Mineral prospectivity analysis for BIF iron deposits: A case study in the Anshan-Benxi area, Liaoning province, North-East China

Accepted Manuscript Mineral prospectivity analysis for BIF iron deposits: A case study in the AnshanBenxi area, Liaoning province, North-East China Jiangning Yin, Mark Lindsay, Shouren Teng PII: DOI: Reference:

S0169-1368(17)30561-9 https://doi.org/10.1016/j.oregeorev.2018.11.019 OREGEO 2746

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Ore Geology Reviews

Received Date: Revised Date: Accepted Date:

22 July 2017 21 December 2017 17 November 2018

Please cite this article as: J. Yin, M. Lindsay, S. Teng, Mineral prospectivity analysis for BIF iron deposits: A case study in the Anshan-Benxi area, Liaoning province, North-East China, Ore Geology Reviews (2018), doi: https:// doi.org/10.1016/j.oregeorev.2018.11.019

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Mineral prospectivity analysis for BIF iron deposits: A case study in the Anshan-Benxi area, Liaoning province, North-East China Jiangning Yina,*, Mark Lindsayb, Shouren Tengc a

MLR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral

Resources,CAGS,Beijing, 100037,China b

Centre for Exploration Targeting, The University of Western Australia, 35 Stirling Highway,

Crawley, WA 6009, Australia. c

Geological survey institute of Liaoning province,Liaoning,230005,China

E-mail address: [email protected] ABSTRACT Banded iron formation (BIF) deposits are the most important source of iron in China, and they are dominantly distributed in the North China Craton (NCC). The Anshan-Benxi area is situated in the northeastern part of the NCC and is considered to be the most fertile and productive iron metallogenic province in China. The recent successive discoveries of large BIF deposits in this area, especially the world-class Dataigou BIF deposit, indicates that there is great potential for iron resources in this area. The BIF deposits are well defined by magnetic and gravity anomalies and thereby extensive petrophysical and geophysical studies are critical for BIF prospective mapping. A data-driven weights-of-Evidence (WofE) analysis was used for BIF prospectivity modeling. The results indicated that prospective areas are generally confined to the Precambrian greenstone belt basin and high-prospectivity zones (Class A) are mainly distributed in the middle and northeastern parts of the study area. Moderate-prospectivity zones (Class B) are mainly distributed in southwestern and northeastern parts of the study area. Finally, we employed

selective human intervention to redefine priority targets to be more reliable. The results suggest the method is successful as 82 target areas were narrowed to Class A 20 and 11 Class B targets. The total of Class A and Class B targets was just over one-third of the total number of targets, offering a much smaller but effective selection of targets for consideration by mineral explorers. Keywords: Banded iron formation (BIF) Weights-of-evidence North China Craton (NCC) Anben area

1. Introduction

Precambrian banded iron formations (BIFs) are economically important sedimentary rocks and constitute a principal source of iron for the global steel industry (Bekker et al., 2010; Beukes and Gutzmer, 2008; Gross, 1991, 1993, 1996; Hagemann et al., 2008; James, 1954; Klein, 2005; Thurston et al., 2008). The BIFs were divided into Algoma type and Lake Superior type based on the characteristics of their depositional environments and the types of associated rocks (Gross, 1980). The Algoma type BIFs are relatively small in size and thickness, and are associated with volcanic and greywacke rock assemblages along volcanic arcs, rift zones, deep-seated faults and fracture systems, whereas the Lake Superior type BIFs were deposited with quartzite, dolomite and black shale in relatively shallow marine environments under more stable tectonic conditions, over a passive margin continental-shelf environment (Gonzalez et al., 2009; Gross, 1980). In general, though not always, the former is commonly of Archean age and the latter is of

Palaeoproterozoic age with considerably more iron (Gross, 1980, 1983). Precambrian banded iron formations (BIFs) are the most important source of iron in China, forming >50% of the total iron reserves in China (Li et al., 2014). The BIFs with total iron between 20% and 50% are commonly utilized as iron ores in China, and magnetite is the main ore mineral (Li et al., 2014). Nearly two-thirds of the iron ores in China are hosted in Algoma-type BIFs, characterized by low-grade (ca. 30% total iron) and coarse-grained magnetite ores (Li et al., 2012a; Li and Li, 2013). Most of the BIF deposits formed in the Neoarchean, with a peak at ~2.5 Ga (Dai et al., 2012, 2013; Li et al., 2014; Shen et al., 2006; Wan et al., 2012a; Zhang et al., 2011; C.L. Zhang et al., 2012b; Zhang et al., 2014), and are dominantly distributed in the North China Craton (NCC) (Li et al., 2014; Zhang et al., 2014 ). The Anshan-Benxi (Anben) area is situated in the northeastern part of the NCC and is considered to be the most fertile and productive iron metallogenic province in China (Wang et al., 2016; Zhang et al., 2014; Li et al., 2014). The Algoma-type BIFs in the Anshan area are usually called Anshan-type BIFs (Li et al., 2014; Shen, 2012; Zhao et al., 2004), which contain more than a quarter of the total iron resource in China (H.M. Li et al., 2014a; Li et al., 2015a). Many large iron deposits (>1000 Mt) have been discovered in the Anben area in the past 50 years, such as Qidashan, Dagushan, Donganshan, Yanqianshan, Nanfen, Waitoushan, Beitai and Gongchangling (Fig. 1b) (Zhang et al., 2014; Li et al., 2014; Li et al., 2016). In recent years, several new large iron deposits have been discovered (e.g. Dataigou, Jiucaigou and Xujiapuziand) and substantial iron resources have been identified at depth in Hujiamiaozi, Heishilazi and Mining area II of the Gongchangling iron deposit in the Anshan area (Li et al., 2014; Li et al., 2016; Zhang et al., 2014 ). Among them the most important discovery is the concealed world-class Dataigou BIF deposit

with a proven reserve of N5.0 Gt iron and an average grade of 35% (Hong et al., 2010; Teng et al., 2013). Those remarkable discoveries indicate there is great iron resource potential in the Anben area. The main objective of this study was to map prospective areas for BIF deposits in the Anben area as decision-support tools to aid targeting of new mineral deposits. Mineral potential mapping (MPM) is a multi-step process for generating and combining evidential maps, and finally ranking promising target areas for further exploration (Bonham-Carter, 1994; Carranza, 2010). Firstly, we collected good quality exploration datasets with consistent coverage over the study area. We then conducted extensive metallogeny study of BIF to understand its ore-forming processes and ore-controlling factors. Since BIF is well defined by magnetic and gravity anomalies, we performed extensive geophysical interpretations and anomalies extraction related to BIF mineralization. We

used a data-driven weights-of-evidence (WofE) analysis for prospectivity

modeling. Finally, we employed selective human intervention to redefine priority targets to increase reliability of the output.

