Lachlan Grose

Lachlan Grose is a Research Fellow at Monash University's School of Earth Atmosphere and Environment, specializing in advanced geological and geophysical modeling. His work focuses on integrating machine learning, 3D structural analysis, and geophysical inversion techniques to address challenges in subsurface resource management and tectonic understanding.

Key projects include the Three-dimensional Bayesian Modelling of Geological and Geophysical data (ARC-funded) and development of the LoopStructural software, a time-aware 3D geological modeling platform. Grose also contributes to industry collaborations like the AMIRA P1249 project on ore body knowledge modeling.

Research interests span structural geology, igneous intrusion modeling, FAIR data principles, and geophysical inversion methods. He has led efforts to improve usability of Loop3D and pioneered machine learning applications for geological structure imaging.

• Leading developer of LoopStructural (open-source 3D modeling framework)
• Primary investigator on usability improvements for geoscientific workflows
• Recipient of ARC and industry grants totaling over AUD 3M
• Supervises PhD candidates focusing on interdisciplinary geoscience problems

Recent publications emphasize transdimensional inversion methods, FAIR data integration, and application of machine learning to geophysical datasets. His work bridges academic research with industrial subsurface resource challenges.