Evaluation of the applicability of geophysical methods

Evaluation of the applicability of

geophysical methods when characterizing

mine waste in Yxsjöberg, Sweden

Matilda Palo

Natural Resources Engineering, master's

2021

Luleå University of Technology

Department of Civil, Environmental and Natural Resources Engineering

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ACKNOWLEDGMENT

This thesis is my final project for my upcoming master's degree in Natural Resources Engineering

from the Luleå University of Technology. It collaborates with the research team of Exploration

Geophysics. It contains a geophysical characterization of Smaltjärnen tailings repository in

Yxsjöberg, Sweden. The thesis is written towards an audience with a background in geoscience,

natural resources engineering, or equivalent.

First of all, I would like to thank my supervisor Thorkild Maack Rasmussen for his patience,

time, and assistance throughout my thesis. For helping me with licenses and problems regarding

the software, answering questions, arranging, and taking part in the fieldwork, and lastly,

providing me with some of the results.

Secondly, I would like to thank Jingyu Gao for providing me with your 3D model, guidance

on using the ParaView software, and answering my questions, and Roshanak Vadoodi for your

help regarding computer software. Thirdly, I would like to thank Einar Valtersson for helping

Thorkild M. Rasmussen and me with data collection.

Finally, I would like to thank Mats Wahlberg at MWG electric power consultant for lending us

the field equipment Terrameter LS and Yxsjöberg AB for letting us perform the field

measurements at your site. Geoscanners AB in Boden is acknowledged for donating their radar

equipment and software to LTU.

Luleå, January 2021

Matilda Palo

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ABSTRACT

Smaltjärnen tailings repository located in Yxsjöberg, Sweden, attracts researchers with questions

regarding characterization and potential re-mining. This thesis continuous geophysical

characterization done by researchers from the Exploration Geophysics department at Luleå

University of Technology but using new data from 2019. Geophysical methods used were self-

potential (SP), direct current resistivity (DCR), induced polarization (IP), and ground-

penetrating radar (GPR).

SP data were collected using a fixed base procedure and equipment from EMIT. Data were

processed in MATLAB and presented in Oasis Montaj/ Geosoft software, yielding results

difficult to interpret. Similar pattern was seen in previous investigations from 2016. However,

some discrepancies were noticed, and more work is needed in order to validate these data.

Therefore, data is presented without any interpretation.

RES2DINV inversion software by Geotomo Software (now maintained by Aarhus

GeoSoftware) was used for inversion of DCR data to produce four 2D resistivity sections, and

the 3D resistivity model was made by Jingyu Gao with his software. DCR data were acquired

by using Terrameter LS by ABEM and measuring using a roll-along procedure and dipole-dipole

configuration. Results show consistency between vertical variations at profile crossings from

different profiles. Three layers are indicated from results, interpreted to contain mine tailings and

quaternary deposits, at some locations interpreted to be water-saturated, and bedrock. No IP

effect is seen at Smaltjärnen.

GPR data were processed in GPRSoft® PRO produced by Geoscanners to understand internal

structures and water table, by using zero-offset surveying with 250 MHz antenna from Malå

Geoscience and 300 MHz antenna from Geoscanners. Since the tailings of Smaltjärnen consists

of very thin layers, the results are complex to interpret. Hyperbolas and layers, along with other

more uncertain patterns, are seen in radargrams, and further research is needed to fully understand

the images.

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SAMMANFATTNING

I södra Sverige, i samhället Yxsjöberg, lockar gruvdammen Smaltjärnen till sig forskare i syfte

kring karakterisering och eventuell brytning av anrikningssanden. Denna avhandling fortsätter

tidigare geofysisk karakterisering utförd av forskare inom Prospekteringsgeofysik på Luleå

Tekniska Universitet, fast med nya data från 2019. Geofysiska metoder använda är självpotential

(SP), resistivitet (DCR), inducerad polarisation (IP) och markradar (GPR).

Genom användning av fast basstation procedur insamlades SP data med utrustning från EMIT.