2. Region and local geology

The NCC is one of the oldest cratonic blocks in the world, containing rocks as old as ~3.85 Ga (Liu et al., 1992; Song et al., 1996; Liu et al., 2008). The NCC can be divided into Eastern and Western blocks with the intervening Trans-North China Orogen (Zhao et al., 2005, Fig. 1a). The Eastern Block consists predominantly of Neoarchean rocks, whereas minor Eoarchean to Mesoarchean tonalite-trondhjemite-granodiorite (TTG) gneisses, metasedimentary rocks and amphibolites have been found only in the Anben and Eastern Hebei areas (Huang et al., 1986;

Jahn et al., 1987; Liu et al., 1992; Nutman et al., 2011; Song et al., 1996; Wan et al., 2005; Wu et al.,

2008;). The Neoarchean rocks are composed mainly of 2.55–2.50 Ga TTG gneisses and

supracrustal rocks, with minor 2.8–2.6 Ga TTG gneisses and ~2.5 Ga charnockites and syenogranites (Wu et al., 1998; Li et al., 2010; Wan et al., 2011). The supracrustal rocks are present as bands, pods and enclaves within the TTG gneisses. The BIFs are extensively distributed in the metamorphosed Archean units of the NCC, and are interlayered with supracrustal volcano-sedimentary rocks in greenstone belts (Zhai and Santosh, 2013). Please insert Figure 1 here. The Anben area is located in the northeastern part of NCC, covering an area of ~7500 km2 (Fig. 1b). It comprises Eoarchean to Mesoarchean rocks, Neoarchean plutons and supracrustal rocks, many of which are unconformably covered by the Paleoproterozoic Liaohe Group (Fig. 1b). These Archean rocks units include: (1) Eoarchean Baijiafen, Dongshan, Shengousi and Guodishan gneisses previously dated at ca. 3.77–3.81 Ga (Liu et al., 1992; Song et al., 1996; Wan et al., 2005; Liu et al., 2008; Wu et al., 2008; Wan et al., 2012a; Zhang et al., 2013; Y.F. Wang et al., 2015); (2) Paleoarchean gneissic trondhjemites, migmatites and porphyritic to fine-grained granites dated at ca. 3.45–3.30 Ga with associated Chentaigou supracrustal rocks (Song et al., 1992; Zhou et al., 1994; Wan et al., 2012a; Wang ,C.L. et al., 2015; Wang, Y.F., et al., 2015); and (3) Mesoarchean trodhjemitic gneisses and granites dated at ca. 3.1–2.9 Ga (Song et al., 1996; Wang et al., 2015; Y.F. Wang et al., 2015). The Anshan basement can be divided into three parts: Tiejiashan gneiss, Anshan gneiss and Anshan supracrustal rocks (Zhai and Windley,1990). The Tiejiashan gneiss occurs at the Tiejiashan area, east of the Anshan city, forming the basement of the Anshan supracrustal rocks.

The Anshan Group is strongly deformed, and they have been intruded and fragmented by the precursors of granitic gneisses (the Anshan gneisses) with the result that the supracrustals now occur as enclaves of variable size (Fig. 1b) (Zhai and Windley, 1990). Within the area there are also some intrusive Cretaceous granites belonging to the Yanshanian period (Zhai and Windley, 1990). Rock types of the Anshan Group comprise plagioclase amphibolites, leptynites, schists, migmatites, BIFs and others including siliceous rocks and carbonates (Zhou, 1994).

2.1. Stratigraphy for mineralization

BIFs occur widely within the Neoarchean supracrustal succession of the Anshan Group (Fig. 1b). The rock associations of Anshan Group consist of amphibolites, leptynites, schists, migmatites, BIFs and other rock types including siliceous rocks and carbonates (Zhou, 1994). In general, there is a close association between amphibolite and BIF (Zhai and Windley , 1990). The Anshan Group is divided into lower, middle and upper parts, accordingly called the Cigou Formation, Dayugou Formation and Yingtaoyuan Formation from bottom to top. Of these, BIFs mainly occur in the Yingtaoyuan Formation and Cigou Formation (Zhou, 1994). The Yingtaoyuan Formation is dominated by terrigenous sedimentary rocks, the Cigou Formation is dominated by basic volcanic rock, and the Dayugou Formation is in the transition zone between the two (Fig. 2). Please insert Figure 2 here. The age of BIFs in Anshan area is constrained by various methods. Zhong (1984) obtained a Rb–Sr isochron age of 2.82 Ga., and Song et al. (1996) reported a zircon U–Pb age of 2.92 Ga for the Tiejiashan gneiss, and a zircon U–Pb age of 2.47 Ga for the Qidashan granitic gneiss (the Anshan gneiss). The latest SHRIMP zircon U–Pb dating of magmatic zircons in metamorphic

rocks from three BIF mine fields of Qidashan, Yanqianshan and Donganshan in the Anben area constrains the peak BIF-deposition age at 2.56–2.53 Ga (Li et al., 2016). It has been roughly deduced that the age of the Anshan supracrustal rocks must be between 2.5 and 2.9 Ga according to geochronology studies. Since plagioclase amphibolites are generally closely related to the BIFs, the age of the protolith of plagioclase amphibolites must closely represent the age of the BIFs (Zhang et al., 2011). Qiao et al. (1990) reported the Sm–Nd isochron age of 2729 ± 245 Ma and 2724 ± 102 Ma from plagioclase amphibolites of the Anshan Group which showed large margins of uncertainty. Recently, Wan et al. (2012b) reported a zircon U–Pb age of 2528 ± 10 Ma of leptynites collected from the BIF interlayer, indicating that the formation age of the BIFs is the late Neoarchean.

