How do we see fractures? Quantifying subjective bias in fracture data collection
<p>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 gather...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-04-01
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| Series: | Solid Earth |
| Online Access: | https://www.solid-earth.net/10/487/2019/se-10-487-2019.pdf |
| Summary: | <p>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.</p> |
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| ISSN: | 1869-9510 1869-9529 |