(Skarn)-Type Ore Deposits in Western Bergslagen,Sweden

Home , Cerite

UNIVERSITY OF GOTHENBURG Department of Earth Sciences Geovetarcentrum/Earth Science Centre

PREDICTIVE PROSPECTIVITY

MAPPING OF RARE EARTH

ELEMENTS IN BASTNÄS

(SKARN)-TYPE ORE DEPOSITS IN

WESTERN BERGSLAGEN,SWEDEN

INTEGRATION AND ANALYSIS OF

GEOSCIENCE DATASETS IN ARCGIS

Ejiro Obotuke-Agbonifo

ISSN 1400-3821 B961 Master of Science (120 credits) thesis Göteborg 2017

Mailing address Address Telephone Telefax Geovetarcentrum Geovetarcentrum Geovetarcentrum 031-786 19 56 031-786 19 86 Göteborg University S 405 30 Göteborg Guldhedsgatan 5A S-405 30 Göteborg SWEDEN Table of contents 1. INTRODUCTION ...... 4 1.1 AIM AND APPROACH ...... 4 1.2 ABOUT RARE EARTH ELEMENTS ...... 7 1.3 GENERAL CHARACTERISTICS OF BASTNÄS-TYPE REE MINERALIZATION ...... 9 1.4 MINING HISTORY OF BERGSLAGEN ...... 13 2. BACKGROUND ...... 15 2.1 REGIONAL GEOLOGY OF BERGSLAGEN...... 15 2.2 LOCAL GEOLOGY OF BERGSLAGEN ...... 17 2.3 BEDROCK OF STUDY AREA ...... 20 2.4 STRUCTURAL GEOLOGY OF STUDY AREA ...... 23 2.5 METAMORPHISM OF STUDY AREA...... 25 2.6 ALTERATION IN STUDY AREA ...... 26 3. MATERIALS AND METHODS ...... 29 3.1 PRINCIPLES OF PREDICTIVE MODELING ...... 29 3.1.1 MECHANISTIC APPROACH ...... 30 3.1.2 EMPIRICAL APPROACH ...... 30 3.2 PREDICTIVE PROSPECTIVITY MODELING WITH ARCGIS ...... 31 3.3 PROCEDURE FOR MINERAL EXPLORATION PREDICTIVE MODELING ...... 32 3.4 GENERALIZED CRITERIA FOR POTENTIAL EXPLORATION ...... 33 3.4.1 EXPLORATION DATA USED ...... 35 3.5 MINERAL DEPOSITS OF REE IN STUDY AREA ...... 36 4. CRITERIA FOR REE MINERALIZATION EXPLORATION IN BERGSLAGEN ...... 39 4.1 GENERAL CLASSIFICATION OF DEPOSITS IN BERGSLAGEN ...... 39 4.2 CONCEPTUAL MODEL FOR BASTNÄS-TYPE DEPOSITS ...... 39 Lithology ...... 39 Mineralization ...... 39 Geophysics ...... 39 Geochemistry ...... 39 Alteration ...... 40 Heat source ...... 40 5. SPATIAL EXPLORATION FACTORS ...... 41

5.1 HOST ROCK FACTOR ...... 41 5.2 HEAT SOURCE FACTOR ...... 43 5.3 GEOCHEMICAL FACTORS ...... 44 5.3.1 Biogeochemistry ...... 49 5.3.2 Lithogeochemistry ...... 53 5.4 STRUCTURAL FACTOR...... 56 5.5 GEOPHYSICAL FACTOR...... 57 5.6 ALTERATION FACTOR ...... 59 6. PROSPECTIVITY MODELING ...... 67 6.1. SPATIAL ASSOCIATION ANALYSIS ...... 70 6.1.1 SPATIAL ASSOCIATION WITH HOST ROCK ...... 70 6.1.2 SPATIAL ASSOCIATION WITH HEAT SOURCE ...... 72 6.1.3 SPATIAL ASSOCIATION WITH GEOCHEMICAL ANOMALIES...... 73 6.1.4 SPATIAL ASSOCIATION WITH GEOPHYSICAL FACTOR ...... 78 6.1.5 SPATIAL ASSOCIATION WITH STRUCTURAL FACTOR ...... 80 7. DISCUSSION AND VALIDATION ...... 81 7.1 CONDITIONAL INDEPENCE TEST ...... 82 7.2 WEIGHTED OVERLAY MINERAL POTENTIAL MAP ...... 83 7.3 CONCLUSION ...... 85 Acknowledgements ...... 89 REFERENCES ...... 90

1. INTRODUCTION 1.1 AIM AND APPROACH Rare earths minerals are not minerals that have usually attracted attention but recent events have put the spotlight on them as the rare-earth elements (REEs) has, now become a more sought–after natural resource and for that reason, it has earned more attention from several scientists especially mineralogists, geologist and exploration districts around the world (Chakhmouradian & Wall, 2012). Modern societies, are now anxious to obtain this resource needed for many technological developments. The REEs which have grown to become economically significant reserves are known to be constituents in Irons as well as other metals such as flour-apatite, phosphate and REE-silicates (Jonsson et al., 2013). As documented by several researchers, Sweden stands as a major producer of iron in Europe and the apatite-iron- oxide ores of the Grängesberg Mining District (GMD) is one of the largest iron ore accumulations in Bergslagen along-side iron oxide deposits of banded-iron and skarn-iron ores which also occur in the Bergslagen province (Jiao J, 2011). Though Sweden has been known to be the "home of the rare-earth elements", following the fact that several of REEs were first discovered here during the late 18th and early 19th centuries (www.eurare.se; Jansson, 2011, p. 1), it has not ranked as a major producer of this rare metals in most recent times globally (Klinger, 2015). However, seeing that the REE metals are becoming most recently a highly sought-after resource in the global market today, it has become a need to look again into prospects of the REE mineralization here in Sweden.

There have been various researches which have shown occurrences of REE mineralization in restricted number of deposits within Sweden as shown in figure 1 and in other to increase the percentage of REE production, there is need to know favourable areas for REE mineralization with consideration of mineral system and available geological, geochemical and geophysical data. This project therefore, seeks to evaluate the prospectivity potential for the REEs in the Bastnäs- type also known as the skarn-iron ores in the Western Bergslagen province of Sweden. It proposes to carry out a predictive mapping using ArcGIS which would be used to clearly distinguish favourable areas for further exploration of REE-bearing ores in Bastnäs type-deposits within the demarcated study area.

The prospectivity map will be based on geoscience data which have been made available by the Geological Survey of Sweden (SGU) and comprises of geological, geochemical, structural and geophysical data of the study area. In this project, the prospectivity modelling will be done in the western part of Bergslagen on regional scale. Analysis will be based on both a knowledge-driven method which comprises mostly on reviews and comparison from past literatures, and the data- driven method which is focused on a more quantitative approach.

Figure 1: Showing occurrences of REE mineralization types in Sweden (Source: Sadeghi et al. 2013 and http://ww.eurar.eu/countries/sweden.html).

As the study, will focus on REE mineralization for the skarn iron-oxide ores within the north- western part of Bergslagen, with most attention on Norberg and Riddarhyttan where there is the REE line, regions such as Grängesberg, Blötberget and Idkerberget are also recognized for their mining history in iron-oxide ores generally. Figure 2 clearly highlights these locations, the rock types and corresponding geological ages as well as where the study area stands geographically

5 in Sweden. Finally, it would be important to note that the coordinate system which is the SWEREF99_Transverse Mercator and the coordinates of the study area represented in this map is applicable to all subsequent maps generated for this thesis.

Figure 2. Map showing location of interest for this study, the corresponding bedrock, coordinates and where the study area stands geographically in the map of Sweden. All five locations are situated in north-western Bergslagen (From database provided by SGU for this thesis).

1.2 ABOUT RARE EARTH ELEMENTS The first rare earth mineral “Ytterbite” later re-named and now known as “Gadolinite”, was discovered by Lieutenant Carl Axel Arrhenius in 1787 at a quarry in the village of Ytterby here in Sweden (Moeller, 1973, pp. 39). The rare-earth elements also known as rare earth minerals or metals, constitutes a group of fifteen (15) elements in the periodic table known as the lanthanide series (La to Lu; Fig. 3), plus yttrium (Y) and most times scandium (Sc), which have been added to the group due to their chemical similarities (Fig 3). These elements (lanthanides also known as lanthanoid, yttrium and scandium), are what make-up the seventeen (17) transition metals shown in figure 3 below and they are the elements that form the Group 3 of the periodic table. Regardless of being called the “rare earth elements, these metals are actually not at all rare in the Earth’s crust. The least abundant REE, thulium and lutetium, still exhibit average crustal abundances of over 200 times that of gold (Haxel et al. 2002). The major challenge regarding

Figure 3: The periodic table highlighting the rare earth elements, their atomic numbers and where they are positioned in the table (modified from U.S geological survey and www.periodni.com).

7 the REEs is the fact that there are relatively few geological processes that can concentrate them into deposits of economic value. On the other hand, the REEs could be certainly rare looking at it from a cosmic scale which could be, recalculating to atomic concentrations. Taking atoms of terbium (Tb) and thulium (Tm), for instance, they are two and five times (respectively) less abundant in the continental crust than Mo and two orders of magnitude rarer than Cu (Chakhmouradian & Wall, 2012). Generally, they are grouped into two categories namely, the heavy and light rare earth elements (HREE and LREE). The heavy REEs (HREE’s) which includes Tb and Tm makes deposits rich in these elements especially valuable, because of both their scarcity and compatibility to nature (Hazel et al., 2002 cited in Sahlström, 2012).

Also from the economic view point, another important factor that accounts for the REEs being rare is the current global political climate. A lot of REEs famous mines where been closed-down during the years leaving China which has been known as one of the countries to have single- handedly dominated the REE-market by controlling over 95 % of the global REE-supply for a long time now to begin to experience decrease in REE export quotas in recent times, and this has created a lot of uncertainty in the future global supply of REE (Moffett & Palmer 2012). Due to this uncertainty, more research on REEs are being conducted and new deposits are now opening for exploration of REE worldwide, this includes the former famous “Mountain Pass Mine” which is being re-opened (Wiens 2012, Molycorp). Likewise, in Sweden, this new-found interest in REE and other strategic metals is indeed very noticeable, with active exploration and planned mining of deposits such as the Norra Kärr Zr-REE deposit (Jonsson 2013, Nebocat 2009), as well as increasing research on other types of REE bearing ore deposits such as the Bastnäs-type (skarn) ore deposits in Bergslagen (e.g Högdahl et al. 2012, Sahlström 2012, www.eurare.eu).

It is significant to note also, that the REE’s are highly important elements, as they are extensively used in several different industries, including the booming “high tech” and “green tech” sectors, which has contributed enormously to the increasing demand for these commodities (e.g. Haxel et al. 2002). Aside from their importance in the production of everyday appliances such as cars, computers, ceramics and more, they are also used for scientific applications and due to this global demand for these products which have become of great necessity to humans, REE demand throughout the world is projected to increase.

