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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.

Soil gas measurements of different gas species with different geochemical behaviors were performed in the area of the Pecore Plain, a 200 m × 300 m sized, fault-bounded extensional basin located in the northern Mount Marzano massif, in the axial belt of the southern Apennine chain. The Pecore Plain area was affected by coseismic surface faulting during the M s = 6.9, 1980 Irpinia earthquake, the strongest and most destructive seismic event of the last 30 years in southern Italy. The collected data and their geostatistical analysis provide new insights into the control exerted by active fault segments on deep-seated gas migration toward the surface. The results define anomalies that are aligned with the NW-SE trending coseismic rupture of the 1980 earthquake along the western border of the plain, as well as along the southern border of the plain where a hidden, E-W striking fault is inferred. Geospatial analysis highlights an anisotropic spatial behavior of 222 Rn along the main NW-SE trend and of CO 2 along the E-W trend. This feature suggests a correlation between the shape and orientation of the anomalies and the barrier/conduit behavior of fault zones in the area. Furthermore, our results show that gas migration through brittle deformation zones occurs by advective processes, as suggested by the relatively high migration rate needed to obtain anomalies of short-lived 222 Rn in the soil pores. IntroductionThe Pecore Plain is a small, fault-bounded basin located in the northern Mount Marzano massif, in the axial belt of the southern Apennine chain, Italy (Figure 1). The area was affected by coseismic surface faulting related to the M s = 6.9, 23 November 1980 Irpinia earthquake, the strongest and most destructive (I 0 = X Mercalli-Cancani-Sieberg scale (MCS)) seismic event of the last 30 years in the southern Apennines [e.g., Pantosti and Valensise, 1990;Porfido et al., 2002;Serva et al., 2007;Locati et al., 2011;Rovida et al., 2011]. The earthquake struck the epicentral Mount Marzano area and the neighboring Irpinia region, which was affected by a large number of secondary geological effects (e.g., landslides, ground cracks, liquefaction, and variations in the discharge rate of major carbonate springs) [Porfido et al., 2002] and by more than 3000 coseismic ruptures within the region recording the largest intensity (I ≥ IX MCS) [Carmignani et al., 1981].The main coseismic fault trace of the 1980 earthquake consists of approximately 7 km long, up to 1 m high, NE facing fault scarp with an average NW-SE trend that crosses the Pecore Plain basin. This fault, which was associated with rupturing caused by the main earthquake, lowered the basin floor relative to its outlet. At present, surface drainage has not been recovered and the fault scarp still acts as a dam and occasionally causes ponding during the wet season [Pantosti and Valensise, 1990;Ascione et al., 2003, and references therein].As soil gas surveys conducted in the years after the 1980 earthquake highlighted large helium anomalies along the faul...

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.