The Blötberget mining area, the focus of this MSc project, is located about 230 km northwest ofStockholm and 12 km southwest of the city of Ludvika (central Sweden). The mining area has beenknown since 1600 for its various types of mineralization particularly iron-oxide deposits (magnetite andhematite) with the mining commenced in 1944. Previous geoscientific research in the area providesdetailed information about lithological variations and structure of the bedrock near the surface.However, knowledge of the depth extent of the mineral deposits and their host rocks is limited. To shedlights on these issues and support deep mineral exploration potential in the study area, within the recentlylaunched StartGeoDelineation project, new ground gravity data, 180 data points on average 150 m apart,were collected during two field campaigns in 2015 and 2016. Aeromagnetic data were obtained fromthe Geological Survey of Sweden (SGU) to complement the ground gravity measurement interpretationsand modelling. After a careful inspection of the field gravity data, they were reduced to completeBouguer anomaly with a maximum error estimate of about 0.6 mGal due to uncertainty in theinstrumental drift, slab density, geodetic surveying, diurnal variations and terrain (or topography)correction. The Bouguer gravity data after separation of regional field (second order polynomial at theend was used) were used (~ 8 mGal range) for interpretation and 3D inverse modelling. Clear anomalouszones are noticeable in the gravity data particularly due to mineralization and a major boundaryseparating a gravity low from gravity high in the southern part of the study area likely representing afault boundary separating two different lithological units. In my study, both forward and inversemodelling using rudimentary objects/shapes and voxel-type (mesh) approach were carried out. Effect ofinitial and reference models were tested on both gravity and magnetic datasets. While the constrainedmodels have still significant ambiguity, they help to suggest structural control on the location ofmineralization and may allow estimating an excess tonnage due to the presence of mineralization in thestudy area. Due to access limitations (e.g., unable to measure on the water-filled pit) the gravity modelis sensitive to the measuring positions and constraints using known shape of mineralization was not atthe end successful to overcome this. Collecting more gravity data on the target area and repeated test of3D inversion by adjusting the inversion parameters might help to improve the final result. / Gruvområdet Blötberget som denna MSc avhandling är fokuserat kring ligger 230 km från Stockholm,12 km från Ludvika, i Bergslagen. Mineralförekomster, framförallt järnmalm (magnetit och hematit)har varit kända i området sedan 1600-talet, och storskalig brytning inleddes år 1944. Tidigare geologiskaundersökningar i området har gett detaljerad information om fyndighetens ytnära litologi och struktur.Hur långt ner förekomsten och moderbergarten sträcker sig har dock varit okänt. Som del av detnystartade projektet StartGeoDelineation utfördes marknära gravimetrimätningar. Totalt 180 mätpunkter,med ett medelavstånd av 150 m, samlades in under två fältkampanjer under 2015 och 2016.Vid modellering komplementades gravimetridata med magnetometridata, insamlad under flygmätningarutförda av Sveriges geologiska undersökningar (SGU). Efter noggrann bearbetning av gravimetridatatogs den kompletta bougeranomalin fram. Det uppskattade felet är ca 0.6 mGal och är till följd avosäkerhet i korrigeringar för drift hos instrument, dygnsvariation, geodesi och topografi. Efter korrigeringav regional trend (uppskattad från 2:a ordningens pylonom, och med satt skala av 8 mGal somresultat) gjordes en 3D modell, via inversionsalgoritmer, samt en tolkning. Det står klart av framförallti gravimetridatan att det finns två avvikande zoner. Dessa indikerar mineraliseringen och en gräns i densödra delen av undersökningsområdet med gravimetridata i låg respektive höga värde. Detta återspeglartroligtvis också en förkastningszon mellan två lithologiska enheter. I denna studie har enkla geometriskaformer och voxlar (mesh) använts för bådadera forward modellering och inversionsalgoritmer. Deursprungliga och referensmodellerna testades på både dataset för gravitmetri och magnetometri. Trotsatt modellerna fortfarande visar tvetydiga resultat så kan de ändå användas för att ge förslag på struktureroch läge för mineraliseringen, och skall även kunna användas för att uppskatta tonnage. Det sistnämndakunde dock inte uppnås då punktäthet i mätdatan, till följd av att det numera vattenfyllda dagbrottet intekunde inkluderas i mätområdet, och att formen av mineraliseringen inte kunde avgränsar på etttillfredsställande sätt. För en förbättring av resultaten bör fler mätpunkter till gravimetridata samlas in iområdet så att 3D-modelleringen kan förbättras genom upprepade justeringar av inversionsparametrarna / StartGeoDelineation

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.