1.3 GENERAL CHARACTERISTICS OF BASTNÄS-TYPE REE MINERALIZATION The Bastnäs (skarn)-type deposits also referred to locally as Fe oxide-REE-Cu- (Co-Au-Bi-Mo) (Holtstam et al., 2014), all occur within a belt of mostly significantly altered felsic metavolcanic to meta-sedimentary rocks in the Nora-Riddarhyttan - Norberg area, which is called the “REE- line” shown in figure 4 (Jonsson & Högdahl 2013).

Figure 4: Map indicating the REE-line which runs from Nora to Riddarhyttan to Norberg (modified from Anderson et al., 2004).

The skarn ore deposits are usually characterized by large amounts of Ca-Mg silicates and Fe oxides which are associated with marble either dolomitic and or calcitic (Holtstam et al., 2014). The REE-silicate bearing mineralization in the area, generally occur as seemingly epigenetic, massive to disseminated magnetite-skarn replacements in mostly dolomitic marbles (e.g. Geijer

1920, Geijer 1961, Holtstam 2004). Figure 5 highlights the carbonate-rich sedimentary rocks as host to the Skarn ore deposits (Armstrong et al., 2011). The ores have been interpreted to be either syngenetic, stratiform, ash–siltstone-hosted Zn-Pb ores or stratabound, volcanic- associated, limestone-skarn-hosted Zn-Pb impregnation ores (Allen et al., 1996 and 2003).

These deposits, among others, have been important for local mining of iron for centuries, and for REE sporadically since the late 1800’s. They are also known for being the location of several discoveries of both new minerals and elements (see Andersson et al. 2004; and references therein).

Figure 5: Showing skarn ore in Carbonate-rich sedimentary rock (Armstrong et al., 2011).

For the Bastnäs-type ore and for REE in general, the element which could be argued as one of the first to be discovered is cerium. It was found as a major constituent of a reddish heavy mineral termed the “Bastnäs tungsten” which was later formally named cerite. Wilhelm Hisinger and

Jacob Berzelius (1779 – 1848) were the scientist who gave a detailed description of the new element after extensive chemical analyses in the early 19th century (Moeller, 1973 & Krogt 2010). The name “cerium” was inspired by the discovery of the asteroid Ceres three years earlier (Trofast 1996). Despite their historic significance in both mining and science, little is still known about the genesis of the Skarn-iron ore deposits. Various theories however have been proposed, and the consensus today is that these metals are hosted by “skarns” which were formed during epigenetic reactions as shown in figure 5, between a hydrothermal mineralizing silica and metal- bearing fluid and pre-existing carbonate rocks (Andersson et al. 2004, and references therein). However, the specific nature as well as origin of such a fluid is yet to be determined. Typical host minerals comprise cerite-(Ce), törnebohmite-(Ce) and allanite, as well as REE-fluorocarbonates such as bastnäsite-(Ce). Table 1 below, list the major mineralization for this ore type and have been checked according to their location of existence.

Table 1. REE mineralization for the Bastnäs-type ore deposits (Anderson, 2004).

Mineral Formula Subtype 1 Subtype 2 Allanite-(Ce) (Ce,La)CaFe2+Al2[Si2O7][SiO4]O(OH) x x Bastnäsite-(Ce) (Ce,La)CO3F x x Bastnäsite-(La) (La,Ca)CO3F x Cerianite CeO2 x Cerite-(Ce) (Ce,La,Nd,Ca)9(Mg,Fe)Si7O24(O,OH,F)7 x x Dissakisite-(Ce) Ca(Ce,La)MgAl2[Si2O7][SiO4]O(OH) x x Dollaseite-(Ce) Ca(Ce,La)Mg2Al[Si2O7][SiO4]F(OH) x Ferriallanite-(Ce) (Ce,La)CaFe2+AlFe3+[Si2O7][SiO4]O(OH) x Fluocerite-(Ce) (Ce,La)F3 x Fluocerite-(La) (La,Ce)F3 x Fluorbritholite-(Ce) Ca2(Ce,Nd)3[SiO4]3F x “Fluorbritholite-(Y)” Ca2(Y,REE)3[SiO4]3F x Gadolinite-(Ce) (Ce,Nd)2Fe2+Be2Si2O10 x x Gadolinite-(Y) (Y,REE)2Fe2+Be2Si2O10 x Håleniusite-(La) (La,Ce)OF x Lanthanite-(Ce) (Ce,La)(CO3)3 • 8H2O x Parisite-(Ce) Ca(Ce,La)2(CO3)3F2 x Percleveite-(Ce) (Ce,La)2Si2O7 x Törnebohmite-(Ce) (Ce,La)2Al[SiO4]2(OH) x x? Västmanlandite-(Ce) (Ce,La)3CaAl2(Mg,Fe)2[Si2O7][SiO4]3(F,O)(OH)2 x

Based on slight local differences in chemistry and mineralogy of the deposits, Holtstam & Andersson (2002) suggested a subdivision of Bastnäs-type deposits into two subtypes as shown in table 1. Subtype 1-deposits comprise the Riddarhyttan and Rödbergsgruvan areas where the mineralisations are mainly enriched in LREE and Fe-rich silicates. In subtype 2-deposits, which are found in the Norberg area (e.g. Östanmossa, Malmkärragruvan, Södra Hackspiksgruvan,

Johannagruvan), the ores are enriched in LREE and HREE+Y together with Mg, Ca and F (Holtstam & Andersson 2007).

Geijer (1927, 1961), was one researcher who reached a conclusion that the Bastnäs-type mineralisations was formed epigenetically, through reactions between pre-existing carbonate rocks and high-temperature ﬂuids. The spatial relation to Mg-enriched country rocks suggested a genetic link to extensive metasomatic alterations, and the emplacement of the early (1.9 Ga) granitoids, that affected the felsic volcanic successions. According to contemporary knowledge this type of Mg metasomatism is, however, related to synvolcanic alteration and circulation of seawater-dominated ﬂuids (e.g. Trägårdh 1988, 1991, Ripa 1994).

Therefore, the Bastnäs-type ores have been described by majority of researchers as hydrothermally- metasomatic deposits (see figure 6) (Chakhmouradian & Wall, 2012).

Figure 6. Major rare earth element deposit type in a tectonic context. The 2nd deposit from the left is the Bastnäs of Sweden clearly identified as a hydrothermally-metasomatic deposit (after; Chakhmouradian & Wall, 2012).

Furthermore, there are literatures which have made available evidence that suggest that, the Bastnäs-type deposits were formed as a result of submarine volcanic to subvolcanic magmatic- dominated hydrothermal activity at around 1.9 Ga. The iron ores and the associated skarn as well as REE mineralization, were generated through reactions between pre-existing marble beds within the volcano-sedimentary sequence and hydrothermal fluids most likely in a sub-seafloor position. The difference in mineralogy between the deposits may originate from, local fluid (and

12 mineral) evolution, the degree of fluid-rock interaction and variations in the local ore forming systems which includes hydrothermal facies (Jonsson et al., 2014).

In prospecting for this ore type therefore, indicator minerals such as Cu, Zn and Mo can be used as input in the prospectivity modeling.

General observation from literatures reviewed have shown that the metamorphic grade in this region is low - medium (amphibolite facies conditions) however, more details of metamorphism in this region will be elaborated upon in subsequent chapter.

1.4 MINING HISTORY OF BERGSLAGEN The Bergslagen ore province has a mining history of more than 1000 years. Bergslagen from a metallogenetic point of view, should be regarded as an iron oxide ore district in terms of the number of deposits and the total tonnage of iron (Fe). This is because, Bergslagen as a region records 41 largest deposits in the region with production of greater than 1 million metric tonnes (>1 Mt), where 32 of these deposits are iron oxide ores and only 9 are sulphide ores, producing (until 1993) approximately 421 and 74 Mt, respectively (Ripa & Kübler 2003). This highlights the fact that iron oxide ore deposits have played a much more important role than sulfide ore deposits in the Bergslagen province; thus, regarding it as an iron oxide ore province would not be regarded as over-estimation.

There are several thousands of discrete iron oxide deposits known in the Bergslagen ore province. The iron oxide ore deposits that have had significant economic importance can be divided into three main groups: skarn iron ores, banded iron formations (BIF) and apatite-iron oxide ore deposits – see figure 6. The skarn iron ores, are the most common and most widespread, as indicative in the map in figure 6 (Magnusson 1970). They have been mined in several ore fields around the Ludvika region most of which are worth mentioning particularly for their remarkable production numbers one of which is the Blötberget ore field. Records of iron-oxide ore deposits in this field calculated in tonnage of iron production for both past production and proven reserves recorded 43.7 Mt, which was been ranked as one-fifth of the tonnage in the Grängesberg Mining District (GMD) (cf. Allen et al. 1996) making Blötberget ore field the second largest iron-oxide ore deposit in the Bergslagen province. Another historic field is the Grängesberg mining field which was one of the earliest mines (ca.1888) and has recorded production of up to 152 Mt of ore with an average of 58% Fe and 0.81% P, from the beginning of the 20th century until they closed in 1989 (Stephens et al., 2009).

Figure 7: Map showing different ores, deposits and host rocks – after Magnusson 1970

2. BACKGROUND 2.1 REGIONAL GEOLOGY OF BERGSLAGEN Regionally, Bergslagen is located in the southern part of the Svecokarelian domain of the Fennoscandian Shield and this is shown in figure 7 below. Its other two counterparts are Norrbotten and Skellefte district which lies in the northern part of the Svecokarelian domain and famous for their apatite-iron oxide ores and massive sulfide deposits respectively. The both volcanic belts (northern and southern), are dominated by felsic metavolcanic rocks whose formation age ranges between 1.90 – 1.87 Ga and are underlain by thick basal greywackes which indicates a continental margin environment (Allen et al., 1996).

The Bergslagen region makes-up the southern volcanic belt in the Svecokarelian domain. It has been pointed out, that there is a lateral variation in lithology and stratigraphy in the Svecokarelian southern volcanic belt, as far as the Swedish part is concerned (Lundström, 1987). Where the western part is dominated by felsic metavolcanic rocks, with metasediments over- lying them, whereas the eastern part is dominated by upper and lower metasediments with mafic to intermediate meta-volcanic rocks in between. The deduction given by Lundström (1987) is that there were predominantly rhyolitic volcanic centers in the west, providing erupted material to the east. Allen et al. (1996) interpreted the Bergslagen region as an extensional, back-arc, active continental margin. It has undergone six major stages of geological events during its primary formation: intense magmatism, thermal doming, crustal extension, waning extension, waning volcanism and thermal subsidence. It has been known to have undergone a later reworking which was moderate, apart from the intrusion of rapakivi granites and the formation of the coherent Trans-Scandinavian Belt of granites and porphyries along the boundary between the Svecofennian and Southwest Scandinavian Domains. The Svecofennian Domain therefore documents to a very large extent, the formation of continental crust during the early Proterozoic times (Gaal and Gorbatschev, 1987).

