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 square (IRLS) scheme has been implemented in the inversion algorithms. This scheme allows for objective re-weighting of data errors based on the ability for a given model discretization to predict individual datums. This, to better identify measurements which have been contaminated by local electromagnetic noise due to anthropogenic sources (mainly the power grid and railway). Due to the mathematical properties of the scheme, it allows for models which minimizes the L1 data error-norm instead of usual L2 minimization. This has yielded models whit sharper contrasts in resistivity and successfully emphasizes data believed to be reliable. Results indicate that the scheme was implemented successfully and the tradeoffs in data-fit are deemed acceptable.In addition, in the Kiruna study potential field data (magnetic total field and gravimetry) have been 3D modelled for the same area. These data sets have been inversion modelled in 3D using the MR3D-code developed at LTU with partners. Resulting 3D models have then been interpreted collectively both traditionally and with the use of machine learning methods. To guide interpretations more than 100 rock samples have been collected in the area and their petrophysical properties (density, magnetic susceptibility, electrical resistivity) have been measured at LTU. These petrophysical properties have been used to guide the machine learning methods for the 3D models by first using K-mean clustering on normalized petrophysical data and then using the resulting centroid vectors as input for a Gaussian mixture model of the similarly normalized 3D models. Resulting clusters show potential in being able to pick up sharp geological boundaries but expectedly is unable to fully capture geological structures one to one.