Robindra Chatterjee1, Dion Weatherley2, Geoff McLachlan3 and Rick Valenta1,2
1The University of Queensland, Sustainable Minerals Institute, W.H.Bryan Mining and Geology Research Centre, Brisbane, Australia, 2The University of Queensland, Sustainable Minerals Institute, Julius Kruttschnitt Mineral Research Centre, Brisbane, Australia, 3The University of Queensland, School of Mathematics and Physics, Brisbane, Australia
Automatic seismic interpretation of 3D volumes used in minerals exploration is an under researched topic. We are conducting a three-part investigation to extend the state-of-the-art in automatic fault interpretation methods based on the FaultSeg3D algorithm developed by Wu et al. (2020, 2019). This approach has demonstrated superior performance in automatic fault and horizon picking on: (i) synthetic seismic volumes created to emulate the geological complexity for petroleum targeting present in sedimentary terranes; and (ii) publically available field surveys that were part of a petroleum exploration program. Our hypothesis is that training FaultSeg3D on this style of data leads to poor prediction performance of fault locations in 3D-seismic datasets surveyed over hardrock terranes that typically exhibit greater geological complexity. This is referred to as a model generalisation issue, that occurs when the joint distribution of the data present in the synthetic volumes used for training a machine-learning-based, fault-prediction algorithm are significantly different from that present in the field survey used for prediction. A 3D seismic volume from an operating gold mine in Queensland was provided for this study courtesy of Evolution Mining Ltd. (ASX: EVN) along with the 3D geological model and drilling database to validate results.
This research has developed a flexible, synthetic-seismic-volume generator that will be used to create more representative training data for a machine-learning-based, fault-prediction algorithm. Geological folding, faulting, post-process-filtering and acquisition noise are modelled via successive image-processing convolutions. The first feature of the model is the ability to accommodate a user-specified degree of geological complexity by adjusting the level of geological folding and faulting present in the target field-survey’s terrane for any size cube. The second feature of the model is a more physically realistic way to model three major kinds of faults based on elastic dislocation theory. The third feature of the model is the addition of multifractal fault and vein population characteristics that further impart geological realism. In the next phase of research we will train a deep-learning algorithm on several hundred synthetic-seismic volumes, that emulate the geological complexity at the gold mine, to produce an automatic-fault-surface prediction tool that could aid the targeting of fault-constrained mineralisation.
Robindra is completing the second year of his PhD at The University of Queensland. His academic interests are in applied multivariate statistics, machine learning and 3D seismic.