Data processerades i MATLAB och presenterades i Oasis Montaj/ Geosoft mjukvara, vilket ledde

till svårtolkat resultat. Liknande SP mönster från tidigare studier i Smaltjärnen kan ses i resultatet.

Dock med lägre SP effekt. Data presenteras därför utan tolkning.

Programvara RESS2DINV från Geotomo Software (nu upprätthållen av Aarhus GeoSoftware)

användes för inversion av DCR data för att skapa 2D resistivitetssektioner. En 3D

resistivitetsmodell skapades av Jingyu Gao med användning av hans mjukvara. DCR data mättes

med användning av utrustning Terrameter LS från ABEM via mätningsproceduren roll-along

med dipol-dipol elektrodkonfiguration. Sammanhängande resultat påvisas för vertikala

resistivitetsvariationer vid profilgenomskärningarna. Tre lager är identifierade med

anrikningssand och kvartära avlagringar, vattenmättade områden och berggrund. Ingen IP effekt

är påvisad i Smaltjärnen.

GPR data processerades i GPRSoft® PRO mjukvara från Geoscanners för att förstå interna

strukturer och grundvattennivå. Detta utfördes genom en zero-offsetmätning med

radarutrustning från Malå Geoscience (250 MHz antenn) och Geoscanners (300 MHz antenn).

På grund av dem fina lagerna i anrikningssanden, är resultat komplext och svårtolkat. Hyperbels

och lager är synliga i radargram, likaså mer osäkra strukturer. Fortsatt arbete med tolkning av

resultat från markradar är nödvändigt för att förstå dem presenterade bilderna.

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TABLE OF CONTENTS ACKNOWLEDGMENT ....................................................................................................... 2

ABSTRACT .......................................................................................................................... 3

SAMMANFATTNING ......................................................................................................... 4

1 INTRODUCTION ........................................................................................................ 7

1.1 Background .............................................................................................................. 7

1.2 Aim .......................................................................................................................... 7

1.3 Questions to be answered ......................................................................................... 7

1.4 Method .................................................................................................................... 8

1.5 Smaltjärnen tailings repository ................................................................................... 8

2 THEORY ....................................................................................................................... 9

2.1 Self-potential ............................................................................................................ 9

2.2 Direct current resistivity ............................................................................................ 9

2.3 Induced polarization ............................................................................................... 11

2.4 Ground-penetrating radar ....................................................................................... 12

3 METHOD .................................................................................................................... 13

3.1 Field measurements ................................................................................................. 13

3.2 Field maps .............................................................................................................. 16

3.3 Data processing and modeling ................................................................................. 17

3.3.1 Terrameter LS Toolbox ................................................................................... 17

3.3.2 RES2DINV ..................................................................................................... 17

3.3.3 GPRSoft.......................................................................................................... 21

4 RESULTS AND INTERPRETATION ....................................................................... 23

4.1 Self-potential .......................................................................................................... 23

4.2 Direct current resistivity .......................................................................................... 25

4.2.1 Profile A .......................................................................................................... 26

4.2.2 Profile B .......................................................................................................... 27

4.2.3 Profile C .......................................................................................................... 28

4.2.4 Profile D .......................................................................................................... 29

4.2.5 Summary ......................................................................................................... 30

4.3 IP effect .................................................................................................................. 31

4.4 Ground-penetrating radar ....................................................................................... 32

4.4.1 The complexity of radar data interpretation ...................................................... 32

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4.4.2 Comparison with borehole data ....................................................................... 33

5 DISCUSSION............................................................................................................... 41

6 CONCLUSIONS ......................................................................................................... 42

7 FUTURE RESEARCH................................................................................................ 43

8 REFERENCES ............................................................................................................. 44

APPENDIX A: Inversion parameters .................................................................................... 46

APPENDIX B: Example L1- and L2- norm ........................................................................... 50

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1 INTRODUCTION

1.1 Background

In today's society, electrification is a hot topic, and perhaps the answer to the environmental

crisis. The demand for critical raw materials used in batteries rises, and abandoned mine sites

attract scientists and industries with the common interest to investigate potential possibilities of

re-mining the waste as a secondary source for critical raw material (Mulenshi, 2019).