Having been subjected to extensive magmatic activities, the Svecokarelian supracrustal rocks of Bergslagen experienced large volumes of plutonic rocks formed in as quartz-rich magmas intruding the supracrustal rocks. Therefore, it is of importance to note that the Bergslagen region consist of types of intrusive rock suites which are, the Granitoid-dioritoid-gabbroid (GDG) intrusive rock suites, the late- and post- Svecokarelian Granitoid-synitoid-dioritoid-gabbroid (GSDG) intrusive rock suites and the Granite-pegmatite (GP) intrusive rock suites of which figure 8 helps on a very partial scale to denote, their distinct metamorphic grades.

Figure 8: Geological map of the Svecokarelian/Fennoscandic Shield placing Bergslagen in the Paleoproterozoic part of the Shield. (after, Weihed et al. 2005).

It is important to note that the Bergslagen region could be structurally divided into four separate domains based on regional variations of structural geology in respect of style, orientation and relative timing of the ductile deformation in the region. The four different domains are named

Northern, Central, Southern and Western domains. The study area predominantly characterized by the Central domain, even though the Northern and Western domains are represented in minor areas in the outskirts of the study area. The two major deformation events that have affected the bedrock of Bergslagen are the Svecokarelian orogeny (1.87-1.75 Ga) and the Sveconorwegian orogeny (1.0-0.9 Ga). The general observations of the deformation in Bergslagen on a regional scale suggest that there are two generations of folds that are present (Stephens et al., 2009) and the possibility of an additional third folding generation has been discussed in the literature (Talbot, 2008; Stephens et al., 2009).

2.2 LOCAL GEOLOGY OF BERGSLAGEN Bergslagen on a local scale, is situated in central Sweden and most of the mineralization in this province is primarily, hosted by Paleoproterozoic rocks of 1.90 – 1.87 Ga in age (Allen et al. 1996) and they are the oldest bedrock in this province. They compose of bimodal metavolcano- sedimentary succession that has been suggested to be approximately 8 – 10km thick which includes the “Leptite Formation”, the “Bergslagen Supracrustal Series” and the “Bergslagen Group” (Beunk & Kuipers 2012; Oen et al. 1982). The lowermost part of the stratigraphy, i.e. the underlying metasedimentary rocks and the lower part of the metavolcanic succession, is exposed in the eastern part of the province, and the upper parts of the volcano-sedimentary succession are exposed in the west (Allen et al. 1996). The metasedimentary rocks in the lowest part of the stratigraphy are dominated by turbiditic greywacke, originally deposited in deep waters. At 1.9 Ga, the sedimentation was interrupted by extension and an intense and explosive volcanism which reduced the water depths, leading to deposition of the volcanic rocks in alternating subaerial and marine environments (Allen et al. 1996). This intense volcanism produced voluminous successions of volcanic sandstone and breccia that are occasionally intercalated with sandy and conglomerate layers, skarn (calc-silicate rock) and marbles (Stephens et al. 2009).

It has been interpreted generally as a felsic magmatic region where supracrustal volcanics were deposited on older continental crust in an intra-continental extensional or a continental back-arc tectonic setting of same age (Weihed et al., 2005). Chemical and tectonic evidence suggests that continental crust was present beneath the calc-alkaline volcanites of the western Bergslagen. Diapirism with attendant folding of the surrounding rocks was an important feature in areas dominated by early Svecofennian granites (Högdahl, Andersson & Eklund, 2004).

The study area is known for its diverse range of ore deposits (examples shown in figure 9), which includes banded iron formations (BIF), magnetite skarns, manganiferous skarns and marble- hosted iron ores, apatite-magnetite iron ores, Stratiform and Stratabound limestone Zn-Pb-Ag- Cu, sulphide ores Zn-Pb-Ag (Cu-Au), and W skarns (Allen et al., 1996). For this project, emphasis

17 will be directed to specific areas most commonly recognized with mineralization of REE for the Bastnäs (skarn)-type ore.

Figure 9. Shows Stratabound Limestone skarn-iron ore associated with Zn-Pb-Ag-Cu occurrence at Garpenberg ore field with indication of Mg alteration. (Allen et al., 1996 modified from Weihed & Allen, 2011)

Following much research however, the skarn ores have vividly shown major occurrences in this study area with most emphasis around the REE line area which is the Riddarhyttan and Norberg area as shown in figure 10 yet not much have been spoken on REE in skarn-ores, hence this study.

Figure 10: Map of Bergslagen region of central Sweden with the area of study marked by the rectangular box (After Holtstam D. et al. 2014).

2.3 BEDROCK OF STUDY AREA As stated previously, the Bastnäs-type deposits in the study area are primarily hosted by crystalline carbonate rocks. However, in order to present a more simplified description, the bedrock of the study area has been divided into two more generalized rock types so as to get a clear understanding of the rock types of particular interest in this study area.

1) Sedimentary rocks The Svecofennian metasedimentary rocks of Bergslagen constitutes of quartzite, crystalline carbonate (marble), meta-argellite, metagraywacke, meta-arkose and feldspathic metasandstone, rocks. In comparison to the Bergslagen region as a whole, the distribution of these rock types are very limited within the study area (Stephens et al. 2009). Also, there are quite a number of occurrences of granites, granodiorites, monazites, dacites and rhyolites occurring mostly within the Svecokarelian acidic - intrusive and -volcanic rocks and this is shown in the local map of the study area in figure 11 below. The crystalline carbonate rocks constitute only a minor segment of the rock type in terms of occurrences however, it could be significant as it is most often associated to the skarn-iron oxide ore mineralization in the study area. The occurrences of this rock type are quite scattered and comprises of calcite- or dolomite- rich crystalline carbonate rocks as well as calc-silicate rocks (Stephens et al., 2009)

Figure 11: Local lithological map of the north-western part of Bergslagen region - Source: SGU database.

2) Igneous rocks The Svecokarelian volcanic and subvolcanic intrusive rocks may be described texturally as fine-grained, quartz and feldspar dominated felsic rocks with epigranular and granoblastic. On a regional scale, most of the host rocks in western areas of Bergslagen have been interpreted as volcanic and sub-volcanic because of their rhyolitic composition which suggests that they have been deposited in an environment dominated by volcanic and sub-volcanic processes. On the other hand, however, it is seen that dacitic composition appear to be more common in the Eastern part and this difference in composition for these rocks could be because of different magma sources. Several researches has recorded that deposition of the volcanic succession in Bergslagen happened in three stages. It was said to have undergone the first stage in an extensional setting with extreme shallow water volcanism and then moved on to waning volcanism for the second stage which contributed to the sea level transgressing and both these stages have occurred during regional metamorphism. The third stage could be told to have been initiated by the sea level subsidence, this then brought about deep sea sedimentation during thermal subsidence. Intrusive rock suites such as the GDG intrusive rock suite could be separated into two different ages which are 1.90-1.87 and 1.87-1.75 Ga (Gaal and Gorbatschev, 1987). The intrusions of the latter suite formed after ductile deformation and are only present in the south-western and north-eastern areas of Bergslagen, thus situated outside the study area. The GDG intrusive rock suite (1.90-1.87 Ga) is of the same age or perhaps slightly younger than the Svecofennian volcanic and sub volcanic intrusive rocks. Most of the older GDG rock suite intrusions are dated around 1.89 Ga and is granitic in composition, whereas most of the GSDG rock suite is dated around 1.87 Ga. (Stephens et al., 2009).

The early Svecofennian granitoid rocks make up the bulk of the Svecofennian continental crust. They are exposed ubiquitously throughout the volcanic belts and form large plutonic complexes, e.g., in the Bergslagen area of south central Sweden. Apart from a specific sub-group of minor, subvolcanic intrusions, the early Svecofennian granitoids are a differentiated suite of calcic and calc-alkaline, I-type rocks ranging from gabbros and diorites through dominant tonatites and granodiorites to granites sensu stricto (after, Gaal and Gorbatschev, 1987).

Figure 12. Regional lithological map of the study area showing REE mineralization distribution - Source: SGU database.

2.4 STRUCTURAL GEOLOGY OF STUDY AREA The domain is dominated by ductile deformation which occurred during the Svecokarelian orogeny (Stephens et al., 2009). However, from different axial plain observations, it has been noted by Stephens et al. (2009) that the Central-West Bergslagen structural domain has undergone several generations of major folding events and most of these folds has been overturned hence showing an East-West to North-East-South-West strike orientation of the axial plains. Stephens et al. (2009) explained these axial planes to have since been re-folded in the western areas of the domain and thus are now striking in a NW-SE to NNW-SSE orientation.

Figure 13: Map to show the sturctural linnings, fault and ductile shear zones of the Bergslagen region – after Stephens et al. 2009.

2.5 METAMORPHISM OF STUDY AREA The age of metamorphism in Bergslagen has traditionally been considered to be late Svecofennian, which is about 1845 - 1780 Ma, but relatively few direct age determinations exist (Andersson et al., 2004). This metamorphism was also considered to be related to the same tectono-magmatic heat flow event that caused the coeval late Svecofennian and TIB magmatism (Öhlander & Romer 1996, Andersson 1997), possibly imparted by mafic underplating.

The study area comprises of four categories of metamorphic domains (see figure 10).

A gradual shift into lower–middle amphibolite facies conditions is observed towards northern Bergslagen, where paragneisses are characteristically andalusite-bearing about <600 °C (Sjöström & Bergman, 1998), with a minor area of greenschist facies rocks in the north-west.

Regional metamorphism of low-pressure type overprinted the Nora– Riddarhyttan–Norberg area some 50–100 Ma after the deposition of the supra-crustal rocks.

Figure 14. Map showing the metamorphic facies of the study region - Source: SGU database.

2.6 ALTERATION IN STUDY AREA Alteration simply means physical, mineralogical or chemical changes experienced by rocks as its environment is changed in such a way that the rock is no longer in equilibrium with its environment. New minerals that are in equilibrium under the new conditions have being formed because of changes in temperature or pressure. Changes in the chemistry of the rock are most commonly due to the entry of an external fluid of which the most common is water which is the best solvent in nature (Magnus Ripa, 2003.)

When water flows through a rock, through fractures and cracks and along mineral boundaries, it changes the environment of the rock and has the capacity to transport different elements in and out of the system. Hydrothermal alteration is therefore one of the most effective ways of changing a rock’s chemical and mineralogical composition. Hydrothermal alteration, in the broad sense, takes place in many settings, from weathering of outcropping rocks in all climates to hydrothermal alteration at depth in volcanic terrains. Due to the sluggish nature of geological processes and the rapid changes in the physical and chemical properties of the hydrothermal fluids, the rocks will in many cases approach, but not reach, equilibrium with the new environment. When this happens, the outcome would be different intermediate reaction products producing partially altered rocks. In this wise therefore, it is in line to say that the chemistry of an altered rock can be used to identify, characterize and classify alteration.

