Our team carefully assesses all the data available and weighs it against the prevailing conceptual geological model. Geophysical and geochemical data is used to improve initial geological models at multiple scales. These practices converge to create a model that geoscientists can trust, diminishing the risk in applications from targeting model generation to geotechnical hazard assessment.
The Mira Geoscience team has been busy using novel approaches to interpretation developed through our internal research. In 2021, we applied cutting-edge technology such as the use of discrete fracture networks in development of high-grade vein predictive models, deep-learning generative networks for targeting and geophysical feature analysis, data-driven structural interpretation using unsupervised learning, and automated geological map generation from multi-layer geophysical data. We are developing new methods of filtering gridded magnetics and gravity data to remove signal related to geological cover sequences. We have continued to develop and refine our unique workflow for applying AI to geohazard assessment, where our focus has been 4D integrated modelling of geology, rock mass characterization, stress, ground deformation, production, and infrastructure. We have recently been asked to provide insights into underlying patterns and to forecast hazard probabilities in several deep rockbursting mines internationally, including both conventional underground and cave mines. Have a look at our team’s profiles and the case studies that highlight their achievements.