The two main types of mine waste are waste rock and mine tailings. Waste rock are the non-

economical rocks excavated to access the ore, seen as waste piles at mine sites. The mine tailings

are the residual waste from the ore processing and seen in repositories at mine sites, often covered

and secured by dams (SGU, 2020). Geophysics is one among several disciplines used when

classifying tailings. It makes it possible to see beneath the subsurface to understand how the

tailings behave (GeoSci Developers, 2017). Compared to a geological or geochemical sampling

approach using sampling sites, geophysical surveying covers a broader area, and using it in

correlation with geology and geochemistry provides a comprehensive characterization (Dahlin,

Rosenqvist, & Leroux, 2010).

In Sweden, the former wolfram mine located in Yxsjöberg has attracted scientists to its tailing

repository, Smaltjärnen, to understand its potential ability to be re-mined (SGU, 2020). During

2016 and 2017, geophysics from the exploration geophysics department at Luleå University of

Technology visited Smaltjärnen, intending to correlate geophysics and geochemistry and the

applicability of various methods. Self-potential, direct current resistivity, induced polarization,

and ground-penetrating radar geophysical data were collected and interpreted. Some data were

not useful, which resulted in the need to go back to Smaltjärnen in the fall of 2019 to collect

new data for interpretation, which made this thesis possible.

For further reading and interests in the on-going research conducted at Yxsjöberg, readers are

referred to (Hällström, 2018), (Mulenshi, 2019), and (Salifu, 2020), which discusses geochemical

characterization and geo-metallurgical re-processing options at Smaltjärnen, and the use of

isotopes as traces in mine waste environments.

1.2 Aim

This thesis aims to evaluate the applicability of geophysical methods when characterizing the

mine tailings at Smaltjärnen, using self-potential (SP), direct current resistivity (DCR), induced

polarization (IP), and ground-penetrating radar (GPR). The study is limited to only use the new

data from 2019, with no comparison to results from 2017 and 2018 because of time restrictions.

The same field methods used in 2017 and 2018 (but with different instrumentation for DCR

and IP) were used, and the area of investigation is limited to Smaltjärnen tailings repository.

1.3 Questions to be answered

- Why is geophysics used for characterizing mine tailings?

- Which geophysical methods are most useful and applicable for mapping the geometry

and characterizing the mine tailings?

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- Which geophysical method has the most significant problem with noise and gives the

worst quality?

- Is there any IP effect on the mine tailings?

1.4 Method

The report's methodology and structure follow a geophysical approach with data collection, data

processing, and, lastly, data interpretation.

- Interpretation of visualized SP data from supervisor Thorkild M. Rasmussen to

understand the distribution of ions, oxidation, and analyze the groundwater flow

phenomena.

- Construction of 2D resistivity sections using RES2DINV software, along with using a

3D resistivity model made by Jingyu Gao (Gao, Smirnov, & Egbert, 2020), to determine

the internal structure, thickness of tailings, depth to the water table, and bedrock, and if

there is any IP effect.

- GPR processing with GPRSoft® PRO to understand the tailings' internal structure and

water table.

1.5 Smaltjärnen tailings repository

This study focuses on Smaltjärnen tailings repository (Fig. 1a), which hosts the mine tailings of

the former W mine located in Yxsjöberg, southern Sweden, in the ore district of Bergslagen

(iron and sulfide ore deposits), within the Svecofennian domain.

The deposit of Yxsjöberg is of skarn type and formed 1789 ±2 Ma ago and consists of three

mineralization's (Salifu, 2020), containing 0.24-0.32 wt.% W, 0.16 wt.% Cu, and 5-6 wt.%

fluorite, with ore minerals scheelite, chalcopyrite, pyrrhotite, and pyrite (Hällström, 2018).