These styles of alteration, considered here to represent regional styles of alteration, caused an increase in the K2O, Na2O or MgO content of the rocks, compared with the less altered precursor, and a decrease in other major elements. These alterations can thus be simplified as a three- component system of MgO-Na2O-K2O. Although a few of the Mg-altered samples show an increase in the Fe2O3 content, this type of alteration should not be confused with the Fe-Mg alteration recorded near iron ores (e.g. Holtstam & Mansfeld 2001) or the skarn deposits discussed below. Magnesian alteration is manifested by the development of discordant zones of pargasite para-amphibolites and formation of stratiform pargasite rocks texturally similar to the interlaminated grandite-epidote-ferroan diopside rocks. Some researchers like Hughes (1973) have applied the igneous spectrum method which although is restricted to variations in the Na2O-K2O content of rocks, it is useful to detect alkali alteration, however may also indicate breakdown of alkali feldspar. To complete the interpretation of the chemical data and to obtain qualitative and quantitative data on the element changes occurring during alteration, mass change calculations of sets of data would be performed using the Alteration index method and the Carbonate-Chlorite-Pyrite Index. For each style of alteration identified, an attempt will be made to look back at published documentation of the petrology and mineralogy of the alteration. In the calculations, it is assumed that the rocks make up a single precursor system, that is, the precursor to all altered felsic volcanic rocks had an identical chemical composition. This is probably not entirely correct, but it is assumed that the chemical variation among the rhyolitic

26 precursors was small and that it’s Metasomatic, mostly syn-depositional, hydrothermal alterations has been reported as to have occurred in large areas in the Bergslagen region, and these alterations where thought to be related to much of the metallogeny in this area (Andersson et al., 2004). Most specifically, for the limestone-skarn-hosted ores, there are commonly associated with extensive footwall alteration and almost undoubtedly related to nearby subvolcanic intrusions.

Alteration types such as Na-K-Mg Metasomatism, has occurred in most of the metavolcanic rocks and from examining these changes in the altered rocks several researchers have attributed most of the variations in alteration to differences in alteration temperature. The large variation in the alkali content of felsic volcanic rocks in Bergslagen has been discussed for nearly a century (e.g. Sjögren et al. 1914. cf, Ripa 2003). It was suggested early on that the anomalous composition of mica-rich volcanic rocks was a result of metasomatic processes and was not a primary feature of the rocks (Sundius 1923). The suggestion that the variation in alkali content was also due to metasomatic processes was put forward by Frietsch (1982) and it was shown that the metasomatism was due to hydrothermal alteration, with seawater as the dominant fluid (e.g. Baker & de Groot 1983, Lagerblad & Gorbatschev 1985). It was observed that some styles of alteration were more common at specific stratigraphic levels (Frietsch 1982), for example, Na- altered volcanic rocks generally occurring below K-altered volcanic rocks. It has also been shown that both the early orogenic intrusions (Baker 1995a, 1995b) and mafic intrusions (Valbracht et al. 1991) were affected by hydrothermal alteration. A most important observation discussed in recent years is the division of alteration in Bergslagen into regional scale alteration and an overprinting local and locally ore-related alteration (Baker et al. 1988a and references therein). Ripa (1994) stressed the importance of distinguishing local from sub-regional and regional alteration when exploring for ore. De Groot and Baker (1992) divided alteration in western Bergslagen into regional alteration, of a widespread, low-temperature style, and local, intense alteration that strongly affected the rocks. Trägårdh (1991) also applied a clear distinction between regional and local alteration in the Riddarhyttan area (Magnus Ripa for SGU, 2003).

In the Bergslagen region, Skarn alteration has calc-silicate blastesis occurring in response to both regional metamorphism of calcareous, volcano-sedimentary rocks and because of alteration by fluids from intrusions as demonstrated in figure 15. The alteration is frequently referred to as skarn formation. In rocks that have undergone greenschist-facies metamorphism, “skarn alteration” is, in many places, demonstrably cross-cutting and linked to the intrusion of mafic dykes. At some distance from a dyke, scattered sheaves of actinolite overprint the volcanic textures. Closer to the dyke, the actinolites merge together, in many places leaving relict areas, similar to clasts, set in an amphibole matrix (Lundström 1995). In this manner, the alteration created deceptive, pseudoclastic textures. Baker et al. (1988) described zones of “skarn alteration” around a mafic dyke and suggested that amphibole growth post-dated earlier

“magnesium alteration”. In several cases, the alteration was associated with magnetite growth and mineralization (Sundius 1923, Baker et al. 1988).

Figure 15. Flow paths and hydrothermal features highlighting mineralizations and different alteration types (after Williams et al., 2005).

3. MATERIALS AND METHODS For this thesis, the “Weights-of-Evidence” method which is applicable in ArcGIS for predictive modelling is used to evaluate mineral weights. This method is an empirical model which estimates by statistical means different evidences of relative importance. From previous definition, the empirical model is a quantitative method however, the Weight-of-Evidence method appears to be a hybrid as it engages both quantitative and qualitative approaches whereby it not only calculates statistically the probabilities for predictor variables against the target variables, it also classifies based on expert opinion (Bonham-Carter, 1994).

There are a few important concepts also in the Weight-of-Evidence model which are applied in predictive modelling which includes, the Boolean logic and the Conditional probability of the Bayesian probability. The former measures the favourability of a target variable while the later estimates both prior and posterior probability.

3.1 PRINCIPLES OF PREDICTIVE MODELING John Carranza (2009) is one researcher who explained predictive modeling as a process of analyzing and integrating pieces of spatial geo-information. He defined it clearly as “making descriptions, representations, or predictions about an indirectly observable and complex real- world system via (quantitative) analysis of relevant data” (Carranza, 2009, p.4). The process involves generally different forms of data analysis that makes description and representation of indicator patterns or geochemical anomalies of the ore forming process for a specific mineral deposit type.

In ARCGIS, models such as the empirical model are one of the approaches used to identify favourable areas for the sought mineral deposit type. To successfully determine favourable areas in mineral exploration, terms such as “background”, “threshold”, “predictor variables” and “target variables” cannot be ignored. Background is the term that describes normal concentrations of elements present in non-mineralized earth material. The “background values” are considered to be relative rather than absolute given that in any earth material, uniform distribution element rarely occurs. The upper limit of the uni-element background values is referred to as “threshold” and concentrations that exceed the threshold are called anomaly. Significant anomalies on the other hand are concentrations that indicate a mineral deposit. Predictor variables would be explained as patterns which are most probably indicative for a specific mineral deposit type while target variables constitute of specified mineral type deposit (Carranza, 2009).

In approaching predictive modelling, it is important to know that there are two major ways of doing so and these are the “mechanistic modelling approach” and “Empirical modelling approach” and this is explained in the subsequent paragraph.

3.1.1 MECHANISTIC APPROACH This kind of approach also referred to as “theoretical approach”, is basically sub-divided into two kinds namely, deterministic and stochastic mechanistic modelling. Both models use mathematical approach of which in a mineral prospectivity mapping context it would show relationships and controls on ore forming processes. Significant differences however between the two sub-types of mechanistic approach would be that while deterministic model does not take randomness into consideration, stochastic model does when working out the distribution of predictor variables in the target variables. Also in the results produced from both models, the stochastic model produces a probability distribution for target variables while deterministic model produces a single estimate for the target variable (Caranza, 2009).

3.1.2 EMPIRICAL APPROACH An empirical model characterizes or quantifies empirical relationships between the target variable and several predictor variables. In the context of mineral prospectivity mapping, this model is particularly useful in cases where the controls on the ore forming processes are poorly or indirectly known. There are two types of empirical models namely, quantitative and qualitative empirical models and selecting which of these to use in a mineral potential mapping depends totally on the amount of data available. The quantitative empirical model is also known as data- driven models. This is so because they require a substantial amount of data on both target and predictor variables in other to estimate their relationship accurately. Qualitative empirical model on the other hand is also referred to as knowledge-driven model and is most applicable when data is insufficient or lacking whereas the relationship between the target and predictor variable is generally determined based on expert opinion (Carranza, 2009).

3.2 PREDICTIVE PROSPECTIVITY MODELING WITH ARCGIS The Weight-of-Evidence modeling has been done using the ArcGIS software with most specific use of the spatial analyst (SA) and the spatial data modeler (SDM) toolboxes. The basic steps which have been followed in executing this project are summarized as follows.

 Demarcate study area: In order to do this each cell size unit of the chosen study area had to be decided. Bonham-Carter et al. (1989) gave the expression T = t/n as the expression to be used to calculate for cell units where “T” signifies the total number of unit cells, “t” is the total study area in km2 and “n” is the size of each unit cell. For this project, the total study area was calculated to be 26,232 km2 and each unit cell was decided to be 1 km2. For the predictive modelling target and predictor variables need to be chosen as well as explained in the previous chapter and for this thesis, datasets have been chosen from SGU data of mineral resources. A total data of 47 occurrences of interpreted REE deposits was chosen and then divided into a training set of 44 deposits and a validation set of 3 deposits.

 Conversion of data: In ArcGIS, to process data according to the WofE model, all data have to be in raster form this implies that if any data has been given in vector format which is a point, polyline or polygonal data, it has to be converted to a raster format for it to function properly in the WofE model. To do this the Inverse Distance Weighting (IDW) method was used and this makes the value of a point to have the same influence in all directions from that given point and the weighting favours data points which are close to the interpolation point as they receive relatively large weights compared to those points far away (Bonham-Carter, 1996). The general formula for IDW is;

푛 ̂ ∑푖=1 푤푖 푧푖 푍0 = 푛 Eq. 1 ∑푖=1 푤푖 Where Z0 represents the estimation point, zi the sample points and wi weights related to distance from point i to point 0.

 Classify and Reclassify data: The interpolated raster data were then reclassified into 10 quantiles (in some cases only 9 quantiles were possible). After the classification of raster data into 10 quantiles, the raster data has to be converted to integer data (i.e. values 1- 10) to enable data to be used in the calculate weights function to calculate weight tables for the different predictor variables and the training set. The Euclidean distance is estimated for vector data such as proximity to favorable structures, heat source or host rock and then classified into 10 quantiles which are represented as buffer distance maps.

 Weight calculation: For calculating weights, the Bayesian probability is an important concept as it calculates for conditional probability which estimates both prior and posterior probability (Bonham-Carter, 1994).

퐷 푃{퐷} = Eq. 2 푇

Where 푃{퐷} is the prior probability, D is the number of geo-objects occurrences of a variable and T is the total number of unit cells.  Weights Classification: Here each integer class is classified into favourable or non- favourable between the predictor variable and the target variable which is basically the concept of Boolean logic. Here each unit cell can either score 0 or 1.

 Binary maps – pairwise test – mineral potential maps: With the classifications been made, binary maps will be created and then pairwise test will be conducted for conditional independence. Finally, by combining all the favourable binary maps together, a mineral potential map is generated and this final map is what shows the posterior probability for mineral deposit occurrences for each unit cell in the demarcated area.

