摘要：The characterisation of natural fracture networks usingoutcrop analogues is important in understanding subsurface fluid flow androck mass characteristics in fractured lithologies. It is well known fromdecision sciences that subjective bias can significantly impact the way dataare gathered and interpreted, introducing scientific uncertainty. This studyinvestigates the scale and nature of subjective bias on fracture datacollected using four commonly applied approaches (linear scanlines, circularscanlines, topology sampling, and window sampling) both in the field and inworkshops using field photographs. We demonstrate that geologists' ownsubjective biases influence the data they collect, and, as a result,different participants collect different fracture data from the samescanline or sample area. As a result, the fracture statistics that arederived from field data can vary considerably for the same scanline,depending on which geologist collected the data. Additionally, the personalbias of geologists collecting the data affects the scanline size (minimumlength of linear scanlines, radius of circular scanlines, or area of a windowsample) needed to collect a statistically representative amount of data.Fracture statistics derived from field data are often input into geologicalmodels that are used for a range of applications, from understanding fluidflow to characterising rock strength. We suggest protocols to recognise,understand, and limit the effect of subjective bias on fracture data biasesduring data collection. Our work shows the capacity for cognitive biases tointroduce uncertainty into observation-based data and has implications wellbeyond the geosciences.