During its active years, waste was pumped southwards from the processing plant and deployed

at the bottom of the repository, close to Smaltjärnen lake (Fig. 1b) (Hällström, 2018). The mine

closed in 1989, leaving 26 ha with about 2.8 Mt of tailings uncovered and unprotected, with

high concentrations of W, Cu, S, Sn, Zn, Be, Bi, and F (Mulenshi, 2019), making the repository

to have a high environmental risk (Hällström, 2018).

Figure 1. Smaltjärnen tailings repository (a) Location of study site (Tavakoli, Salifu, & Maack Rasmussen, 2018) (b) Smaltjärnen lake

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2 THEORY

2.1 Self-potential

SP is a near-surface passive geophysical method traditionally used for sulfide mineral exploration

but has been more popular within the last decade to use in environmental and engineering

applications. It measures the natural potential variations in the subsurface caused by spontaneous

polarization. The electrochemical mechanisms causing spontaneous polarization, or self-

potential, are not fully understood (Dentith & Mudge, 2014). The theory will be described

briefly for the probable mechanisms causing the SP effect seen in Smaltjärnen (streaming

potentials and mineral potentials) using (Bérubé, 2004). For further interest regarding the SP

phenomenon, readers are referred to (Parasnis, 1986).

Streaming potentials are caused by electrofiltration, which means that liquid flows through

porous media, carrying charged particles that gather at the solid's surface (Fig. 2a). Making up a

convection current where charges get depleted or accumulated on either side of the pore is

defined as the streaming potential (Fig. 2b). Where groundwater moves, SP anomalies caused by

streaming potentials are seen, making the method useful for groundwater investigations.

Potentials related to minerals occur where conductive minerals are present, with anomalies often

more extensive than those from streaming potentials. The potentials derived from minerals are

diverse and complex, making SP anomalies from mineral potentials challenging to interpret

(Bérubé, 2004).

Figure 2. Electrofiltration causing streaming potentials (a) Flow of liquid through porous media (b) Convection current, resulting in streaming potential

2.2 Direct current resistivity

DCR is a geophysical method mainly used in environmental surveys. Current is injected into

the ground by a transmitter connected to a power source with electrodes buried in the ground

in various configurations, yielding different subsurface information results. DCR aims to measure

contrasts in conductivity or its inverse, resistivity, within the subsurface (GeoSci Developers,

2017).

The theory of resistivity is derived from Ohm's Law, and the case when electrical current flows

through a homogeneous conductor between two points,

𝑅 =𝑑𝑉

𝐼 (1)

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where R [Ω] is the resistance inside the conductor, dV [V] is the potential difference between

the points, and I [A] represent the electrical current flowing from high to low potential (Parasnis,

1986). The relationship between resistance and resistivity for homogenous material is described

as

𝑅 = ρL

A (2)

where 𝜌 [Ωm] is the resistivity of the conductor, A [m2] is the cross-section of the conductor,

and dL [m] represents its length. By making simplifications with electrodes placed on a

homogenous halfspace and integrations of Eq. (1) and (2) (Parasnis, 1986), the voltage at one

observation point (potential electrode) with two current electrodes for describing DCR theory

is,

𝑉 =Iρ

2𝜋(

1

𝑟−

1

𝑟′) (3)

where one electrode transmits current on r distance from one observation point, and the second

electrode refers to the returning current at distance r' from the same observation point. In nature,

homogeneity is not reality. Resistivity varies in the subsurface resistivities calculated using

Eq. (3) where dV represents voltage difference between potential electrodes are referred to as

apparent resistivities ρ𝑎,

ρ𝑎 = 2πdV

IG (4)

G represents the geometrical factor dependent on the electrode array used for DCR surveying,

𝐺 =1

𝐴𝑀−

1

𝐵𝑀−

1

𝐴𝑁+

1

𝐵𝑁 (5)

A and B are current electrodes, and M and N being the potential electrodes (Parasnis, 1986). For

a homogenous halfspace, the apparent resistivity is equal to the true resistivity. AM, BM, AN,

and BN are the distance between electrodes.

