| Summary: | The Northern Norrbotten ore district hosts a multitude of Sweden’s mineral deposits including world class deposits such as the Malmberget and Kirunavaara Iron oxide apatite deposits, the Aitik Iron oxide copper gold deposit, and a multitude of smaller deposits. Northern Norrbotten has been shaped by tectonothermal events related to the evolution of the Fennoscandian Shield and is a geologically complex environment. Without extensive rock outcropping and with most drilling localized to known deposits the regional to local scale of mineralization is not fully understood. To better understand the evolution and extent of the mineralization’s cross-disciplinary geosciences must be applied, where geophysical methods allow for interpretations of the deep and non-outcropping subsurface. Common earth modelling is a term describing a joint model derived from all available geoscientific data in an area, where geophysical models provide the framework.This study describes the geophysical modeling of two IOA deposits in Norrbotten, the Malmberget deposit in Gällivare and the Per-Geijer deposit in Kiruna. To better put these two deposits into a semi-regional setting magnetotelluric (MT) measurements have been conducted together with LKAB. LTU and LKAB have measured more than 200 MT stations in the two areas from 2016-2023. These measurements have then been robustly processed into magnetic transfer functions (impedances) for the broadband MT frequency spectrum (1000Hz,1000s). Then, all processed data judged to be of sufficient quality have been used for 3D inversion modelling using the ModEM code. The resulting conductivity/resistivity models reveals the local conductivity structure of the area, believed to be closely tied to the mineralization due to the conductive properties of the iron bearing minerals. Both areas yielded believable models which pinpointed known mineralization’s at surface as conductive anomalies and their connections to deeper regional anomalies.During modelling a robust iteratively re-weighted least ...
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