3.3 PROCEDURE FOR MINERAL EXPLORATION PREDICTIVE MODELING In a mineral exploration context, predictive modeling can be carried out using the following procedure referred to mostly as phases;

(1) Area selection: Identifies regions where the tectonic setting is deemed as favorable for the sought mineral deposit type (2) Target generation: Targeting prospective areas within the tectonically permissive study area so as to provide exploration targets for the sought mineral deposit type. This usually is based on spatial association between anomalies and the sought after mineral deposit as well as prospective areas defined by the intersection of such anomalies. (3) Resource evaluation: This happens only if a mineral deposit has been identified. (4) Reserve: This is the stage where a mineral deposit attains classification into being called a mineral resource. (Carranza, 2009). That is after a mineral deposit has been identified in the area for exploration and this confirmation most times is done by drilling to enable estimation of grade and tonnage. This thesis however, is mainly focusing on the second phase of the predictive modeling, i.e. the target generation phase, as the area selection phase was executed during the initial stage of this

32 project. The area selection was influenced by predictive prospectivity mappings which have been done in this same district as the study area. Though it has always been done for VMS deposits, Dr. Martiya Sadeghi of SGU came up with the idea that it would be interesting to do a similar study for the REE deposits in this same region of Bergslagen. Furthermore, the area selection has been refined by reference to lithological maps showing that the meta-volcanic rocks and iron-ore deposits were generally distributed both on the REE-line (Bastnäs-type ores) and in major bedrocks of Western Bergslagen. The North-Western areas of Bergslagen showed a particularly high abundance of the favorable host rock and known REE mineralization and were thus selected as a study area (see Figure 3) (Stephens et al., 2009).

3.4 GENERALIZED CRITERIA FOR POTENTIAL EXPLORATION

Study Area North-Western Bergslagen

Geology and Conceptual Models of Mineral deposits Exploration Database Knowledge driven SGU

Processing

Predictor Maps For each Exploration variable

Reclassification

Weight of Evidence Modeling (WofE)

Spatial Association Evidence Maps

Validation

Mineral Potential Map

Figure 16: Flow chart of the methodology for the prospectivity modeling – modified from Porwal, 2006.

The flow chart in figure 14 is a generalized method used in this project for the mineral potential mapping. As described above the project is implemented in stages where the first stage would be to establish criteria for exploration based on conceptual models of the two type ores and this is obtainable from reviews of literatures as well as data available from the exploration database. These chosen exploration criteria data from the exploration database are then processed to produce “predictor maps” which are meant to show favourable areas and then reclassification is done to get “classified predictor maps”. Subsequently, weight tables are computed and classified to enable identification of key predictor indicators from which “spatial association evidence maps” are generated and presented as binary favourability maps where all areas on the maps are classified to be either favourable or non-favourable.

Finally, validation is done for all the predictor variables by running a conditional independence test after which we would produce the final “Mineral potential map”.

3.4.1 EXPLORATION DATA USED Dataset used for this prospectivity modeling has been provided by the Sweden’s geological survey and are all based on regional scale of 1:250 000. They are summarized into the following:

. Geophysical data - For the sake of this thesis, geophysical datasets such as magnetic field data was available for analysis.

. Geochemical data - The geochemical datasets on the study area comprises of biogeochemical data, till geochemical data and litho-geochemical data.

. Lithology – Various rock types in the study area such as volcanic rocks, intrusive rocks, metamorphosed sedimentary rocks, e.t.c.

. Structural geology – Fault lines of the North-Western Bergslagen area which includes the NE-SW, NW-SE, WSW, ENE, e.t.c.

. Mineral deposits – Such as iron oxide deposits of the study area.

3.5 MINERAL DEPOSITS OF REE IN STUDY AREA The mineral deposits as shown in table 3 have been carefully selected from the dataset provided by SGU. The selected deposits are a total of 47 and they have been divided into a training and validation dataset for the type ore. The first three (3) highlighted deposits that begin the table below are the chosen validation deposits for the prospectivity mapping.

Table 3. Chosen training and validation deposits for weight of evidence binary maps – from SGU dataset provided for this thesis.

NAME COMMODITY HOST ROCK DEPOSIT MINERAL STATUS N_COORD E_COORD

Bjursön Zn; REE Schist/gneiss Prospect 6635850 1488920

Riddarhytte Fe; REE calc -silicate / skarn magnetite; Prospect 6658190 1500939 mälmfältet

Storfallsberg Fe; CU; FE- calc -silicate / skarn Calcopyrite; Prospect 6684976 1461228 sulphides; REE; Sn; magnetite Bi

Sankt Fe; Cu; REE; iron formation; calc - closed mine 6636359 1487829 Gransgruvan silicate / skarn;

Ceritgruvan REE; Fe; Cu; iron formation; calc - cerite; closed mine 6636370 1487805 silicate / skarn;

Järnklockan Fe; REE; calc -silicate / skarn; closed mine 6636980 1487350

Stσlklockan Fe; Mo; Au; REE; calc -silicate / skarn; closed mine 6637231 1487681

Afzeliusgruvan Fe; REE; magnetite; closed mine 6657609 1502220

Vσggruvan Fe; REE; calc -silicate / skarn; magnetite; closed mine 6660033 1502297

Malmkärragruvan Fe; REE; calc -silicate / skarn; magnetite; closed mine 6660063 1502280

Lilla Fe; REE; calc -silicate / skarn; magnetite; closed mine 6660103 1502298 Malmkärragruvan

Johannagruvan Fe; REE; calc -silicate / skarn; magnetite; closed mine 6660347 1506062

Fallgruvan 4 Fe; apatite; felsic volcanic rock; calc apatite; magnetite; prospect 6663892 1455252 -silicate / skarn;

Fallgruvan 3 Fe; apatite; felsic volcanic rock; calc apatite; magnetite; prospect 6663900 1455264 -silicate / skarn;

Fallgruvan 2 Fe; apatite; felsic volcanic rock; calc apatite; magnetite; prospect 6663907 1455248 -silicate / skarn;

Haggruvan Fe; REE; gabbro; magnetite; closed mine 6664044 1532534

Skinnarbogruvan Fe; REE; iron formation; hematite; magnetite; closed mine 6664178 1532064

Förlikningsgruvan Fe; apatite; felsic volcanic rock; calc magnetite; prospect 6666856 1459151 -silicate / skarn;

Castorgruvan Fe; apatite; felsic volcanic rock; magnetite; prospect 6667049 1458509

Kaggruvan 3 Fe; apatite; felsic volcanic rock; calc magnetite; prospect 6667084 1459094 -silicate / skarn;

Polloxgruvan Fe; apatite; felsic volcanic rock; apatite; magnetite; prospect 6667176 1458620

Väsmanbacken apatite; Fe; calc -silicate / skarn; magnetite; prospect 6669576 1462203

Lekomberga 2 Fe; apatite; felsic volcanic rock; magnetite; prospect 6676283 1462995

Lekomberga 1 Fe; apatite; felsic volcanic rock; magnetite; prospect 6676309 1462993

Storfallsberget 2 Fe; Cu; Fe- calc -silicate / skarn; chalcopyrite; closed mine 6684988 1461248 sulphides; REE; Sn; magnetite; Bi;

Fäbodbacksgruva Fe; marble; marble; calc -silicate / magnetite; prospect 6689594 1459849 n 2 skarn;

Samuelsgruvan 2 Fe; marble; marble; calc -silicate / magnetite; prospect 6689697 1460026 skarn;

Holmtjärnsgruvan quartz; feldspar; pegmatite; closed mine 6693552 1469966 REE;

Alderbäcksgruvan Fe; apatite; biotite schist; magnetite; prospect 6695521 1468968 4

Alderbäcksgruvan Fe; apatite; amphibolite; magnetite; prospect 6695609 1468853 2

Alderbäcksgruvan Fe; apatite; dacite; pegmatite; magnetite; prospect 6695638 1468897 3

Stora Fe; apatite; calc -silicate / skarn; magnetite; prospect 6695968 1478687 Snöbergsgruvan 3 dacite;

Stora Fe; apatite; pegmatite; calc -silicate magnetite; prospect 6695974 1478643 Snöbergsgruvan 2 / skarn; granitic gneiss;

Stora Fe; apatite; pegmatite; granitic magnetite; Prospect 6695997 1478634 Snöbergsgruvan 1 gneiss; calc -silicate / skarn;

Södra Fe; apatite; calc -silicate / skarn; magnetite; Prospect 6696031 1477852 Stjerngruvan granitic gneiss;

Södra Fe; apatite; calc -silicate / skarn; magnetite; Prospect 6696065 1477869 Stjerngruvan 2 granitic gneiss;

Södra Fe; apatite; calc -silicate / skarn; magnetite; Prospect 6696070 1477911 Stjerngruvan 3 granitic gneiss;

Stora Fe; apatite; calc -silicate / skarn; magnetite; Prospect 6696070 1478611 Snöbergsgruvan 4 gneiss;

Nya Mσnsgruvan Fe; apatite; calc -silicate / skarn; magnetite; Prospect 6696132 1478213 granitic gneiss;

Snöberget 2 Fe; apatite; calc -silicate / skarn; magnetite; Prospect 6696351 1479179 quartz -feldspar gneiss;

Snöberget 4 Fe; apatite; calc -silicate / skarn; magnetite; Prospect 6696369 1479389 quartz -feldspar gneiss;

Snöberget 3 Fe; apatite; calc -silicate / skarn; magnetite; Prospect 6696385 1479255 quartz -feldspar gneiss;

Haggruvan 2 Fe; apatite; calc -silicate / skarn; apatite; magnetite; Prospect 6696939 1477074

Haggruvan 3 Fe; apatite; calc -silicate / skarn; apatite; magnetite; Prospect 6696963 1477063

Rista quartz; feldspar; pegmatite; closed mine 6713323 1457889 REE;

Kσrarvet (gamla) feldspar; quartz; pegmatite; closed mine 6721056 1487568 mica; REE;

Nya Kσrarvet quartz; feldspar; pegmatite; closed mine 6721325 1487160 brott 4 REE;

Nya Kσrarvet feldspar; quartz; closed mine 6721439 1486942 brott 1 mica; REE;

Nya Kσrarvet quartz; feldspar; pegmatite; closed mine 6721547 1487114 REE; mica;

Finnbo quartz; feldspar; pegmatite; feldspar; quartz; closed mine 6722482 1493719 pegmatitbrott REE;

4. CRITERIA FOR REE MINERALIZATION EXPLORATION IN BERGSLAGEN 4.1 GENERAL CLASSIFICATION OF DEPOSITS IN BERGSLAGEN They are several kinds of mineral deposits in the Bergslagen region and it is important that they are clearly stated to ensure correct selection of the exploration criteria for the chosen type deposit for this research. Stephens et al. 2009 classified these deposits into groups namely; Metallic mineral deposits, non-metallic mineral deposits, and bedrock deposits. The metallic mineral deposits is then sub-divided into the iron-manganese-tungsten deposits and the base metal-iron-sulphide deposits. For this predictive modelling of REE in the skarn iron-oxides, the metallic minerals and bedrock deposits are the target deposits as they consist of crystalline carbonate rocks and limestone which are rich in skarn ores.

4.2 CONCEPTUAL MODEL FOR BASTNÄS-TYPE DEPOSITS

Lithology Meta-volcanic intrusive/ felsic rocks, calc-alkaline meta-sedimentary rocks (1.9 – 1.87 Ga). Primary host rocks include Granitoids, granites, gneisses and marbles.

Mineralization Fe, Mg or Mn, Ca and K. Na enriched, also La, Ce and Y as well as Fluorine. Buffer distance proximity to REE mineralization in the skarn-ore deposits of the study area is calculated to visualize proximity to already existing REE mineralization.