At Smaltjärnen, the dipole-dipole electrode configuration was used, which means four electrodes

are arranged as two dipoles: the transmitter dipole (Tx) and the receiver dipole (Rx). The

transmitter dipole consists of the current electrodes, and the receiver consists of the potential

electrodes (Dentith & Mudge, 2014). The investigation depth gets deeper when the cable length

between the dipoles is increased. As seen below, when the receiver dipole moves along the

survey line, the deeper the investigation depth becomes, enabling the survey to measure deeper

(Fig. 3) (Parasnis, 1986).

Figure 3. Schematic figure describing a dipole-dipole configuration.

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2.3 Induced polarization

The IP method works similarly to DCR in terms of field setup, equipment, and electrode

configurations. Traditionally, IP was used for mineral exploration, but nowadays, it is also applied

for environmental applications, such as delineating contaminations in the ground (GeoSci

Developers, 2017). The basic principle involves applying an electric current to the ground. If

the current gets interrupted instantaneously and the voltage does not drop to zero simultaneously,

this phenomenon is called the IP effect, which is commonly seen in areas where clay or

conductive minerals are present (Parasnis, 1986).

IP is measured in the time or frequency domain, where time-domain is the more used within

geophysics, and results expressed as the chargeability. The transmitter used for IP alters between

being turned on, producing current, and switched off, producing no current. When the current

is off, the voltage decay is measured and integrated with respect to time and later divided by

primary voltage, resulting in chargeability, which expresses how much a material can keep a

charge when no current is applied (Fig. 4) (Dentith & Mudge, 2014).

As mentioned, the IP effect is often seen in areas where conductive or clay minerals are present,

indicating that IP originates either from electrode polarization (mineral exploration) or

membrane polarization (environmental studies) (Loke, 2020). Since Smaltjärnen is a tailings

repository to a former mine, both electrode polarization and membrane polarization are

described here since both may be of relevance. Electrode polarization occurs when conductive

particles block pore space. The applied current creates an electric field, which causes positive

and negative charges to move in opposite directions in the ground. Charges get attracted by

nearby ions, generating electrode polarization and charge accumulation (Fig. 5a). Membrane

polarization occurs in areas with clay when pore space narrows and blocks ionic charges from

passing through. The applied electric field makes the ions to accumulate on either side,

generating membrane polarization (Fig. 5b) (GeoSci Developers, 2017). The IP effect is related

to discharging of the ground when the source current is turned off.

Figure 4. Induced polarization, where chargeability being the area under the current decay curve.

Figure 5. Origins of IP effect (a) Electrode polarization (b) Membrane polarization

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2.4 Ground-penetrating radar

GPR is a near-surface geophysical method used in environmental, geological, engineering, and

archeological investigations. Its mathematical description is based on electromagnetic (EM)

theory, and the equations are describing a wave phenomenon. However, it is a standalone

method from other EM methods, and no EM theory will be discussed here (Dentith & Mudge,

2014). High-frequency EM waves (GPR signals) are generated in the antenna referred as the

transmitter (Tx), and the signal is transmitted into the subsurface, reflected, transmitted, or

refracted back to the receiver (Rx) if reaching a contrasting boundary of dielectric permittivity,

electrical conductivity, and magnetic permeability. The amplitude and travel time of the signals

are measured at the receiver, and data from each trace (Tx-Rx pair) are presented in a radargram

with travel time and potentially depth on Y-axis and distance on X-axis. The physics behind the

reflection of radar signals follows Snell's Law (Fig. 6) (GeoSci Developers, 2017),

sin 𝜃1

𝑉1=

sin 𝜃2

𝑉2 (6)

where 𝜃1 is the angle of the incoming wave, 𝜃2 being the angle of the refracted (or reflected)

wave, 𝑉1 is the incoming wave velocity, and 𝑉2 is the wave velocity in the second medium (if

reflected wave, then 𝑉1 = 𝑉2). The propagating velocity of a GPR signal is calculated by dividing

the velocity of light c with square root of relative dielectric permittivity (𝜀𝑟) (Eq. 7). The most

crucial property related to GPR is relative dielectric permittivity, also known as the dielectric

constant (Eq. 8), which affects the penetration velocity, attenuation (loss of energy when

traveling through a medium), and wavelength.

