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

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The characterisation of natural fracture networks using outcrop analogues is important in understanding subsurface fluid flow and rock mass characteristics in fractured lithologies. It is well known from decision sciences that subjective bias can significantly impact the way data are gathered and interpreted, introducing scientific uncertainty. This study investigates the scale and nature of subjective bias on fracture data collected using four commonly applied approaches (linear scanlines, circular scanlines, topology sampling, and window sampling) both in the field and in workshops using field photographs. We demonstrate that geologists' own subjective biases influence the data they collect, and, as a result, different participants collect different fracture data from the same scanline or sample area. As a result, the fracture statistics that are derived from field data can vary considerably for the same scanline, depending on which geologist collected the data. Additionally, the personal bias of geologists collecting the data affects the scanline size (minimum length of linear scanlines, radius of circular scanlines, or area of a window sample) needed to collect a statistically representative amount of data. Fracture statistics derived from field data are often input into geological models that are used for a range of applications, from understanding fluid flow to characterising rock strength. We suggest protocols to recognise, understand, and limit the effect of subjective bias on fracture data biases during data collection. Our work shows the capacity for cognitive biases to introduce uncertainty into observation-based data and has implications well beyond the geosciences.

LanguageEnglish
Pages487-516
Number of pages30
JournalSolid Earth
Volume10
Issue number2
DOIs
Publication statusPublished - 11 Apr 2019

Fingerprint

Flow of fluids
fluid flow
Rocks
Statistics
Sampling
uncertainty
statistics
rocks
sampling
Lithology
outcrops
lithology
photographs
topology
Topology
analogs
fracture network
subsurface flow
radii
rock

Keywords

  • fractures
  • fieldwork
  • structural geology
  • perception
  • bias
  • uncertainty

Cite this

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abstract = "The characterisation of natural fracture networks using outcrop analogues is important in understanding subsurface fluid flow and rock mass characteristics in fractured lithologies. It is well known from decision sciences that subjective bias can significantly impact the way data are gathered and interpreted, introducing scientific uncertainty. This study investigates the scale and nature of subjective bias on fracture data collected using four commonly applied approaches (linear scanlines, circular scanlines, topology sampling, and window sampling) both in the field and in workshops using field photographs. We demonstrate that geologists' own subjective biases influence the data they collect, and, as a result, different participants collect different fracture data from the same scanline or sample area. As a result, the fracture statistics that are derived from field data can vary considerably for the same scanline, depending on which geologist collected the data. Additionally, the personal bias of geologists collecting the data affects the scanline size (minimum length of linear scanlines, radius of circular scanlines, or area of a window sample) needed to collect a statistically representative amount of data. Fracture statistics derived from field data are often input into geological models that are used for a range of applications, from understanding fluid flow to characterising rock strength. We suggest protocols to recognise, understand, and limit the effect of subjective bias on fracture data biases during data collection. Our work shows the capacity for cognitive biases to introduce uncertainty into observation-based data and has implications well beyond the geosciences.",
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How do we see fractures? Quantifying subjective bias in fracture data collection. / Andrews, Billy J.; Roberts, Jennifer J.; Shipton, Zoe K.; Bigi, Sabina; Tartarello, M. Chiara; Johnson, Gareth.

Vol. 10, No. 2, 11.04.2019, p. 487-516.

Research output: Contribution to journalArticle

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