Geophysics Magnetic field signatures that is; Low signature – felsic volcanic rocks and Strong signature – Intrusive rocks. Geochemistry Cu, Zn, Pb, Mg, Mo, B, Y, Ce, Cl, P, Hg and As are geochemical factors that may show strong anomalies or depletion that could in turn be a useful indication on REE mineralization.

Alteration Rocks are either enriched in – or depleted in- K, Na and Mg. Most of the iron-oxide deposits in Bergslagen are characterized by intense hydrothermal alteration. Hydrothermal alteration cause remobilization of elements, typically higher K, Ca, Mg, Fe, Mn, Rb, Th, Pd, Zn and lower Si, Ti, Al, Na, Sr, Y, Zr, Ba in intensely altered rocks (Ripa, 1996). REE deposits in Bergslagen are associated with enriched K, respectively

Mg enriched zones. Alteration intensity in respect of the Na2O content in lithogeochemical rock analysis may also provide useful information on alteration. Alteration will be modelled in the below listed categories: - Alteration Index - Carbonate-Chlorite-Pyrite Index

- Normalized K2O - Normalized MgO

- Normalized Na2O

- Normalized Zr/TiO2 ratio

- Normalized Na2O/K2O ratio

Heat source Metasomatism by hydrothermal fluids or other fluids such as silicate melts, carbon- dioxide rich and water-rich fluids. The syn-volcanic rock intrusion by plutonic rocks producing hydrothermal fluids. -Proximity to GSDG intrusive suite (ca 1.9 – 1.87 Ga)

5. SPATIAL EXPLORATION FACTORS To generate evidential maps for each recognition criterion, which includes ‘host rock lithology’, ‘proximity to heat source’, ‘proximity to favourable structures’, ‘geochemical criterion’, geophysical factors’ as well as ‘hydrothermal alteration’, the 44 classes of deposits were extracted and reclassified and then used as predictor maps representing each recognition criterion. This chapter therefore shows maps representing the various factors which predict favourability of the REE mineralization in the study area. 5.1 HOST ROCK FACTOR

Figure 17. Simplified bedrock map of North-West Bergslagen

The map of the host rock factor in figure 17 has been reclassified to show the major bedrocks of the study area and it is observed that the “Svecofennian volcanic and subvolcanic rocks (ca. 1.96- 1.86 Ga) and the Early Svecofennian intrusive GDG/GDSG rocks (ca.1.96-1.87 Ga)” appear as the favourable host rock types for the REE in the Bastnäs-type ore of the study area.

Plot 1. Chondrite-normalized REE values plotted against host rocks of North-western Bergslagen region.

Subsequently, from the provided data also all REE values were Chondrite normalized and plotted against rock types of the study area which is shown in plot 1. The Granodiorite, Granite, Tonalite as well as Diorite as displayed on this graph appear too as strong host factors for the REEs in this area. Although further emphasis will not be placed on the Chondrite-normalized REEs it however, has contributed in clearly displaying the intrusive rocks as host rocks for the REEs.

5.2 HEAT SOURCE FACTOR

Figure 18. Map of buffer distance from heat source

The granitoid-dioritoid-gabbroid (GDG) syn-volcanic intrusive rock suite of 1.96 – 1.87 Ga, has been shown as a favourable host rock factor for most of the rare-earth elements mineralization in the study area therefore the metagabbroid rocks which are a constituent of the favourable rock suite has been mapped to show spatially, its distribution in relation to the mineralization’s distance to the source of heat. The intrusive rocks tend to display greater abundance in the north- east areas of the study area and this primarily suggest most likely areas where hydrothermal convection could have developed.

5.3 GEOCHEMICAL FACTORS Geochemical data that have been used here include lithogeochemical and biogeochemical data. All the data presented here has been interpolated with the inverse distance weight (IDW) method and reclassified into ten (10) quantiles. This classification shows anomalies based on relative values rather than threshold values and the purpose of this is to identify the most prospective areas for a specific variable within the study area.

Till geochemistry data is available for most of the study area, and this is particularly useful in this study area where most of the supra-crustal rocks are covered by thick layers of till.

Figure 19. Map of till geochemistry showing the distribution of Cu anomalies and its relation to REE deposits across the study area.

“Till” means unsorted glacial sediments that mostly vary in composition. Regardless that till geochemistry anomalies may not be totally reliable since the till most commonly has been transported from the source mineralization, it is as important also as it models for transport direction and distance which may-be constructed by the display of the direction of glacial landscape features as well as the composition of the surrounding bedrock hence making it possible to trace potential source mineralization. For REE mineralization, anomalies of elements such as Cu, Zn, Pb, Fe, Ce which is a light REE (LREE) and Y a heavy REE (HREE) are strikingly useful to look out for as they stand as important key indicators pointing to potential prospectivity areas and these maps are shown in figures 19 through to 30.

Figure 20. map showing the Zn anomaly of till geochemistry in the study area.

Most of the high concentrated areas for Cu and Zn towards the north-eastern part of the study areas as observed in figures 19 and 20. Also, there seems to be a general positive correlation between these two anomalies and the REE mineralization in the north-western side of the study area. This is much more different however for the following elements in figures 21, 22 and 23 which are Fe, Y (HREE) and Mg respectively.

Figure 21: Map showing the till geochemistry of Fe in the study area.

It can be observed that these elements and the deposits correlates in the north-western and south-western areas of the study area. Unlike for the Cu and Zn anomalies, the correlation in the Fe, Mg and Y tends to be weaker in the north-, south- eastern corners of the study area.

Figure 22: Till geochemistry of Y in the study area

Figure 23: Till geochemistry of Mg in the study area

5.3.1 Biogeochemistry

Figure 24: Biogeochemistry of Cu in the study area.

The biogeochemical spatial evidence maps, Cu, Zn, Pb and MgO relativity to the REE mineralization is shown in figures 24, 25, 26 and 27 respectively. The Cu biogeochemistry map is observed to show smaller Cu content compared to the Zn, Pb and MgO where relatively high contents of the elements spread over a considerably larger area of the study area.

Figure 25. Map of biogeochemical Zn anomaly

The Zn biogeochemistry map seems to correlate most properly with the REE mineralization in the north-west section of the study area.

Figure 26. Map of Biogeochemical Pb anomaly

We could also clearly observe that the Pb and MgO biogeochemistry maps tend to show correlation between themselves in the south-west areas and this in turn could appear as a positive indicator for the biogeochemical factor in regards to particularly favourable areas in the study area.

Figure 27. Map of normalized MgO of study area

5.3.2 Lithogeochemistry For lithogeochemical spatial analysis, Cu, Ce, and Y have been chosen as key indicator elements. They have been chosen because aside from them being commonly occurring elements in REE deposits, they have also appeared to be the only elements to give significant correlation to the REE mineralization deposits for this study.

Figure 28. Map of lithological Cu of study area

The Cu Lithogeochemistry map in figure 28 along with the Ce and Y maps in figure 29 and 30 respectively, have been interpolated from sampling locations that are unevenly spread across the study area. The Cu map appears clearly to show a negative correlation with the REE deposits.

Figure 29. Map of lithological Ce of study area

This is apparently the reverse for Ce and Y as figure 29 and 30 shows that there is substantial correlation between the REE deposits and these minerals in the north-west locations of the study area.

Figure 30. Map of lithological Y of study area

5.4 STRUCTURAL FACTOR The fault lines of the study area have been divided into six categories to enable visualization of the structural trending and their relation to the REE deposits in the study area.

Figure 32. NW-SE trending Figure 33. SW-NE trending

Figure 34. ENE-WSW trending Figure 35. WNW-ESE trending

Figure 36. SSE-NNW trending structures Figure 37. SSW-NNE trending structures

Figure 32 shows the NW-SE trending, figure 33 the NE-SW, figure 34 and 35 shows the ENE-WSW and WNW-ESE trending respectively. The final two fault line trends are mapped in figure 36 and 37 with the SSE-NNW and SSW-NNE trending structures.

5.5 GEOPHYSICAL FACTOR The geophysical factor is quite limited in spatial evidence as it has only magnetic field data available for the course of this study. However, this data has proven to be much useful as magnetic anomalies have shown areas with relatively high magnetic strengths.

Figure 38 shows several occurrences of magnetic areas which indicates the abundance of magnetic minerals that most often are magnetite in intrusive rocks and pyrrhotite in Gneisses (Freden, 2002). The map shows spatial association with the REE deposits especially in the South- Western areas of the study area.

Figure 38. Magnetic field strength of the study area

5.6 ALTERATION FACTOR In large areas of the Bergslagen region, metasomatic, mostly syndepositional, hydrothermal alteration has occurred as they are related to much of the metallogeny (Andersson et. al, 2015). Spatial evidence for alteration factors will therefore be focused on metasomatic hydrothermal alteration since the ore forming fluids for the metals, are generated by hydrothermal fluids which have resulted in the alteration of the host rocks. This alteration results in either enrichment or depletion of the composition of different elements within the same rock type. Hence, alteration factor is evidence from various variables which could be used as indicators for hydrothermal alteration.

To showcase spatial evidence which will identify areas that have undergone intense hydrothermal alteration, the lithogeochemical data has been used. All data used have being normalized meaning that they have been adjusted to enable vivid identification of areas high in alteration.

The formula used to normalize the data was taken from Carranza, 2009. He gave the expression as;

훸ᵢ − µ 푆푡푎푛푑푎푟푑푖푧푎푡푖표푛: 푍ᵢ = [ ] σ

Where {훸ᵢ} = the value to be normalized

{µ} = the arithmetic mean of the distribution, and

{σ} = the standard deviation of the entire distribution.

Figure 39. Map of normalized MgO of the study area.

Indicator elements such as K2O, MgO, Na2O, Na2O/K2O ratio, TiO2/Zr ratio, alteration index and the Chlorite-Carbonate-Pyrite Index (CCPI) are the elements which have been normalized from lithogeochemical data that was provided by the SGU for this study.

Figure 40. Map of normalized Na2O of the study area

The alteration as shown in figures 39, 40, and 41 Respectively, is as a result of depletion or enrichment of one or more other elements indicating hydrothermal alteration. For Magnesium, it’s enrichment is indicative of hydrothermal alteration unlike for K2O and Na2O whose depletion tells a lot more of alteration in the area.

Figure 41. Map of normalized K2O

From the maps, it can be observed that the spatial association between the REE deposits of the study area and MgO in figure(MgO) shows a negative spatial association. On the other hand, for the K2O and Na2O it suggests that the spatial association is a positive one.

Figure 42. Map showing normalized ratio of Na2O/K2O in the study area

The map in figure 42 shows that there is a negative association between the REE deposits and the Na2O/K2O ratio. It is important to note that Na- metasomatism has generally postdated the K-metasomatism of this study area as the former seems to have been favoured more by stratigraphic depth or proximity to heat source such as intrusions (e.g Andersson et. al 2015, Lundström 1995, Hallberg 2003).

To identify hydrothermally altered areas, the alteration index is usually used and following Sadeghi (2008), the following formula has been used to calculate for the alteration index shown in figure 43 for the study area.