𝑉 =𝑐

√𝜀𝑟 (7)

𝜀𝑟 =𝜀

𝜀0 (8)

where 𝜀 is dielectric permittivity, 𝜀0 being permittivity of vacuum [8.89x10-12 F/m], and 𝑐 is

speed of light [3×108 m/s]. Various media have varying dielectric constants, and depending on

the type of medium, the velocity will change. The velocity will be slower for materials with a

high dielectric constant than if it has a lower dielectric constant.

Figure 6. Reflection and refraction by Snell's Law.

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3 METHOD

3.1 Field measurements

The surveying took place at Smaltjärnen tailings repository in fall 2019, measuring SP, DCR,

IP, and GPR on a target of approximately 500 m2. North of the repository is the former

processing plant and current operating industry "Yxsjö Industriservice AB" located, and south of

the site is lake Smaltjärnen. The terrain is slightly hilly with coarse-grained sand and vegetation

consisting of overgrown grass with shrubs and sparse forest (Fig. 7). Water is present at the lake

in the south and smaller creeks surrounding the area. Small- and large-scale metal-scrap are

distributed irregularly over the study site, many of which were rusty in color (Fig. 8). Weather

conditions alternated between rain and clear sky, and temperatures ranging from a few degrees

Celsius during the mornings, warming up later in the afternoon. Car was driven via the industry

site down to the tailings repository, but due to vegetation and terrain, most of the target area

could not be covered by car, and the equipment had to be carried by hand. Four straight profiles

(A-D) were constructed and marked with plastic poles for SP, DCR, and IP measurements, and

GPR was measured along irregular line patterns using GPS for location instead of along pre-

defined profiles. Field maps with measuring locations are found in 3.2 Field maps.

Figure 7. Hilly terrain at Smaltjärnen tailings repository 2019.

Figure 8. Large-scale metal scrap at Smaltjärnen tailings repository 2019.

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SP was collected on the ground surface using a voltage difference recording using the

SMARTem24 recording system from company EMIT (ElectroMagnetic Imaging Technology)

(Duncan, 2017) (Fig. 9a). The voltage differences were measured with a pair of non-polarizing

electrodes buried in humid soil, containing Cu-sulfate, a cylinder of insulated wire. One

electrode was placed at a base station, and the other one moved with increasing distance from

the base station (Fig. 9b). Recording time was 5 s with a sampling rate of 1200 samples/s for

most recordings. Average voltages were calculated for each time section. Configuration used was

fixed-based procedure, and GPS were recorded with the SMARTem24 system.

Figure 9. SP field setup (a) SMARTem24 recording system from EMIT (b) Fixed-base procedure

DCR and IP data were collected along profiles A-D using the Terrameter LS (Lund System) by

ABEM (Guideline Geo AB, 2016) (Fig. 10). The ABEM 4x21 cable system was used following

the roll-along sampling procedure, with a dipole-dipole electrode configuration. ABEM 4x21

cable systems mean that four cables (100 m á cable) are used, each with 21 take-outs for the

electrodes (with 5 m electrode spacing), which yields good near-surface resolution (ABEM

Instrument AB, 2016). The four cables available were rolled out along the first measuring profile,

being profile A (Fig. 11a). The cables were connected to the Terrameter, which was powered

by an external 60 Ah battery (standard car battery). The electrodes were connected to each cable's

take-outs and buried, leaving only the electrode's top visible to yield a good connection to the

subsurface. Since electrodes are limited to 21 per cable, using the roll-along principle enables the

survey to extend since it works in the way that one cable moves past the last cable after each

measurement (Fig. 11b) (Loke, 2020). Readers are referred to (ABEM Instrument AB, 2016) for

further information regarding the roll-along procedure.

Figure 10. Terrameter LS by ABEM.