2.2. Features of Anshan-type iron ore

The BIFs in Anben contain huge reserves of iron ore, which are mainly low grade (20–40% total Fe) with some high grade (>50% total Fe) areas (Li et al., 2015; Zhang et al., 2014 ). The high-grade ores are usually enclosed within the thick-layer low grade orebodies, and controlled by faults and folds, especially in the axis of synclines. Usually BIF deposits contain several layers of ore. The orebodies are stratiform, lenticular, up to 200–300 m thick, hundreds to thousands of meters long, and extend to hundreds to a few thousands of meters in depth. The orebodies show evidence of multiple and intense deformation (Zhang et al., 2014). Iron minerals are dominated by oxides with minor silicate or carbonate ore, a stratiform or irregular orebody, and a general close association with meta-basalt and meta-sediment caused by metamorphism of amphibolite facies (Zhai and Windley, 1990). However, the lithologies in Anshan area and Benxi areas have distinct

characteristics in terms of rock assemblages, metamorphic grades and degrees of deformation (Zhou, 1994). Lithological suites in the Anshan area mainly consist of metasedimentary rocks (e.g., mica–quartz schist, phyllite and quartz chlorite schist) and large-scale BIFs (e.g., Qidashan, Dong'anshan and Dagushan). These contain minor meta-volcanic rocks (e.g., amphibolite, biotite and leptynite), which have generally undergone upper greenschist facies metamorphism, but locally up to lower amphibolite facies. In contrast, the Benxi area contains a volcanic-dominated assemblage consisting mainly of amphibolites, biotite leptynites and amphibole schists with lesser amounts of mica schists and chlorite schists, which were metamorphosed to amphibolite facies. In addition, relatively small-scale BIFs exposed in Benxi (e.g., the Waitoushan, Nanfen and Beitai BIFs) were interlayered with mafic meta-volcanics and displayed strong deformation (Dai et al., 2012).

2.3. Regional metamorphism

Since the metamorphic event at Archean/Proterozoic has been recognized across large tracts of the NCC, it appears to be of wide regional significance (Grant et al., 2009; Yang et al., 2008; Zhang et al., 2011, 2012b). The Anshan Group supracrustal rocks have undergone varying degrees of metamorphism (Li et al., 2014, 2015; Zhai and Windley, 1990; Zhang et al., 2014). In general, the grade of metamorphism increases from greenschist facies in the west of the area to amphibolite facies in the east, while the retrograde metamorphism decreases from the west to the east (Zhai and Windley, 1990; Zhou, 1994). The protoliths are argillaceous and silty rocks with minor basic to acid volcanic rocks (Zhou, 1994). Biotite gneiss and plagioclase amphibolite-magnetite-quartzite formations are mainly distributed around Gongchangling,

Waitoushan-Beitai and Nanfen. Metamorphic grade ranges from high greenschist facies to low amphibolite facies. Based on the major host rocks of the BIFs, the following five rock associations characterize the deposit geology (Zhang et al., 2012b): 1) amphibolites (or hornblende plagioclase gneiss) and magnetite quartzite; 2) amphibolites, biotite leptynite, mica-quartz schist and magnetite quartzite; 3) biotite leptynite (or biotite-quartz gneiss) and magnetite quartzite; 4) biotite leptynite, sericite-chlorite schist, biotite-quartz schist and magnetite quartzite association; and 5) amphibolites (gneiss), marble and magnetite quartzite association. The protolith of the host rocks are interpreted to be three main types: 1) mafic volcanic rocks (tholeiitic)-dominated with pelitic-arenaceous rocks; 2) sedimentary rocks intercalated with volcanic rocks; and 3) tuffaceous rocks-bearing sedimentary succession (Yang et al., 2008; Zhang et al., 2011). In general, the degree of metamorphism is closely related to the formation of the BIFs. The Paleoarchean and Mesoarchean BIFs generally experienced granulite facies metamorphism, and the Neoarchean BIFs have undergone amphibolite-facies metamorphism. The post-Paleoproterozoic BIFs metamorphism is only up to the lower greenschist facies (Fig. 2) (Zhang et al., 2000).

2.4. Ore-controlling structures

The generally poor exposure makes structural interpretation difficult in the Anben area (Zhai and Windley, 1990). Liu et al. (1984) suggested that (1) the Tiejiashan gneiss suffered two phases of deformation in an early tectonic cycle with vertical movements and the Anshan Group underwent four phases during a later horizontal compressive regime; (2) the earliest deformation affecting the Tiejiashan gneiss was ductile, producing amphibolite-facies gneiss; and (3) the first deformation affecting the Anshan Group produced a gneissic foliation and macroscopic folds, and

later fold phases produced a variety of refolded structures. Refolded minor folds are common in the Anshan Group, and many mines display isoclinal and tight folds (Zhai and

Windley, 1990).

Early prospectors summarize prospecting general rules of Anshan type BIFs as “synclinal ore control” and “refolded ore control” (Yao et al., 1993). Please insert Figure 3 here. Two main fault systems developed in Anben area striking NE and NNW respectively (Fig. 3), which are Hanling faults (NE), Pianling faults (NE), Sishanling fault (NE), Tangheyan-Nanfen faults (NNW), Sandaoling-chenjialingzi faults (NNW) and Huangnigang faults (NNW) (Yao et al., 2014). The Hanling and Pianling faults control the geological architecture of the Anben area and their movement has an important influence on the spatial distribution of BIFs as well as other geological entities such as Archean, Proterozoic and Mesoarchean strata and magmatic activities (Zhang et al., 2004). The Hanling–Pianling strike-slip faults are affiliated to the Tan-Lu Fault Zone, which is one of the largest crustal faults in east China. The Hanling–Pianling fault zone almost traverses the entire Anben area, extending west from the south of Anshan City, across Dagushan Mountain, Guanmenshan Mountain, Gongchnagling Town, the Benxi iron ore fields, Hanling county, Beitai and Benxi cities, and ending at the town of Pianling. At the southern end of the fault zone, due to sinistral tensile stress, a secondary fault developed paralleled with the Hanling–Pianling fault, attached to Sishanling fault zone, extending from Gongchangling, across Dataigou and Sishanling to Renjiapuzi, then crosscut by late north-west fault (based on Exploration Report of Dataigou and Sichanling Iron Deposit, 2015). Such fault systems are most widely observed in many sectors of the ore cluster recorded by the footprint of sinistral movement resulting from compression and fault scissoring near Gochangling, Beitai, Sishanling, and the

Benxi ore field. Massive thrust nappe structures extensively developed in Nanfen, Diaoyutai, Beitai, and in the northeast of Benxi, which were likely to be caused by stress field of strike-slip (based on the Exploration Report of Dataigou and Sichanling Iron Deposit, 2015). Most orebodies in the Anben ore cluster show dominant northwest trends, consistent with the trending direction of magnetic anomalies. However, in Gongchangling, Dagushan, Beitai, orebodies of NW and SE border were crosscut by subordinate northeast faults of the Hanling–Pianling faults. The BIF-hosted iron ore system is structurally controlled, mostly via km-scale normal and strike-slip fault systems, which allow large volumes of ascending and descending hydrothermal fluids to circulate during Archean or Proterozoic deformation or early extensional events (Hagemann et al., 2016). In the Anben area, BIF mineralization also has a close relation with faults, especially high-grade iron ores. Though the genesis of high-grade iron is still controversial in the area (Sun et al., 2014a; Wang et al., 2014), the high-grade iron ore usually occurred within or near the fault, or within fold gaps (Zhou, 1994), indicating clear structurally controlled characteristics (Sun et al., 2014b; Wang et al., 2014). Previous studies have suggested that metamorphic hydrothermal fluid produced during the regional metamorphism stage was rich in iron, and it was transferred along the reverse fault to form the high-grade iron ores (Guan, 1961; Shi and Li, 1980; Zhou, 1994).

