Geology differentiation by applying unsupervised machine learning to multiple independent geophysical inversions

Effective quantitative methods for integrating multiple inverted physical property models are necessary to increase the value of information and advance interpretation further to produce interpretable geology models through geology differentiation. Geology differentiation is challenging in greenfield exploration areas where specific a priori geological information is scarce. The main problem is to identify geological units quantitatively with appropriate 3-D integration of these models. The integration of multiple sources of information has been conducted with different unsupervised machine learning methods (e.g. clustering), which can identify relationships in the data in the absence of training information. For this reason, we investigate the performance of five different clustering methods on the identification of the geological units using inverted susceptibility, density, and conductivity models that image a synthetic geological model. We show that the correlation-based clustering yields the best results for the geology differentiation among those investigated by identifying the correlation between physical properties diagnostic of each unit. The result of the differentiation is a quasi-geology model, which is a model that represents the geology with inferred geological units and their spatial distribution. The resulting integrated quasi-geology model demonstrates that individually inverted models with minimal constraints have sufficient information to jointly identify different geological units.