New and innovative methods are required to find critical mineral deposits to transition from fossil fuels to renewable energy. Geophysical modelling and inversion has been crucial in finding new deposits over the last few decades, but success rates are declining as the easy to find deposits have been discovered and new deposits become are deeper below the surface. Machine learning may offer a new way to ingest and interpret geophysical and geological data, and improve exploration success rates. The synergy of geophysical modelling and machine learning has not yet been well explored, and thus far machine learning has predominantly been used in mineral exploration to identify patterns in disparate geophysical dataset that are not easy to observe otherwise. In this paper I examine a new approach to achieve better synergy between geophysical and machine learning modelling. The approach relies on generating an ensemble of geophysical inversion results by varying some of the subjective inversion parameters, such as damping and regularisation, and using logged drilling information as training label to predict future drilling success. I show the application of the method in an active exploration program in Western Australia, where ambient seismic noise surface wave tomography ensemble models were used parameters and laboratory zinc mineralisation assay results were used as labels. The method achieved an out-of-box accuracy of 97% and identified new drill targets which are currently being investigated. Although relatively little training data was available for this project, it shows promise as a new way to synergise geophysical and machine learning modelling.