15

Figure 11. Roll-along sampling procedure (a) Cable setup for profile A (b) Schematic figure describing roll-along

GPR data were measured using two different antennas. One from Malå Geoscience (Guideline

Geo AB, 2016) with a frequency of 250 MHz (Fig. 12a) and the other being from Geoscanners

(Geoscanners AB, 2020) with 300 MHz (Fig. 12b). Both types of equipment were measuring

continuously while walking and using GPS for data location. Both instruments are based on the

zero-offset surveying technique, i.e., transmitter and receiver adjacent to each other (Fig. 13).

Figure 12. GPR equipment (a) Malå Geoscience (b) Geoscanners

Figure 13. Zero-offset surveying technique.

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3.2 Field maps

Field maps (Fig. 14-16), including measuring locations from 2019, are presented here.

Figure 14. Map showing data collection for SP, DCR, and IP data from 2019. SP data along profile A is not included in further analysis due to complications.

Figure 15. Map showing data collection for GPR data using Malå Geoscience antenna of 250 MHz from 2019.

17

Figure 16. Map showing data collection for GPR data using Geoscanners antenna of 300 MHz from 2019.

3.3 Data processing and modeling

3.3.1 Terrameter LS Toolbox

Terrameter LS Toolbox is a software by ABEM Instruments, used to pre-process raw DCR data

collected by Terrameter LS and to determine IP effect of Smaltjärnen 2019. It lets the user open

raw field data collected using Terrameter LS, display it in various ways, and export it into a

preferable format used for inversion software (ABEM Instrument AB, 2016). For Smaltjärnen,

topography and global coordinates were added to the DCR dataset with help from supervisor

Thorkild M. Rasmussen. Data were exported into suitable file format readable by RES2DINV

for being used for processing and inversion. Pseudosections of chargeability were plotted using

Terrameter LS Toolbox to determine the presence or absence of IP effect in the tailings.

3.3.2 RES2DINV

RES2DINV is a 2D inversion software developed by Geotomo Software (Geotomo Software,

u.d.), used to produce 2D resistivity sections of Smaltjärnen profile data 2019.

3.3.2.1 Inversion

In inverse modeling, also called inversion, the modeling process is automated and done by an

algorithm in which measured data are compared to model response during several iterations. In

general, there is no unique solution for a model made by inversion (Dentith & Mudge, 2014),

making it difficult to know if it is reasonable or not. Ideally, a model should be as simple as

possible (Occam's principle), consistent with any known properties (a priori information) of the

ground, and with low data misfit. However, simplicity can be defined in different manners, such

as a minimum number of layers, as smooth as possible, etc. The inversion used here is based

upon a discretization of Earth. Each cell/ model block contains an estimated value for

Smaltjärnen being either resistivity or chargeability (Fig. 17).

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Figure 17. Model discretization for the extended model of profile A using blocks with the same width as electrode spacing.

Two critical terms for inversion are the model norm and data norm, where the model norm

𝛷𝑚𝑜𝑑𝑒𝑙 expresses the complexity of the model and the data norm 𝛷𝑑𝑎𝑡𝑎 expresses how well the

model fits the data. In both cases, we have the choice of using an L1-norm or an L2-norm, as

explained below. The inversion seeks to minimize the chosen data norm and model norm

simultaneously with the requirement of obtaining a reasonably low data misfit with respect to

data errors.

The calculation of the model nom 𝛷𝑚𝑜𝑑𝑒𝑙 is exemplified using the model discretization shown

in Table 1, where resistivities ρi,j is the log10 resistivity in row i and column j in the grid. The

expressions are

𝛷𝐿1𝑚𝑜𝑑𝑒𝑙 = ∑ ∑ (|𝜌𝑖,𝑗+1 − 𝜌𝑖,𝑗| + |𝜌𝑖+1,𝑗 − 𝜌𝑖,𝑗|)𝑀−1

𝑗=1𝑁−1𝑖=1 (9)

𝛷𝐿2𝑚𝑜𝑑𝑒𝑙 = ∑ ∑ ((𝜌𝑖,𝑗+1 − 𝜌𝑖,𝑗)

2+ (𝜌𝑖+1,𝑗 − 𝜌𝑖,𝑗)

2)𝑀−1

𝑗=1𝑁−1𝑖=1 (10)

where N is the number of rows, and M is the number of columns in the model grid. The value

of L2-norm is very sensitive to large contrast in resistivities compared to the choice of L1-norm,

as exemplified in Appendix B. Thus, if an L1-norm is used, larger contrast is allowed compared

to the L2-norm choice in the inversion.