3. Geophysical interpretation

This task involved the recognition and delineation of areas with distinctive or clearly defined anomaly patterns on magnetic and gravity maps and the explanation of these in terms of their possible causative geological bodies and structures (Castro et al., 2014). The results of the

geophysical interpretation can be applied for subsequent BIF prospective analysis. The delineation of geophysical domains was based on information about the main geological units and structures of the outcropping basement.

3.1. Geophysical data and processing

Geophysical datasets including ground-based Bouguer gravity data with a 2 km × 2 km spatial resolution and airborne total magnetic intensity data at a scale of 1:100000 were obtained from China Geological Survey (CGS). The airborne magnetic survey covered Liaoning province and was flown along N–W flight lines spaced every 500 m with flight elevation ranging from 100 to 150 m above the topographic surface. Free-air and Bouguer gravity corrections were made using the Chinese height standard of 1985 as a datum and 2.67 g /cm−3 as a reduction density. The radius was 166.7 km for terrain corrections (RCMRAP, 2013). The gravity data were gridded using the minimum curvature method (Briggs, 1974) with a cell size of 500 m and contoured to produce a Bouguer gravity anomaly map (Fig. 4a). The same procedure was carried out to deal with aeromagnetic data to produce a total magnetic intensity map (Fig. 5a). The aeromagnetic anomalies were reduced to the pole (RTP) using magnetic inclination 59° and declination −8.6° given by International Geomagnetic Reference Field (IGRF) . The RTP transformation was applied to center the magnetic anomalies over their causative features, making the interpretation easier and more reliable (Damaceno et al., 2017). Both aeromagnetic data and gravity data were upward-continued to various heights, ranging from 1 to 10 km, to enhance long wavelength (deep-seated) anomalies. Using the method described by Boyce and Morris (2002), the residual gravity was separated from the regional field by subtracting the gravity data

upward-continued to some height from the original gravity data. Other common approaches to analyze potential field data are derivatives where either vertical derivatives, horizontal derivatives or some combination of both (e.g., analytic signals), which are applied to better locate density contrasts or magnetic bodies in the subsurface (Verduzco et al., 2004). Both vertical and horizontal derivatives are useful but can provide different information about the density or magnetic sources because horizontal derivatives are better suited to locate or enhance the edge of a body (Blakely and Simpson, 1986; Cordell, 1979). Conversely, vertical derivatives, including first vertical derivatives (FVD) and second vertical derivatives (SVD), are useful in narrowing the width of the anomalies so that the source body can be better located. Additionally, the zero lines of the SVD can determine the boundaries of the gravity or magnetic bodies (Cooper and Cowan, 2004; Marson and Klingele, 1993).

3.2. Geophysical properties of rocks and tectonic unites

Magnetic susceptibility and density are the physical properties of rocks required to constrain the modeling of potential field data and interpretation of gravity and magnetic data (William et al., 2013; Zeng, 2013). Thus, more than 700 rock samples of sedimentary, metamorphic and magmatic rocks were measured to obtain their magnetic susceptibility and densities by the Liaoning Geological and Mineral Survey Institute (Table 1). Table 1 shows that the magnetic susceptibility of different rocks in the study area varies greatly. The average magnetization susceptibility of Archean rocks is 2910 × 10−5 (SI) and Archean magnetite quartzite rocks can reach 147980 × 10−5 (SI). Paleoproterozoic rocks from the Liaohe Group have an average magnetization susceptibility of 1978 × 10−5 (SI). Thus, Archean crystalline

basement and Paleoproterozoic strata present a strong magnetic anomaly and have a large magnetic background that is still strong even excluding magnetic quartzite rocks and magnetite (Cui et al., 2014; Fan et al., 2014). Magnetization susceptibility has been reported to be weak in other rock units. There are significant differences of magnetization susceptibility between Archean strata and other rock units and consequently the latter usually tends to exhibit low–moderate amplitude magnetic anomalies. In addition, the magnetization susceptibility shows a general increasing trend with stratigraphic chronology from young to old (Cenozoic to Archean). Upper Proterozoic, Paleozoic, Mesozoic and Cenozoic rocks, excluding iron-bearing rock sequences, are generally magnetically weak. Sedimentary rocks dominated by carbonate and clastic rocks always have weak- to non-magnetic susceptibility and consequently represent low and negative magnetic anomalies. Mafic-ultramafic volcanic rocks including pyroclastic rocks show strong magnetism. Due to the heterogeneities of ferromagnetic substances, the magnetic amplitude of volcanic rocks changes rapidly and produces high magnetic anomalies. Intrusive rocks are distributed widely in Anshan area and magnetization susceptibility of these intrusive rocks varies considerably. In general, mafic-ultramafic rocks, intermediate, and intermediate-basic rocks, containing more mafic rock show positive or high positive anomalies, whereas acidic and alkaline rocks exhibit a low negative magnetic field. Similar to magnetization susceptibility, the density also displays an increasing trend with stratigraphic chronology from young to old (Cenozoic to Archean) (Table 1). The density contrast between the Archean strata and its covering strata is more than 0.10 g/cm3, generating an up to 1.2 mGal positive gravity anomaly. The average density of basic rocks is 2.96 g/cm3, intermediate rocks (e.g., diorite) is 2.80 g/cm3, intermediate- and intermediate-felsic is 2.60 g/cm3 and alkaline

rock (e.g., syenite) is 2.57 g/cm3. The density of intrusive rocks decreases from basic, to intermediate to felsic and consequently produces low to middle gravity anomalies. Density of migmatite including migmatitic granite ranges from 2.39 to 2.96 g/cm3 and its average is 2.60 g/cm3 (the same as felsic rocks). The density of magnetite and magnetite quartzite rocks can reach a maximum of 4.0 g/cm3 (3.8 g/cm3 on average) with 0.5–1 g/cm3 higher density than wall rocks, producing prominent positive gravity anomalies.

