How do we see fractures? Quantifying subjective bias in fracture data collection
How do we see fractures? Quantifying subjective bias in fracture data collection
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DOI：
10.5194/se-10-487-2019
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发表时间：
2019-04-11
期刊：
影响因子：
3.4
通讯作者：
Johnson, Gareth
中科院分区：
文献类型：
--
作者：
Andrews, Billy J.;Roberts, Jennifer J.;Johnson, Gareth
使用露头类似物表征天然裂缝网络对于理解裂缝岩性中的地下流体流动和岩体特征非常重要。决策科学众所周知，主观偏见会严重影响数据收集和解释的方式，从而引入科学不确定性。本研究调查了在现场和研讨会中使用现场照片使用四种常用方法（线性扫描线、圆形扫描线、拓扑采样和窗口采样）收集的断裂数据的主观偏差的规模和性质。我们证明地质学家自己的主观偏见会影响他们收集的数据，因此，不同的参与者从相同的扫描线或样本区域收集不同的裂缝数据。因此，对于同一扫描线，从现场数据得出的裂缝统计数据可能会有很大差异，具体取决于收集数据的地质学家。此外，收集数据的地质学家的个人偏见会影响收集具有统计代表性的数据量所需的扫描线大小（线性扫描线的最小长度、圆形扫描线的半径或窗口样本的面积）。从现场数据得出的裂缝统计数据通常被输入到地质模型中，这些模型用于从理解流体流动到表征岩石强度等一系列应用。我们建议制定协议来识别、理解和限制数据收集过程中主观偏差对骨折数据偏差的影响。我们的工作表明认知偏差能够将不确定性引入基于观测的数据，其影响远远超出了地球科学范围。
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.