integration of airborne geophysics data with fuzzy c-means unsupervised machine learning method to predict geological map, shahr-e-babak study area, southern iran

Fuzzy c-means (fcm) is an unsupervised machine learning algorithm. this method assists in integrating airborne geophysics data and extracting automatic geological map. this paper tries to combine airborne geophysics data consisting of aeromagnetic, potassium, and thorium layers to classify the lithological map of the shahr-e-babak area, a world-class porphyry area in the south of iran. the resulting clusters with fcm show appropriate coincidence with the geological map of the study area. the clusters are adapted with high magnetic anomalies corresponding to the mafic volcanic rocks and the clusters with high radiometric signature associated with igneous rocks. the cluster is associated with low magnetic anomaly and low radioelements concentration representing sedimentary rocks. some clusters are associated with two or more lithological formations due to similar signatures of geophysics properties. the fuzzy score membership in all clusters is above 0.71 indicating a high correlation between geological signatures and multigeophysical data. this study shows geophysical signatures analyzed with the machine learning method can reveal geological units.