3.3. Gravity interpretation result

The Bouguer gravity image (Fig. 4a) shows, in the central parts of the study area, a prominent and broad NNE trending gravity high flanked by gravity lows along boundaries characterized by steep gradients. The amplitude of the central gravity high and the gradients of the peripheral gravity lows show a steady decline from the north to the south. The remarkable high gravity anomalies are roughly parallel to the crustal Tan-Lu fault. The NE oriented prominent anomalies separate the Anben area into three regional domains, denoted as A, B and C (Fig. 4a). This pattern is enhanced in the 10 km upward-continued gravity images (Fig. 4b). However, the pattern is only faintly discernible in the FVD residual gravity image (Fig. 4c), in which the central gravity high is replaced by a largely low-residual gravity matrix ingrained with bands of high residual gravity. Based on the geological studies, the B domain is located in a Precambrian greenstone belt covered by Paleozoic, Mesozoic and Cenozoic sequences with migmatitic granite intrusions as stated above. Because upward-continuation and FVD filters enhance long wavelength (deep-seated) and short wavelength (near-surface) anomalies, respectively (Porwal et al., 2006), two explanations can be surmised about the nature of the central gravity high (B domain). First, the source of

central gravity high is a high-density body at deeper crustal levels; and second, near-surface sources do not contribute significantly to the central gravity high, indicating the presence of a giant Archean metamorphic crystalline basement emplaced at upper crustal levels, which might provide sufficient mineral sources for BIF deposition. In the northwest part of Anben area (the A domain), the gravity anomalies exhibit a NE trend containing alternating positive and negative anomalies with 3.6 mGal magnitude, which is the lowest gravity field in Anben area. These gravity lows are located in a Xialiaohe depression region and might be caused by lighter sources such as sedimentary basins or granitic rocks and/or low-grade metasedimentary sequences. Therefore, the A domain is not suitable for prospecting BIF. In the southeast part of the study area (C domain), the gravity anomalies are irregular, represented by middle-high positive gravity anomalies striking northwest and significant negative anomalies in the east and southern part of the study area. It was inferred that the negative field of the southern part of the study area was induced by intrusive rocks and the positive field was caused by Archean metamorphic rock and granitic gneiss. Please insert Figure 4 here. A horizontal derivative filter (HDR) can highlight anomalies of certain directions and the response of its wave number can enlarge the high frequency component of the X axis (Guan, 2005). In addition, the HDR of different continuation heights can qualitatively reflect lineament depths. Therefore, we conducted HDR at different angles of gravity after 1000 m upward continuation, which reflects lineaments in different directions. When interpreting lineaments, correlations of different directions and continuation heights must be considered to make a comprehensive conclusion. After 1000 m continuation to attenuate short wavelength anomalies

caused by shallow disturbances, the deep causative bodies would be highlighted with an SVD filter. From the resultant Fig. 4d, it can be seen that most of the iron occurrences fell in the zero lines of SVD, indicating a close spatial association between the BIF deposits and zero lines of SVD.

3.4. Magnetic interpretation result

The RTP anomaly map (Fig. 5b) shows that the study area is dominantly positive magnetic with minor areas of negative values, indicating a high magnetic background. The high positive magnetic anomalies (>1000 nT) perfectly match with iron occurrences (magnetite). However, the overall RTP anomalies were irregular and undistinguishable. Therefore, we conducted upward continuation of 1000 m to filter the short wavelength disturbance caused by shallow features (Fig. 5c). Subsequently, insignificant patterns disappeared and some significant anomalies are highlighted. The shape of the magnetic anomalies mostly elongated to oval and some anomalies are almost round, which can be apparently identified three prominent positive magnetic domains, denoted as A, B and C in Fig. 5c. Domain A is in the southwest corner of Anben area near Qiaotou, where some small BIF occurrences occurred. Domains B and C have the most remarkable circular magnetic highs in the Anshan-Liaoyang-Gongchangling-Beitai-Waitoushan region of Anshan area, where the Archean strata outcrops widely with high magnetic susceptibility. However, the magnetic intensity is not weakened too much and the high anomaly in A-B-C domain is still obvious even when upward continued to 10 km (Fig. 5d), indicating a giant magnetic basement blocks at depth, where many big BIF deposits occurred such as Waitoushan, Jiajiapuzi, Maerling, Yanlongshan, Jiguanshan and Mianhuapuzi. The negative anomaly is mainly distributed in

southwest and east part of Anshan area, which can be explained by intrusive rocks or sedimentary sequences with low magnetic susceptibility (Fig. 6a). Similar to gravity data processing, HDR was performed to extract structural lineaments. The FVD and SVD techniques were performed to guide the location boundaries of magnetic bodies. As shown in Fig. 6c, SVD can depict the edges of magnetic bodies and most of the BIF deposit fell in the SVD anomalies, which provides a good prediction layer for BIF prospective mapping. Please insert Figure 5 here. Please insert Figure 6 here. The lineaments interpreted by gravity and magnetic can be finally synthesized to form one comprehensive lineament structures map (Fig. 6d). Combined with the gravity interpretation result, Archean crystalline basement blocks may be present beneath the upper crust, characterized by high gravity and a high magnetic anomaly. The boundary of basement blocks can be defined by gravity and magnetic anomalies. The Archean basement blocks have a close relation with the large scale of BIF deposition. We presume that all large BIF deposits are produced in or close to the basin.

4. Mapping mineral prospectivity

4.1. Ore-controlling factor recognition

Recent studies have concluded that the ore-forming materials of Algoma-type BIFs were derived from volcanic hydrothermal fluids, with contributions from mafic and ultramafic rocks, in a marine environment (Bekker et al., 2010; Bolhar et al., 2004; Derry and Jacobsen, 1990; Frei et al., 2008; Graf, 1978; Isley, 1995; Jiang et al., 1992; Kato et al., 1998; Li et al., 2008; Tang et al.,

2013; Zhang et al., 2011; Zhang et al., 2012a, b). According to Hagemann et al. (2016), Algoma-type BIF iron ore has the following critical elements: it is hosted in granite–greenstone belts; its hydrothermal fluid flow is controlled by greenstone belt or basin-scale structures, such as strike-slip or normal faults or graben structures; and it is deposited in metamorphosed volcanic sedimentary sequences. Combined with previous studies related to BIF mineralization of the Anshan area, the following characteristics were used to recognize the key recognition criteria for exploration targeting of BIF deposits in the study area: presence of ore-bearing strata (Anshan Group), presence of or proximity to faults/fractures and structure lineaments derived from geophysical interpretation, and gravity and magnetic signatures as recognition proxies for magnetite quartzite rocks.

4.2. Mineral deposit dataset

A mineral deposit dataset comprising geological and grade-tonnage attributes of 64 major BIF deposits of Anshan area was extracted from a proprietary mineral occurrence database owned by Liaoning Geology Survey Institute.