(100 ∗ (푀푔푂 + 퐾₂푂)) 퐴푙푡푒푟푎푡푖표푛 퐼푛푑푒푥 (퐴퐼) = (푀푔푂 + 퐾2푂 + 퐶푎푂 + 푁푎2푂)

Figure 43. Alteration index of the study area.

Figure 44. Map showing the CCPI of the study area

The CCPI has also been calculated to estimate hydrothermal alteration for this area as it is used most often in geochemical analysis of alteration. This has been done using the following expression (after, Sadeghi, 2008).

(100 ∗ (퐹푒푂 + 푀푔푂)) 퐶퐶푃퐼 = (퐹푒푂 + 푀푔푂 + 퐾₂푂 + 푁푎₂푂)

Figure 45. Map showing normalized Zr/TiO2 ratio of the study area.

Zr/TiO2 ratio has been calculated for this study because Zr and Ti are elements that are relatively immobile during hydrothermal alteration and from practice, they have been tagged as the most reliable immobile elements (Sadeghi, 2008).

The map in figure 45 has hence displayed an overall negative spatial association in relation to the REE deposits.

6. PROSPECTIVITY MODELING For the predictive prospectivity modeling for this thesis, the weight of evidence method is the modeling method used to generate the mineral potential maps. Various weight tables have been produced and significant results will be presented in the following tables (3). Where N{B} is the number of pixels in binary pattern, W+ means the weight due to the presence of a map feature, W- means the weight due to the absence of a map feature, C is the spatial Contrast, S(W+), S(W- ) and S(C) then means the standard deviation of W+, W- and C respectively and the final WofE parameter Stud C stands for studentized contrast which is basically the one parameter that sets calculated evidential parameters within an acceptable range.

Table 4: Calculated weights for predictor variables of REE

Spatial Evidence CLASS N {B} Points W+ W- S(W+) S(W-) C S(C) Stud_C Weight

Bedrock Svecofennian acidic volcanic rocks 3 5959 19 0.5993 -0.2755 0.2298 0.1926 0.8748 0.2998 2.9178 0.5993 Acidic intrusive rocks 4 3019 15 1.0447 -0.2728 0.2588 0.1797 1.3175 0.3151 4.1811 1.0447

Proximity to Heat source <20th percentile 2 4108 13 0.5917 -0.1621 0.2778 0.1742 0.7537 0.3279 2.2986 0.5917

Biogeochemical anomalies Cu biogeochemistry <60th percentile 7 637 4 1.2799 -0.0665 0.5016 0.1544 1.3464 0.5248 2.5654 1.2799 >80th percentile 10 99 6 3.6042 -0.1362 0.4212 0.1582 3.7404 0.4500 8.3127 3.6042

Zn biogeochemistry <60th percentile 6 1313 9 1.3687 -0.1667 0.3345 0.1645 1.5353 0.3728 4.1188 1.3687 <70th percentile 7 916 9 1.7318 -0.1825 0.3350 0.1645 1.9142 0.3732 5.1292 1.7318 <90th percentile 10 236 6 2.6988 -0.1309 0.4135 0.1582 2.8298 0.4428 6.3909 2.6988

Pb biogeochemistry <30th percentile 9 2463 18 1.4330 -0.3984 0.2366 0.1891 1.8314 0.3029 6.0470 1.4330 <40th percentile 10 2453 10 0.8458 -0.1472 0.3169 0.1668 0.993 0.3581 2.7729 0.8458

Till geochemical anomalies Fe <20th percentile 1 2241 5 1.2934 -0.2851 0.4477 0.3016 1.5785 0.5398 2.9242 1.2934 <40th percentile 3 3608 7 1.1533 -0.4268 0.3783 0.3334 1.5800 0.5043 3.1333 1.1533

Cu <10th percentile 1 7475 24 0.6063 -0.4028 0.2045 0.2133 1.0090 0.2955 3.4148 0.6063 >40th percentile 8 141 4 2.8125 -0.0857 0.5073 0.1544 2.8982 0.5302 5.4658 2.8125

>60th percentile 9 47 2 3.2267 -0.0427 0.7226 0.1509 3.2694 0.7382 4.4290 3.2267

Zn <20th percentile 1 1983 4 1.1975 -0.2092 0.5005 0.2887 1.4067 0.5778 2.4345 1.1975 <40th percentile 3 4424 6 0.7998 -0.2854 0.4085 0.3163 1.0852 0.5167 2.1004 0.7998

Mg <20th percentile 1 2325 10 0.8999 -0.1526 0.3169 0.1668 1.0525 0.3581 2.9389 0.8999 <30th percentile 2 2783 10 0.7193 -0.1332 0.3168 0.1668 0.8525 0.3580 2.3811 0.7193 <40th percentile 3 2548 12 0.9909 -0.2004 0.2894 0.1716 1.1913 0.3364 3.5412 0.9909

Ba <40th percentile 3 3342 11 0.6313 -0.1373 0.3020 0.1692 0.7685 0.3462 2.2202 0.6313 <70th percentile 6 3513 16 0.9572 -0.2841 0.2506 0.1827 1.2413 0.3101 4.0029 0.9572

Y <30th percentile 1 2466 13 1.1041 -0.2338 0.2781 0.1742 1.3379 0.3281 4.0771 1.1041 <40th percentile 2 2672 14 1.0979 -0.2559 0.2680 0.1769 1.3538 0.3211 4.2161 1.0979

La <20th percentile 1 2502 13 1.0894 -0.2322 0.2781 0.1742 1.3217 0.3281 4.0278 1.0894 <30th percentile 2 2652 13 1.0311 -0.2259 0.2780 0.1742 1.257 0.3281 3.8312 1.0311 <40th percentile 3 2788 10 0.7174 -0.1330 0.3168 0.1668 0.8503 0.3580 2.3751 0.7174

K <40th percentile 3 4324 19 0.9137 -0.3517 0.2299 0.1926 1.2654 0.2999 4.2192 0.9137

Ca <40th percentile 3 6198 14 0.7070 -0.3816 0.2136 0.2042 1.0886 0.2955 3.6838 0.7070

Na <40th percentile 3 9841 32 0.6191 -0.7203 0.1771 0.2674 1.3393 0.3207 4.1763 0.6191

Lithogeochemical anomalies Fe_Total <20th percentile 1 1432 9 1.2810 -0.1618 0.3344 0.1645 1.4428 0.3727 3.8716 1.2810 <40th percentile 2 3134 10 0.6002 -0.1181 0.3167 0.1668 0.7183 0.3580 2.0065 0.6002 <70th percentile 8 1747 9 1.0809 -0.1490 0.3342 0.1645 1.23 0.3725 3.3019 1.0809

Cu >40th percentile 1 6839 28 0.8502 -0.6370 0.1894 0.2358 1.4873 0.3024 4.9176 0.8502

Y >60th percentile 9 2623 16 1.2509 -0.3226 0.2508 0.1827 1.5735 0.3103 5.0716 1.2509

>70th percentile 10 2618 15 1.1877 -0.2899 0.2589 0.1797 1.4777 0.3152 4.6880 1.1877

Ce <80th percentile 10 2583 13 1.0576 -0.2288 0.2781 0.1742 1.2864 0.3281 3.9206 1.0576

Alteration Index <30th percentile 1 2456 9 0.7389 -0.1196 0.3339 0.1645 0.8585 0.3723 2.3060 0.7389 <50th percentile 4 2642 20 1.4686 -0.4651 0.2245 0.1962 1.9336 0.2981 6.4857 1.4686

K2O <30th percentile 2 2626 15 1.1851 -0.2896 0.2589 0.1797 1.4747 0.3152 4.6787 1.1851

MgO <10th percentile 1 2755 5 1.0917 -0.2639 0.4476 0.3016 1.3555 0.5397 2.5115 1.0917

Na2O >20th percentile 1 530 3 1.1749 -0.0471 0.5790 0.1526 1.222 0.5988 2.0409 1.1749 >60th percentile 7 4963 18 0.7286 -0.2872 0.2361 0.1891 1.0157 0.3025 3.3576 0.7286 >70th percentile 9 1540 6 0.8006 -0.0794 0.4090 0.1582 0.88 0.4386 2.0065 0.8006 >90th percentile 10 469 8 2.2909 -0.1733 0.3566 0.1623 2.4642 0.3918 6.2891 2.2909

Chlorite-Carbonate-Pyrite Index (CCPI) <10th percentile 1 2382 9 0.7698 -0.1227 0.3340 0.1645 0.8925 0.3723 2.3973 0.7698 <30th percentile 3 2658 16 1.2376 -0.3211 0.2508 0.1827 1.5587 0.3102 5.0240 1.2376

Na2O_K2O ratio Did not show any positive spatial correlation with the training deposits

Geophysical anomaly Magnetic Field >90th percentile 10 1097 9 2.6035 -0.7842 0.3347 0.3780 3.3877 0.5049 6.7096 2.6035

Structural Trends ESE-WNW 4 2618 5 1.1424 -0.2697 0.4476 0.3016 1.4120 0.5398 2.6161 1.1424 WSW-ENE 2 2694 27 1.7514 -0.7768 0.1934 0.2295 2.5281 0.3001 8.4231 1.7514 NNE-SSW 7 2448 10 0.8476 -0.1474 0.3169 0.1668 0.9950 0.3581 2.7785 0.8476 >90th percentile 9 2574 18 1.3887 -0.3937 0.2365 0.1891 1.7824 0.3028 5.8860 1.3887 NNW-SSE 2 3187 17 1.1160 -0.3323 0.2432 0.1858 1.4483 0.3060 4.7322 1.1160

The predictor variables that have been presented in table 3 are those that are considered as favorable being that they have satisfied the confidence level which was set at 2.0 for the weight of evidence calculations. This clearly means that the studentized contrast of each variable had to be >2.0 to be considered as favourable indicating that the confidence level is >98%.

6.1. SPATIAL ASSOCIATION ANALYSIS In this chapter, the spatial association between significant mineral occurrences and geological features in the chosen study area will be analyzed based on both the qualitative and quantitative knowledge that have been presented in the previous chapters of this thesis. The weight of evidence model has produced statistically, results showing quantified spatial associations between mineral deposits and geological features and this has enabled insight in terms of which geological features are significant predictors of mineral deposits (Sadeghi, 2008).

In line with this therefore, predictor variables that have displayed particularly high studentized contrast values are those which have been chosen for further analysis in this chapter.

6.1.1 SPATIAL ASSOCIATION WITH HOST ROCK The spatial association with the known base metals recognized to be hosting the REE metals and the bedrock of the study area, has shown strong associations with two (2) particular lithologies namely, the acidic volcanic rocks and the acidic intrusive rocks both of Svecofennian age. This is displayed in the favourability map (figure 46) of the bedrock of the study area.

The acidic intrusive rocks, constitutes of rocks such as Granites, Granodiorite and Monzonite while the acidic volcanic rocks include Rhyolite and Diorite. Though, it can be observed from table 3 that the acidic intrusive rocks displayed statistically higher studentized contrast which interprets to have a confidence level much greater than that of the acidic volcanic rocks, the later has a stronger spatial association to the mineral occurrences when compared to the other as it is host to more than 70% of the mineral deposits.

