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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. [ABSTRACT FROM AUTHOR]

When funding or conducting a seismic hazard assessment, facility owners and seismic hazard analysts need to estimate the possible added value that could be obtained by collecting additional data to inform the assessment. This added value needs to be balanced against the budget and time available for the collection of the data. In other words, they need to answer the question "Is it worth paying to obtain this information?". Conducting a Value of Information (VoI) analysis before any data collection would help to answer this question and to optimise the data collection process. In this article, we develop and illustrate a method to assess the VoI of improving estimates of the average shear-wave velocity in the top 30 m within the site-response component of a seismic hazard assessment to decide on the optimal seismic design for a reference building in Greece. The approach is based on decision trees to translate the causal relationships between the input parameters in site-response analysis and Bayesian inference to update the model when new data are considered. The results show that VoI is highly sensitive to prior probabilities and the accuracy of the data collection method (e.g. geophysical survey). This stresses the importance of defining prior probabilities based on available information as well as only considering data collection methods that are suitable for a project's needs and budget. • The Value of Information (VoI) improves decision-making in earthquake engineering. • The developed method can be used to prioritise and justify data collection. • Monte Carlo simulation and Bayesian updating is used for continuous variables in VoI. • Prior probabilities and likelihoods significantly impact the VoI. [ABSTRACT FROM AUTHOR]