4.3. Favorable strata

The geological studies described in previous sections show that the favorable strata of BIF in Anben area is mainly within the Neoarchean Anshan Group, which not only provides mineral resources but also traps for BIF mineralization. The favorable strata of Anshan Group were extracted from a 1:200000 scale geology map provided by the China Geology Survey (Fig. 7). Please insert Figure7 here.

4.4. Geophysical layers

Based on regional and deposit geophysical studies in Anben area, BIF deposits show close spatial correlations with gravity and aeromagnetic SVD anomalies and most of the BIF deposits were in the SVD anomalies. Therefore, we selected SVD anomalies of both gravity and aeromagnetic as geophysical layers for BIF prospective mapping.

4.5. Favorable structures

The favorable information extraction for faults includes two aspects: first, we directly extracted information from the geological map, and second, we deduced faults in the study area, which were obtained from a geological interpretation of the gravity and magnetic anomalies. We used the buffer analysis function in the GIS software to establish tectonic buffer zones. Most of the known BIF deposits occurred within 2 km of faults, indicating that a 2-km buffer of faults can be selected as the favorable tectonic buffer zone (Fig. 8). Please insert Figure 8 here.

5. Prospective mapping for BIF deposits

5.1. WofE approach

WofE modeling is a data driven method, based on a Bayesian conditional probability framework, for mineral-potential mapping.

Since WofE was first introduced into mineral

potential mapping by Bonham-Carter et al. (1988) and Agterberg et al. (1992), it has been applied widely for mapping mineral prospectivity (Agterberg, 1989a; Bonham-Carter, 1994; Carranza, 2004; Cassard et al., 2008; Porwal et al., 2010).

WofE modeling requires the assumption of conditional independence (CI) among predictor maps with respect to prospects, which is one of the most important issues to be considered in WofE applications, to avoid bias in the obtained results. There are many CI tests, such as pairwise G2 and χ2 tests (Bonham-Carter et al., 1989; Agterberg, 1992), the omnibus test (Bonham-Carter, 1994), and the new omnibus test (Agterberg and Cheng, 2002; Thiart et al., 2006). If the CI requirement cannot be met, it is necessary to explore ways to reduce the effect of conditional dependence. To date, many improvements have been made to the WofE method to produce unbiased estimates by relaxing the requirement of conditional independence or reducing the data redundancy. For example, the Tau model (Journel, 2002), weighted WofE and stepwise WofE (Agterberg, 2011; Deng, 2009; Krishnan et al., 2005; Polyakova and Journel, 2007; Zhang et al., 2009). Chen (2008) proposed a convenient method to adjust the posterior probability based on grouping the known mineral deposits. More recently, Cheng (2015) proposed a new Boost WofE method, which showed significant reduction of these effects. In addition, some hybrid methods have been developed, such as fuzzy WofE, and modified fuzzy WofE (Porwal et al., 2003; Zhang et al., 2012;Zhang et al.,2014). WofE modeling of mineral potential is generally a 3-stage process: (1) estimation of prior probability (Pprior) of prospects; (2) estimation of weights of spatial evidence with respect to prospects; and (3) updating of the WofE prior by weights of spatial evidence to estimate posterior probabilities of prospects. WofE modeling is usually applied to map mineral potential in areas with a large number of deposits or prospects because it has been demonstrated to be effective in mineral-potential mapping in areas with a large number of training points (e.g., Bonham-Carter, Agterberg, and

Wright, 1989; Paganelli, Richards, and Grunsky, 2002; Raines and Mihalasky, 2002; Porwal, Carranza, and Hale, 2003). Since there are so many known BIF deposits in the Anben area, WofE modelling is ideally suited for prospecting mapping for BIF. The study area was divided into 2500 cells with 3 km × 3 km unit cell size, each of which generally satisfied the assumption in prospectivity mapping that each mineral deposit occurrence is contained in only one unit cell (Carranza, 2009). Verification of conditional independence between the input layers was performed using a pairwise χ2 test. Based on the results of the χ2 test, the calculated χ2 values were not more than theoretical values of chi-square distribution with one degree of freedom (3.841) at the level of  = 0.05. Thus, all four layers passed the CI test. Finally, the four predictor maps were integrated by using WofE to estimate the posterior probabilities for each cell and produce a final favorability map, showing the potential areas of BIF mineralization of the study area. Please insert Figure 9 here.

5.2. Prospective results

The output map of posterior probabilities is classified subsequently to delineate zones with high-favorability, moderate favorability, and low-favorability for BIF deposits, denoted as A, B and C class respectively. For classifying the favorability map, the plot of cumulative area versus posterior probability curve was prepared (Fig. 9). Based on this graph three thresholds were determined using the inflection points, which are 0.05, 0.169, 0.634, to separate low (0.05–0.169), moderate (0.169–0.634) and high favorability (>0.634) classes from the background (<0.05). From the prospective map we can see that prospective areas are generally confined to the

Precambrian greenstone belt basin and most known BIF deposits fall in the Class A or Class B zones (Fig. 10). High-prospectivity zones (Class A) are mainly distributed in the middle and northeastern part of the study area and middle-prospectivity zones (Class B) are mainly distributed in southwestern and northeastern parts of the study area. Please insert Figure 10 here. Please insert Figure 11 here. In order to evaluate the performance of the model, capture-efficiency curves (or fitting-rate curves, Fabbri and Chang-Jo, 2008; Porwal et al., 2010) were used to depict the cumulative proportion of known deposits captured by a prospectivity model in cumulative proportions of the study area. The higher the curve above the diagonal line, the better the performance of the prospectivity model. Figure 11 shows that the model yield capture-efficiency curve is apparently above the diagonal line, indicating that the model captured the training deposits efficiently. From Table 2 we can see that both Class A and Class B zones occupy about 4.32% of the total area but contain 90% of the known BIF deposits, indicating that the result has a high prediction accuracy.

