GAZE-INDUCED QUALITY CONTROL IN GEOLOGICAL VOXEL MODELS

Abstract

Knowledge of the subsurface is an important aspect for a countries welfare. To gain an understanding of this, geologists use boreholes com- bined with interpolating methods to build statistical 3D models. Due to the stochastic properties of these models, domain experts perform a quality control procedure to find errors, which can be a time consuming endeavor. In this thesis, we looked into methods for predicting areas of errors in GeoTOP, a geological voxel model. Firstly, we show that a previously used Attention Model performs well when we optimize parameters for each participant, but with an AUC of 0.61, the algorithm lacked finding optimal parameters for the combined participants. We show that variance among experts in assessing errors is high, making generalizing predictions hard. Secondly, we showed that entropy, the voxel models quantification of uncertainty, is not a good indicator of where errors occur. With an average AUC of 0.54, where some participants scored even under 0.5, we show that there is no relation between entropy and the assessment of experts. Finally, we introduced a Velocity-Threshold Identification (I-VT) algorithm combined with tree-based classifiers and showed that with an AUC of 0.8 over each participant, errors can be found regardless of the differences among participants. We show why finding optimal parameters for fixation algorithms is difficult due to a lack of ground truth, but despite that our new algorithm performs better and faster, allowing for real-time error predictions. These findings suggest that a geologist combined with our introduced algorithm can decrease their time spent on quality control. Furthermore, this thesis can provide as a framework for other fields with a similar problem description, such as radiologists looking for malignancies.