. Geologists appear to be either focussing on the detail or focussing on gathering larger volumes of data, and this innate personality trait affects the recorded fracture network attributes. As a result, fracture statistics derived from the field data and which are often used as inputs for geological models, can vary considerably between different geologists collecting data from the same scanline. Additionally, the personal bias of geologists collecting the data affects the size (minimum length of linear scanlines, radius of circular scanlines or area of a window sample) required of the scanline that is needed to collect a statistically representative amount of data. We suggest protocols to recognise, understand and limit the effect of subjective bias on fracture data biases during data collection.

Billy J. Andrews et al.
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Short summary
Rocks often contain fracture networks, which can strongly impact sub-surface fluid flow and the strength of a rock mass. We examine how participants interpret the same fracture network, both in the field and using field photographs. Considerable variability between participant's resulted 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.
Rocks often contain fracture networks, which can strongly impact sub-surface fluid flow and the...
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