Fault fictions: systematic biases in the conceptualization of fault-zone architecture

Author:

Shipton Z. K.1ORCID,Roberts J. J.1ORCID,Comrie E. L.2,Kremer Y.1ORCID,Lunn R. J.1,Caine J. S.3ORCID

Affiliation:

1. Department of Civil and Environmental Engineering, James Weir Building, 75 Montrose Street, Glasgow, G1 1XJ, UK

2. Department of Management Science, Strathclyde Business School, 199 Cathedral Street, Glasgow, G4 0QU, UK

3. US Geological Survey, Denver, CO 80225, USA

Abstract

AbstractMental models are a human's internal representation of the real world and have an important role in the way we understand and reason about uncertainties, explore potential options and make decisions. Mental models have not yet received much attention in geosciences, yet systematic biases can affect any geological investigation: from how the problem is conceived, through selection of appropriate hypotheses and data collection/processing methods, to the conceptualization and communication of results. We draw on findings from cognitive science and system dynamics, with knowledge and experiences of field geology, to consider the limitations and biases presented by mental models in geoscience, and their effect on predictions of the physical properties of faults in particular. We highlight biases specific to geological investigations and propose strategies for debiasing. Doing so will enhance how multiple data sources can be brought together, and minimize controllable geological uncertainty to develop more robust geological models. Critically, there is a need for standardized procedures that guard against biases, permitting data from multiple studies to be combined and communication of assumptions to be made. While we use faults to illustrate potential biases in mental models and the implications of these biases, our findings can be applied across the geosciences.

Publisher

Geological Society of London

Subject

Geology,Ocean Engineering,Water Science and Technology

Reference117 articles.

1. Expertise in context: personally constructed, socially selected, and reality-relevant?;International Journal of Expert Systems,1994

2. How do we see fractures? Quantifying subjective bias in fracture data collection;Solid Earth,2019

3. Atewologun D. , Cornish T. & Tresh F. 2018. Unconscious Bias Training. An assessment of the evidence for effectiveness. Equality and Human Rights Commission Research Report 113 .

4. Designing Risk Communications: Completing and Correcting Mental Models of Hazardous Processes, Part I

5. Sensitivity to landscape features: a spatial analysis of field geoscientists on the move;Journal of Geoscience Education,2016

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3