Interpretation of Electrical Resistivity scans with the assistance of Machine Learning

The proposed research aims to use Machine Learning Methods to interpret data obtained during the Electrical Resistivity scans in the delineation of Fracking Sand deposits in Western Canada. Traditional exploration for sand deposits involves pricey and not always efficient auger and sonic drilling on the entire investigated Property. Currently, costs associated with those operations are the reason for importing the proppant sand from the USA rather than using our Canadian resources. The Electrical Resistivity Topography is a much more affordable field operation that can determine the near-surface lithology and location of valuable sand deposits by establishing the material’s resistivity distribution. Results obtained from ERT, combined with machine learning modelling and its predicting capabilities, would be an innovative approach for a quicker and more accessible exploration which will directly benefit the partner organization. The intern through his participation in this project will learn how to conduct mineral exploration using both the traditional exploration drilling techniques and geophysical methods. The work for the industrial partner will allow the intern to gain Canadian experience in the mineral exploration in Canada which will help him in finding employment un Canada.

Faculty Supervisor:

Derek Apel

Student:

Partner:

TerraShift Engineering

Discipline:

Engineering

Sector:

Mining

University:

University of Alberta

Program: