What do you think this is? “Conceptual uncertainty” in geoscience interpretation

Abstract

Interpretations of seismic images are used to analyze subsurface 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. 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. 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. These results illustrate that conceptual uncertainty exists, which in turn explains the large range of interpretations that can result from a single data set. We consider the role of prior knowledge in biasing individuals in their interpretation of the synthetic seismic section, and our 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.

The importance of characterizing uncertainty in controversial geoscience applications: induced seismicity associated with hydraulic fracturing for shale gas in northwest England

“…3, then the co-ordinates of the candidate seismogenic fault recognized on although with some doubt over its precise location), one might reasonably revert to the older data source. The non-uniqueness of many interpretations of seismic sections, especially those that consider data near the limit of resolution, is widely recognized (e.g., Bond et al, 2007aBond et al, ,b, 2012.…”

Geological Mapping Using Machine Learning Algorithms

“…As the volume and variety of data become increasingly available and useful, new obstacles arise, namely (1) manual interpretation cannot maintain the pace with the amount of incoming data and (2) manual photo interpretation is generally subjective and can be inconsistent among interpreters, especially with large datasets. This can be true for experts as well, as demonstrated in the Bond et al (2007) study of conceptual uncertainty. Machine learning algorithms (MLA) are a rapid and more objective approach to photo interpretation that automates feature classification for these datasets -a commonly used technique in image analysis.…”

Effect of operating dump truck vibrations on bench stability in deep open pit mines

“…Different interpretations can be influenced by interpreter experience and bias. This is illustrated by a study in which several hundred geologists were asked to interpret a 2D seismic section (Bond 2007). Eight distinct interpretations were possible, but only a handful of interpreters correctly interpreted.…”