integrating geophysical attributes with new cuckoo search machinelearning algorithm to estimate silver grade values–case study: zarshouran gold mine

The exploration methods are divided into the direct and indirect categories. among these,the indirect geophysical methods are more time- and cost-effective compared with thedirect methods. the target of the geophysical investigations is to obtain an accurate imagefrom the underground features. the induced polarization (ip) is one of the commonmethods used for metal sulfide ore detection. since metal ores are scattered in the hostrock in the zarshouran mine area, ip is considered as a major exploration method. parallelto ip, the resistivity data gathering and processing are done to get a more accurateinterpretation. in this work, we try to integrate the ip/rs geophysical attributes withborehole grade analyses and geological information using the cuckoo search machinelearningalgorithm in order to estimate the silver grade values. the results obtained showthat it is possible to estimate the grade values from the geophysical data accurately,especially in the areas without drilling data. this reduces the costs and time of theexploration and ore reserves estimation. comparing the results of the intelligent inversionwith the numerical methods, as the major tools to invert the geophysical data to the oremodel, demonstrate a superior correlation between the results.