Lindsay, Mark1, Richard, Stephen2, Brodaric, Boyan3, Giraud, Jeremie1, de Kemp, Eric3, Jessell, Mark1
1Centre for Exploration Targeting, School of Earth Sciences, The University of Western Australia, Perth, Australia; 2US Geoscience Information Network, Tuscon, USA; 3Geological Survey of Canada, Natural Resources Canada, Ottawa, Canada
The Loop platform aims to provide geoscientists with an open-source solution to probabilistic analyses of 3D geoscientific models. The process of 3D modelling in the geosciences involves the collection, aggregation and integration of usually disparate data sources into a structured knowledge-base. Supporting this crucial endeavour within the Loop platform is the Geoscience Knowledge Manager (GKM), an ontology-based resource that incorporates knowledge of geological units, structures, properties, events and their relationships. Ontologies are typically known for pulling data into a ‘machine-readable’ structure, and are a foundation for the sematic web and associated AI and computer-science applications (Google, Amazon, Microsoft etc.). The geosciences are also familiar with ontologies, with many proponents seeing benefits from a successful implementation of: (1) a shared vocabulary; (2) increased interoperability and (3) automated reasoning. GeoSciML, perhaps the most well-known example of a geoscience data structure, provides a standardized model for geological concepts and an associated format for exchanging data between geoscience databases. In a similar manner, the Loop GKM and its GeoScience ontology (GSO) aim to enhance 3D modelling practices by providing support via the three advantages listed above, but specific to the procedures and expectations of the 3D modeller. To begin testing the GKM for geophysical modelling purposes, we to perform the simple task of assigning plausible petrophysical values and their errors to a 3D model in order to obtain a geophysical forward model. This is achieved by structuring the petrophysical and stratigraphic knowledge using the GSO, and storing it in GKM semantic database for retrieval by 3D modelling tools. This task is by no means difficult, but the use of GKM is superior to the traditional procedure which typically involves obtaining the necessary data from disconnected databases, published papers, archived data stores and when not available, from generic ‘global’ examples. Thus, repeatability is not always ensured. Using an ontology-based GKM can highlight subtle but important relationships between geological entities that might be missed in a manual process to improve accuracy and repeatability.. For example, rock properties beyond lithology that commonly affect petrophysical properties, such as porosity, schistosity and alteration, can be encoded to a rock unit under study and considered when deriving values from generic examples. The KM goes beyond just petrophysics and lithology, with temporal, spatial and theoretical (laws, theories, rules) knowledge, observations and interpreted data combined with 3D models being encoded. An ongoing and consistent record of data use and knowledge gain also provides a valuable resource for decision accountability, knowledge-transfer and uncertainty reduction.
We acknowledge the support of the MinEx CRC and the Loop: Enabling Stochastic 3D Geological Modelling (LP170100985) consortia. The work has been supported by the Mineral Exploration Cooperative Research Centre whose activities are funded by the Australian Government’s Cooperative Research Centre Programme. This is MinEx CRC Document 2020/xx.
Mark is a geoscientist and Senior Research Fellow at the University of Western Australia, specialising in integrated geoscientific and 3D modelling and understanding the value of geoscientific information. He also has research interests that include investigating complex mineral systems and their representations.