Abstract
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.
Highlights
Natural fracture networks exert a strong control on the hydrogeological and mechanical properties of a rock mass and are useful indicators of paleo-stress directions
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
The variability in the trace length data depended on the scanline being sampled, more so than which participant was sampling, and could be as low as 0.15 (L1) or as high as 0.82 (L5, Workshop 1 (WS1))
Summary
Natural fracture networks exert a strong control on the hydrogeological and mechanical properties of a rock mass and are useful indicators of paleo-stress directions. There are four methods for characterising natural fractures in outcrops: linear scanlines (Priest, 1993; Priest and Hudson, 1981); circular scanlines (Mauldon et al, 2001; Rohrbaugh et al, 2002); topology sampling (characterising node types; Manzocchi, 2002; Sanderson and Nixon, 2015, 2018); and tracing out the fracture network (window sampling; Wu and Pollard, 1995) These methods handle orientation, censoring or truncation biases (Mauldon et al, 2001; Zeeb et al, 2013), and heterogeneity in the fracture network (Watkins et al, 2015) with different degrees of success. We characterise how much the degree of variability introduced by subjective uncertainties is dependent on the method of data collection
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