An exercise to document "conceptual uncertainty" in the interpretation of seismic images is deserving of wider discussion. Few readers of this blog will know anything about seismic sections, but the reported research addresses principles that are relevant to us all.
Seismic data can be used to provide 3D models of rocks and geological structures below the ground. Seismic imaging is a standard tool of petroleum geologists, but many other geoscientists find the technique invaluable. There is a major constraint: "All geological data sets are spatially limited and have limited resolution. Geoscientists who interpret such data sets must, therefore, rely upon their previous experience and apply a limited set of geological concepts." The researchers set out to understand more about the human factors involved in the process of interpretation. Geologically-alert readers can consult the original paper to read about the findings and their significance. We shall focus on more generic issues raised in the discussion section.
Three main sources of bias were identified in the participants (all of whom were geoscientists with some expertise with seismic sections, but with varying lengths of experience). It must be emphasised that "bias" has no connotations of unprofessional behaviour or unethical practice. It relates to identifiable influences on human judgment. The three categories are as follows.
1. Availability bias. This refers to the recent experiences of subjects, who tend to employ models that have contemporary application in their own thinking.
2. Anchoring bias. Subjects were reluctant to move away from an initial framing of the problem where the views of experts provided contextualisation and reassurance.
3. Confirmation bias. This "involves actively seeking out opinions and facts that support one's own beliefs or hypotheses" (that is, the reverse of pursuing falsification).
These three types of bias were not at all rare. "Examples of bias based on dominant tectonic setting expertise can be found at all levels of experience. Individual participants with 15+ years experience anecdotally show evidence of availability and anchoring bias in the same way students do." "Many" participants were considered to exhibit confirmation bias.
Why is this relevant? It is because these geoscientists are representative of the scientific community as a whole, wherever human judgment/interpretation is involved in their work. Many of the issues aired in this blog relate to popular biological models (genetic reductionism, common ancestry, Darwinism, etc) that involve the interpretation of data and which can be favoured because of availability bias. The influence of opinion formers often goes far beyond the content of their words. This is anchoring bias. And the tendency to seek out support for one's personal views (rather than challenge them) is widespread (perhaps exemplified in the way Darwinists indulge in imaginative story-telling). This is confirmation bias.
There's plenty of "conceptual uncertainty" in all matters relating to origins. It would be healthy if some researchers could do for the biological community what the seismic modeling team has done for geoscience.
What do you think this is? "Conceptual uncertainty" in geoscience interpretation
C.E. Bond, A.D. Gibbs, Z.K. Shipton, S. Jones
GSA Today, 17(11), (November 2007), 4-10 . DOI: 10.1130/GSAT01711A.1
Interpretations of seismic images are used to analyze sub-surface geology and form the basis for many exploration and extraction decisions, but the uncertainty that arises from human bias in seismic data interpretation has not previously been quantified. [. . .] We have documented the range of interpretations to a single data set, and in doing so have quantified the "conceptual uncertainty" inherent in seismic interpretation. In this experiment, 412 interpretations of a synthetic seismic image were analyzed. Only 21% of the participants interpreted the "correct" tectonic setting of the original model, and only 23% highlighted the three main fault strands in the image. [. . .] [O]ur results demonstrate that conceptual uncertainty has a critical influence on resource exploration and other areas of geoscience. Practices should be developed to minimize the effects of conceptual uncertainty, and it should be accounted for in risk analysis.
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