6. Discussion

6.1. Evaluation of the importance of evidence layers

The aim of the investigation is to explore the relative importance of the recognition criteria represented by a predictor map and infer which of them should be given priority for BIF exploration. In WofE modeling, an area can be divided into a number of N{T } equal-area unit cells, of which there are N{D} cells each containing one and only one deposit D. Suppose that in the area there are N{B} and N{B} pixels where spatial evidence B is present and absent, respectively. A positive ( W  ) weight and a negative ( W  ) weight, which are assigned to B and

B,

respectively, can be estimated from likelihood ratios as: W   log e

W   log e

N B

D / N D

N B

D /N D



(1)

  

  N B D / N D

N B D / N D

(2)

The contrast value C (where C  W   W  ) measures the degree of correlation between a set of spatial evidence and a set of prospects; C > 0 if spatial association is positive, C < 0 if spatial association is negative, and C = 0 if there is no spatial association. In addition, the Studentized C ( s(C ) ) was proposed as a useful guide for quantitatively estimating the statistical significance of spatial association between prospects and evidence layers (Agterberg and Bonham-Carter, 2005; Agterberg et al., 1990; Bonham-Carter, 1994; Bonham-Carter et al., 1989; Carranza, 2004). On the basis of a Bayesian conditional probability framework, the Studentized C value can be defined as: t

C  s(C )

(3)

C 



s (W )  s (W ) 2

2

where s 2 (W  ) and s 2 (W  ) are the variances of W  and W  respectively. They can be estimated as: s 2 (W  ) 

1 1  N ( B D) N ( B D)

(4)

s 2 (W  ) 

1 1  N ( B D) N ( B D)

(5)

Estimates of values of Studentized C > 1.96, suggests a statistically significant contrast at α = 0.05 under some conditions (Bonham-Carter, Agterberg, and Wright, 1989). Table 3 shows that the s(C ) values of four evidence layers are larger than 1.96, implying a statistically significant spatial correlation between point events and a map pattern. Empirically, the larger the Studentized C, the stronger the spatial correlation. The Anshan Group have the largest C and s(C ) value, indicating a strong spatial association with BIF deposits and it is no doubt that the Anshan Group represents the most important prediction layer. The large C and s(C ) value of

faults confirmed the importance of structural control on BIF mineralization, as stated in forgoing sections. In addition, magnetic anomalies showed better spatial association than gravity anomalies, which may be because the gravity data are at a scale of 1:200000, whereas magnetic data are at 1:100000, possibly causing some pseudo-gravity anomalies. Nonetheless, the comprehensive application of magnetic and gravity method proved to be a powerful tool to explore BIF deposits characterized by high density and high magnetic susceptibility. The results of the spatial association analyses indicate that strata and structures are effective spatial predictors, which are the most important ore-controlling factors for BIF mineralization. In other words, the BIF mineralization will not occur without the Anshan Group in the Anben area. The evidence layers of gravity and magnetic anomalies are not the main ore-controlling factors and less important than the two geological factors, but they provide important geophysical signatures for BIF exploration. Although the presence of the Anshan Group is considered to be a key exploration criterion for BIF deposits in the Anshan area, about 15% of the Anshan Group is mapped as unprospective by the models and around 10% of high prospectivity zones contain no Anshan Group (Fig. 8). The implication is that the Anshan Group cannot be used in isolation to select exploration targets. Although the Anshan Group represents an important source and carrier rock of BIF, the BIF mineralization might not occur if other critical components, particularly favorable structures, are not present.

6.2. Problem of asymmetric information

Asymmetric information (AI) is the specialization and division of knowledge or information (Jesen,2008). It is common in society, which means that one party in the mutual relationship has

more information than the other party. In certain circumstances, asymmetric information may lead to adverse selection and it is one of the causes of uncertainty in decision making (Jesen,2008;Vanek and Botlik,2013;Barakat et al., 2014). AI also exists in MPM practice and it tends to be greater in those areas where information is complex, difficult to obtain or both. For instance, it is relatively difficult to obtain large information especially in the areas where the target geology is poorly exposed or under cover, or where relevant datasets are not available, spatial information is limited, or input data are inconsistent or contain errors (Porwal and Kreuzer, 2010), but it is relatively easier in the areas where the exploration activities have been extensively carried out. Under this situation, the AI problem arises and it may lead to adverse or spurious targets selection. In order to deal with the problems, we tried to use selective human intervention (SHI) approach to refine the prospective areas to be more reasonable. In general, the prediction result in Anben area is satisfactory and middle-high prospective area is consistent with favorable regional metallogenic conditions. However, some target areas appear to be unreasonable. For instance, Wangjia village in Yingkou and Gaotai village in Haicheng have an obvious magnetic anomaly and are considered to have a high probability to contain BIF deposits according to local geologists. However, the two areas were grouped into the Class C by the model (Fig. 10). Moreover, there are several target areas in which the magnetic anomaly is not obvious, but they are regarded as prospective areas. Therefore, the problematic target areas need to be corrected empirically by geologists. After correction and selection, we output 82 target areas (Fig.12), 20 of which were considered Class A, 11 Class B, and 51 Class C. The final targets are more convincing and reliable to guide follow-up exploration activities. Please insert Figure 12 here.

Given that the human mind is capable of lateral thinking (De Bono, 1970), it is able to discern invisible ,missing or error information more efficiently than a computer based on their personal experience and intuition. Hence, we think SHI is effective to refine prospective areas after automated prospectivity analysis. However, the human brain can not handle complex situations without simplifying them (e.g., Simon, 1983). Furthermore, this process can lead to heuristics and may introduce significant bias and errors of judgment (Kreuzer, et al.,2010) (e.g., Tversky and Kahneman, 1974; Kahneman, 2003; Bond et al., 2007). In our opinion, computer automated approaches and SHI are complementary to address each other's limitations essentially. Therefore, it is important to harness the strengths of both manual and automated approaches in exploration decision-making (such as target area selection). It should be noted that SHI is not a necessary step in MPM As the objective of any exploration program is to identify prospective areas, the target validation is critical and should be carried out during active exploration follow-up.

7. Conclusions

Regional metallogeny study and mineral system analysis show BIF is structurally controlled and the Anshan Group represents the main host strata of BIF in the Anshan area. Analysis of aeromagnetic and gravity anomalies indicate that the Anshan area can be divided into several tectonic domains, which correlate broadly with lithostratigraphic belts. It is presumed that Archean crystalline basement blocks may be present beneath the upper crust, which have a close relation to the high production of big BIF deposits in the Anshan area. Based on geophysical interpretation, geophysical anomalies and lineaments were extracted as predication layers for BIF

prospective mapping. Results of spatial association analyses suggest that the presence of the Anshan Group represents the most important prediction criterion followed by structure, which is another important ore-controlling factor. Furthermore, regional-scale Bouguer gravity and aeromagnetic data may also be useful for the identification of the favorable region to explore for potential BIF deposits, especially in the Anben area under cover. Due to asymmetric information, it is essential to undertake artificial correction and selection to redefine the target area to prevent the geologists from being biased by the outputs of the automated prospectivity analyses. In summary, an important consideration for this type of metallogenic analysis is whether the product is useful to mineral explorers. If the analysis produces many targets, it may appear to be a success, but this is not useful for the explorer as they need to select a specific target area to invest time and money to find a deposit. Thus, a method that can effectively filter the less prospective regions from the analysis are desirable and effectively reduce the area to be considered for exploration (Lindsay et al., 2016; Occhipinti et al., 2016). The results obtained here suggest that the presented method successfully achieved this objective, as 82 target areas were narrowed to 20 Class A and 11 Class B targets. The total of Class A and Class B targets is just over one-third of the total number of targets, offering a much smaller, but effective selection of targets for mineral explorers to consider.