Figure 46. Favourable host rocks in the North-western Bergslagen area.

6.1.2 SPATIAL ASSOCIATION WITH HEAT SOURCE With the understanding that the metagabbroid rocks are rocks with much higher temperature in the GDG intrusive suite, the proximity to the heat source for this base metals have been assessed by analysis of the relativity of the known deposits and their proximity to the heat source. This was done, by calculating the proximity buffer distance while putting into consideration both prior and posterior probability to produce the map in figure 47. The map shows that there is a relatively weak spatial association between the heat source proximity of up to 6.9km and the deposits.

Figure 47. Map of heat source in the study area showing favourable and non-favourable areas of heat source.

6.1.3 SPATIAL ASSOCIATION WITH GEOCHEMICAL ANOMALIES Data was provided as well as analyzed for three (3) kinds of geochemical anomalies namely, till geochemistry, biogeochemistry and Lithogeochemistry. Geochemical anomaly maps for several elements typical for REE deposits such as Cu, Zn, Pb, Fe, Ce, La, Y, K, Na and Ca were produced by interpolation using the IDW tool and then re-classified to 10 integer values based on percentile. However, after calculating the weight of each of these elements using the spatial data modelling (SDM) tool, a few of them showed really high studentized contrast values compared to the average of them all. These few special elements are those which will be presented in this chapter.

Figure 48. Map showing biogeochemical Cu anomaly favourable and non-favourable areas.

Figure 49. Map showing biogeochemical Zn anomaly in study area

Figure 50. map showing Pb biogeochemical anomaly favourable areas in study area.

Figure 51. Map showing favourable and non-favourable areas of Ce in study area

Figure 52. Map showing favourable and non-favourable areas of Y in study area

Figure 53. Map showing favourable and non-favourable areas of Cu lithogeochemical anomaly in study area

6.1.4 SPATIAL ASSOCIATION WITH GEOPHYSICAL FACTOR The geology of the study area has been analyzed to consist of intrusive rocks making them the most common host rock in the Bergslagen area and it is of common knowledge that these rock types are known significantly to constitute high magnetic minerals such as Magnetite. On the other hand, rocks such as the gneisses have Pyrrhortite as its common magnetic mineral. Overall,

78 these magnetic minerals are very useful as they aid in identifying strongly magnetic areas and these findings are tools needed in prospectivity mapping.

Gravity survey of the area which stands to be another important tool in prospectivity mapping, shows the specific gravity of various rock types, their mineral composition as well as porosity. The red on the map therefore indicates favourable magnetic areas in the study area.

Figure 55. Map showing favourable and non-favourable areas of magnetics in study area

6.1.5 SPATIAL ASSOCIATION WITH STRUCTURAL FACTOR

Figure 56. Structural favourability in the study area.

The REE mineralization has shown more positive spatial association to the NE-SW deformation structures in comparison to all controls carried out on structural trends in the study area. This can be seen in the buffer proximity map in figure 56 as >60% of the Bastnäs (skarn) ore deposits falls within favourable structures of the study area.

7. DISCUSSION AND VALIDATION Multi-class maps were produced for both geochemical (Cu, Zn, Pb, Mg, Fe and Y) and alteration

(MgO, K2O and Na2O) predictor maps. These were then combined to the binary predictor maps of Host rock, heat source, structural trending and regional magnetics to produce the weighted overlay map. The Posterior probability, standard deviation and confidence level for this map were then calculated using the ‘Calculate response tool’ in the Spatial data modeler tool box in ARCGIS. The posterior probability values are classified in terms of favourability for potential mineralization and the map in figure 57 is produced.

Figure 57. Continuous-scale posterior probability map derived from combining weights using Calculate Response tool.

Bonham-Carter (1994) however, is one among other researchers who have emphasized the importance of acknowledging that there is always possibility of artificial inflation or deflation of the posterior probability as a result of a certain degree of conditional dependence amongst the predictor maps. Hence a conditional independence (CI) test is needed to quantify the degree of conditional dependence of these predictor variables.

7.1 CONDITIONAL INDEPENCE TEST The Conditional independence test of Agterberg & Cheng is to estimate the probability for a given combination of predictor variables to be conditionally dependent of the other and this is expressed by the equation;

{T-n}/Tstd

Where ‘T-n’ shows the difference between the expected number of training deposits and that of the observed deposits. Results of conditional independence test carried out on some combination of predictor variables is shown in table 5. The ‘Overall CI’ is where the probability for a given combination of predictor variables is estimated in percent (Wareing, 2007). From the table, only two combinations namely; ‘Host rock + Zn biog’ and Host rock + Cu till violates the CI test as they both showed probabilities lower than 50%.

Table 5. Table showing results of conditional independence test.

Predictor Overall CI Observed Expected Difference(T-n) Standard CI ratio (T-n)/Tstd variables (%) No. of TP No. of TP deviation (n/T) (%) Host rock + 84.2 44 42.3 -1.7 8.640 1.04 42.1 magnetics Host rock + Cu 88.5 44 45.2 1.2 8.389 0.97 55.7 biog Host rock + Zn 31.1 44 49.9 5.9 6.293 0.89 82.5 biog Host rock + Cu Till 45.3 44 49.7 5.7 7.580 0.89 77.4 Host rock + La 62.9 44 48.9 4.9 10.060 0.9 68.5 Host rock + Mg 99.9 44 44 0 7.113 1 50.1 Host rock + 89.7 44 44.8 0.8 6.293 0.98 55.1 alteration Host rock + 78.6 44 45.6 1.6 5.962 0.96 60.7 magnetics + multi- class (Geochem, alteration) + WSW structural trend

The column titled ‘Observed no. of training point (TP)’ represents the training deposit set used for the prospectivity mapping. The ‘Conditional independence ratio is another important column to understand because values below 1.00 could indicate conditional dependence among two or more of the combined data while values <0.85 may indicate that there is a problem ((Bonham- Carter, 1994). With this assumption, we can discuss that from the table of CI test, only “host rock + magnetics” and “host rock + mg” showed conditional independence as they had CI ratio values >1. However, this result is likely for predictor maps of rocks of volcanic or intrusive origin as they always strongly correlate with deposits.

7.2 WEIGHTED OVERLAY MINERAL POTENTIAL MAP The weighted overlay operation was carried out by attributing different individual weights to values depending on their estimated relative importance in accordance to the weight tables which could be seen as ‘data driven’ as well as in accordance to literature review which is ‘knowledge driven’. Beginning with the host rock which was assigned the highest value as the most important predictor variable, followed by the magnetics, multi-classed geochemical and alteration variables and then the structural (WSW) predictor variable all following in descending order. The outcome of this map is what is shown as the ‘Mineral prospectivity potential map’ and this is shown in figure 58. It has been divided into 5 segments ranging from very low to very high.

Figure 58. Mineral potential map of REE in Bastnäs (skarn)-type ore deposits in North-Western Bergslagen region.

7.3 CONCLUSION The results have shown that by the reduction of the study region through statistical analysis and overlay of weights of selected predictor variables, exploration targets can be identified for the purpose of mineral exploration. An area is considered an exploration target when a substantive portion of that area displays high prospectivity potential and the two final maps in figures 57 and 58 shows the Riddarhyttan–Norberg area as one of the particularly interesting exploration target areas. The posterior probability map from figure 57 have been edited and these possible exploration targets have been rounded out. The light coloured pink circles highlight significant exploration targets with the presence of deposits especially historical deposits such as Riddarhytte mälmfältet, Storfallsberg and Bjursön.

On the other hand, the area circled with the darker pink, though has no known deposits could be considered as a particularly interesting exploration target as it has shown to be a significantly high zone of potential mineralization. Thus, this area was closely looked into and has been identified and zoomed out in the map in figure 59 as the Risberget, Mejdåsen and Lövberget region situated in top north-west Bergslagen. Where the Risberget and Lövberget areas have well known mining history of iron ores such as copper.

Figure 58. Map highlighting exploration target areas generated from the posterior probability map of predictor variables.

Figure 59. Map identifying the area that displayed highest posterior probability(PP) in the map in figure 58. Source: SGU and Lantmäteriet.

Furthermore, this area on the posterior probability (PP) map has also been identified as constituting the same volcanic rock type of Svecofennian origin as that of the rock types where deposits already exist and this correlation has been signified by the blue squared-shaped box in the lithologic map of the study area.

Figure 60. Comparison between the original regional bedrock map of the study area and the final Posterior Probability map of predictor variables.

Hence relating to the initial aim of this thesis which was to simply distinguish particular prospective areas for Bastnäs (skarn)-type REE mineralization within the study area, it can therefore be said that the conclusion reached is more of a relative one rather than absolute as proposed target areas are not totally decisive but totally open for discussion and more research in the future.

ACKNOWLEDGEMENTS First, my absolute thanks goes to God almighty for granting me the strength to work through and complete this task despite all the challenges.

I acknowledge with thanks the EURARE project and Geological Survey of Sweden for supporting the dataset and geological information regarding the REE mineralization in Bergslagen. The EURARE project is funded by the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 309373.

I express my deepest gratitude to my supervisor at the Geological Survey of Sweden (SGU), Dr. Martiya Sadeghi for without him this entire thesis wouldn’t be. Also, for his guidance, the training he gave me on mastering the SDM program used in this project, his encouragement, suggestions and his very constructive criticism which contributed immensely to the evolution of my ideas in this project.

I cannot express enough thanks to my professor and supervisor at the University of Gothenburg, Dr. Thomas Zack for his continued support, encouragement and the learning opportunity he provided which helped to mold me to go beyond my limits and get the best.

My sincere gratitude also goes out to a dear and true friend, Emma-Karolina Grandberg for her support and contributions.

Deeply pained but with earnest appreciation, I honour and acknowledge my Dad Mr. Amos Obotuke of blessed memory who passed-on the same day I received confirmation to defend my thesis, for his total believe in me to not just finish but finish well. I also express my heartfelt thanks to my Mum Mrs. Elizabeth Obotuke, your encouragement when the times got rough are very much appreciated.

To my family, my strength and inspiration, my backbone and life support whenever I found myself at the edge – to my beloved husband Mr. Eddy Agbonifo and my priceless children Joy, Jemima, Jeremy and Juanita Agbonifo. You’re all the reason I kept pushing forward, I love you all so much!!

Ejiro Obotuke-Agbonifo. REFERENCES Agterberg, F.P & Cheng, Q. (2002) Conditional Independence Test for Weights-of- Evidence Modeling, Natural Resources Research Vol.11 Issue 4 (2002---12) pp. 249---255

Allen, R. L., Lundström, I., Ripa, M., Simeonov, A., and Christofferson, H., (1996). Facies Analysis of a 1.9 Ga, Continental Margin, Back-Are, Felsic Caldera Province with Diverse Zn-Pb-Ag-(Cu- Au) Sulfide and Fe Oxide Deposits, Bergslagen Region, Sweden. Economic Geology, Vol. 91, pp. 979 – 1008.

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