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

Aline Melo, Yaoguo Li
2021 Geophysical Journal International  
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 geologic information is scarce. The main problem is to identify geological units quantitatively with appropriate 3D integration of these models. The
more » ... tion 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 geologic units using inverted susceptibility, density, and conductivity models that image a synthetic geologic 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 geologic 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 geologic units.
doi:10.1093/gji/ggab316 fatcat:tcoahb6x5rehxkdsfk2zdacavu