Acknowledgments

Special thanks are extended to the Geological survey institute of Liaoning province for their support in field work and providing raw data. We appreciate Shouren Teng, Xiaojuan Wang for

helping processing data and graph. This research benefited from the joint financial support from the National Key Research and Development Program of China (2017YFC0601501), National Mineral Resources Assessment Project (41372007), Quantitative Assessment Scheme of Major Metals (1212011120140), Basic Research Fund for Public Welfare Institute(K1419) and Project 2006BAB01A01 from the National Key Technology Research and Development Program of the Ministry of Science and Technology.

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List of Figures Fig. 1. (a) Subdivisions of the North China Craton (NCC) showing location of the Anben area and distribution of major BIFs (modified from Zhao et al., 2005; Zhang et al., 2012a); (b) Geological sketch map of the AnBen area indicating study area (modified from Wang et al., 2016). Fig.2 Stratigraphic section of metamorphic formation related to BIF in Anshan area (modified from Zhang (2014)). Fig.3 Main faults system of Anben area (modified after Zhang et al., 2010). Fig.4. (a) Bouguer gravity anomaly map of the study area. The thin white lines represents the North-East trending crustal Tanlu fault zone;(b) Upward continuation of the Bouguer gravity field to 10 km;(c) First vertical derivative of residual gravity;(d) Second vertical derivative of residual gravity. Fig.5. (a) Maps of the Total Magnetic Intensity (TMI); (b) Magnetic anomaly by RTP transformation ; (c) Upward continuation of RTP anomalies to 1000m; (d) Upward continuation of RTP anomalies to 10Km. Fig.6. (a) Upward continuation of RTP anomalies to 1000m.Thin red lines represent intrusive rocks derived from geology map; (b) FVD amomalies after 1000m upward continuation; (c) SVD amomalies after 1000m upward continuation ; (d) Regional-scale structural lineaments interpreted by gravity and magnetic data. Fig. 7 Prediction layer of Anshan Group Fig.8 Prediction layer of favorable fault buffer Fig.9 Plot of cumulative area(Y-axis) versus posterior probability (X axis)

Fig. 10 Prospective map of BIF created by automated prospectivity analysis Fig.11 Plot of capture-efficiency curves Fig.12 Prospective map of BIF modified by SHI approach

Fig. 1.

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

proportion of deposits

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0

0.2

0.4

0.6

proportion of area

Fig.11

0.8

1

Fig.12

Table 1 The density and magnetic properties of rocks in Anben area Magnetic Susceptibility Rock types Age

-5

Remnant Polarization

（10 SI）

Density 3

-3

(10 A/M）

(g/cm )

Range

Average

Range

Average

Range

Average

Volcanic breccia

2-12.1

4.1

0-13.4

3.1

1.64-2.44

2.05

Olivine basalt

2.83-5.950

204

21.2-1240

1240

2.20-2.85

2.56

Sandstone

N.A.

N.A.

N.A.

N.A.

2.46-2.63

2.55

Tuff

0-187.1

26

0-98.9

82.5

2.36-2.75

2.6

Andesitic tuff

30-2254.2

230.4

31.7-4215.3

829.4

2.40-2.72

2.57

Basaltic tuff

0.832-459.3

846

200-3270

639.5

2.48-2.71

2.62

Rhyolite

120-1.500

0.138

N.A.

N.A.

2.46-2.68

2.62

Sandstone

N.A.

N.A.

N.A.

N.A.

2.61-2.80

2.7

Limestone

N.A.

N.A.

N.A.

N.A.

2.61-2.79

2.71

Phyllite

N.A.

N.A.

N.A.

N.A.

2.40-2.62

2.52

N.A.

N.A.

N.A.

N.A.

2.46-2.64

2.55

Schist

0-12.9

5

0-5.8

1.9

2.56-2.92

2.72

Marble

0-26.2

0.2

0-10.7

0.4

2.54-2.88

2.76

Serpentine marble

10-380

287.2

0-139

287.2

2.57-3.04

2.79

Metamorphic tuff

0-98.5

55.5

0-1268

41.9

2.56-2.66

2.62

0-88.4

36.3

0-78.9

11.1

2.36-2.83

2.57

Leucoleptite

0-120

26

0-108

27

2.36-2.86

2.71

Marble

0-14.7

0.9

0-27.1

1.6

2.50-2.82

2.67

Feldspar leptynite

0-81.3

30.6

0-5.1

2.3

2.56-2.66

2.61

891.3

2.78-2.89

2.84

Cenozoic

Mesozoic

Paleozoic

Yushulazi Metamorphic group sandstone

Plagioclase Proterozoic

leptynite Liaohe Group

Amphibolite

Archean Anshan Group

83.6

Ludwigite

690-7880

984.5

400-9730

1014.6

N.A.

N.A.

Magnetite

770.9-25710

2704.6

944.3-12180

1808.5

N.A.

N.A.

Leucoleptite

N.A.

N.A.

N.A.

N.A.

2.56-2.83

2.63

Leptynite

0-50

15.5

0-22

9.2

2.66-2.84

2.79

Magnetite

89.6-150000

65840.2

1300-22000

2000

3.6-4

3.8

Magnetite quartzite

200-61000

147980.4

290-47900

83520.6

2.67-3.77

3.18

Leptynite

38.3-62.5

53.9

7.3-10.1

9

2.56-2.68

2.6

Table 2 Proportion of favorability map and the known BIF occurrences. Favorability zone

Potential area (%)

BIF occurrences (%)

High favorability

2

60

Moderate favorability

1.1

30

Low favorability

1.2

5

Table 3

Weights of evidence, contrast, and studentized contrast values for evidential maps

s 2 (W  )

Evidence layer

W

W

C

s 2 (W  )

Fault buffer

1.453

-1.175

2.628

0.227

0.181

4.113

Gravity anomaly

0.521

-0.457

0.978

0.022

0.212

2.042

Magnetic anomaly

0.447

-0.562

1.009

0.030

0.172

2.293

Anshan Group

3.303

-1.265

4.568

0.234

0.174

7.159

s(C )

Graphical abstract

Highlights

Highlights ●Extensive study of geophysical anomalies related to BIF mineralization. ●Recognition criterion for BIF prospective were defined. ●The importance of evidence layers were evaluated. ●Problem of asymmetric information in mineral prospective mapping was discussed.