NSERC Industrial Research Chair in Advanced Computational Methods for Geophysical Electromagnetics Modeling, Inversion and Integration

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Electromagnetic methods are commonly used for geophysical explorations in applications such as mineral exploration, hydrocarbon detection; management of water, salt water and CO2, and reservoir monitoring.**Whereas, in the past, electromagnetic methods suffered from expensive data collection, we are now collecting massive amounts of data over space and time, using new systems and instrumentation, which allow for higher quality and accuracy of the data we collect, at much lower cost. These improvements in hardware and data collection systems bring new challenges that need to be met, if we are to use, interpret and understand the massive amounts of data generated. In particular, in this research we aim to study three main problems that arise from such data collection, for the mining industry.**The first problem is the modeling and inversion of large scale electromagnetic data. Such data sets are routinely collected from the air and contain millions of data points over a range of frequencies or times and scales in space. They tend to be of high resolution and cover a huge volume of the earth. Understanding these data has become a recent priority as a tool to map large-scale geological structures as well as smaller targets within those structures. This mapping is needed in order to find new mining targets, support population growth, as well as map reservoirs for shale gas and fracturing.**The second problem is the extraction of new information from commonly collected data and the design of new data collection systems. Given high quality airborne data, we will investigate the potential to extract chargeability, that is the leading property of some mineralized targets. Currently, estimating chargeability requires ground-based systems with electrodes, which hampers its use over large regions of land.**The third problem we propose to investigate is the integration of the geophysical and geological data in order to obtain prospectivity maps. Prospectivity maps predict the locations of mineralized zones and therefore can be used to reduce risk in mineral exploration.