Table 1. Model discretization.

ρ11 ρ12 ρ13 …

ρ21 ρ22 ρ23 …

ρ31 ρ32 ρ33 …

… … … ρN,M

For the data norm 𝛷𝑑𝑎𝑡𝑎, in a similar manner to model norm 𝛷𝑚𝑜𝑑𝑒𝑙, two choices are available:

𝛷𝐿1𝑑𝑎𝑡𝑎 =

1

𝑁𝑑𝑎𝑡𝑎∑ |

𝜌𝑐𝑗−𝜌𝑚𝑗

𝛥𝜌𝑚𝑗+𝜀|

𝑁𝑑𝑎𝑡𝑎𝑗=1 (11)

𝛷𝐿2𝑑𝑎𝑡𝑎 = √ 1

𝑁𝑑𝑎𝑡𝑎∑ (

𝜌𝑐𝑗−𝜌𝑚𝑗

𝛥𝜌𝑚𝑗+𝜀)

2𝑁𝑑𝑎𝑡𝑎𝑗=1 (12)

19

where 𝜌𝑐𝑗 and 𝜌𝑚𝑗 are calculated and measured apparent resistivity and Δ𝜌𝑚𝑗 is the estimated or

assumed error and 𝜀 is a small term included to prevent the denominator from becoming zero

(Geotomo Software Sdn Bhd, 2019). Table 2 describes the four possible choices for the

inversion.

Table 2. Table describing the four possible choices for inversion made by Thorkild M. Rasmussen.

Data Norm

L1 L2

Mod

el n

orm

L1

Good when having outlier in data and

we want to allow large contrast in

resistivities between adjacent cells

Good when data errors are Gaussian

distributed with no outliers, and we allow

large contrast in resistivities between adjacent

cells

L2

Good when having outlier in data and

we do not want to allow large contrast

in resistivities between adjacent cells

Good when data errors are Gaussian

distributed with no outliers, and we do not

allow large contrast in resistivities between

adjacent cells

The expression of Φ𝐿2𝑑𝑎𝑡𝑎 is also referred as the Root Mean Square (RMS) difference. When the

data errors are not known in advance and therefore assumed, it is common practice as done in

RES2DINV to use Δ𝜌𝑚𝑗 = 0.01 ∙ |Δ𝜌𝑚𝑗| in the equations and then multiply by 100 to get a

percentage RMS value.

Ideally, the inversion seeks to minimize a weighted sum of 𝛷𝑑𝑎𝑡𝑎 and 𝛷𝑚𝑜𝑑𝑒𝑙 :

𝜙 = 𝛷𝑑𝑎𝑡𝑎 + 𝛽 ∙ 𝛷𝑚𝑜𝑑𝑒𝑙 (13)

where the damping, weighting, or Tikhonov regularization factor β is chosen such that a 𝛷𝑑𝑎𝑡𝑎

corresponding to a chosen RMS is achieved. In this manner, data is fitted to a prescribed choice

based on assumed data errors while obtaining the simplest (i.e., smooth) model. Quantitative

knowledge about data errors is often not available, and an approach referred to as L-curve analysis

is often used instead. The procedure is as follows, data misfit plots to the model norm to find the

most optimum 𝛽. Being where the value has equally much weight to data misfit to model norm

(Fig. 18).

Figure 18. L-curve (Tikhonov curve), with the red mark being an optimal 𝛽-factor.

Regarding 2D inversion for Smaltjärnen, the 𝛷𝐿1𝑑𝑎𝑡𝑎 and 𝛷𝐿1

𝑚𝑜𝑑𝑒𝑙 were used. The reasoning

behind using L1-norm for both data misfit and model norm had to do with the same reasons

explained in Table 2. The damping factor β was automatically calculated in RES2DINV using

the L-curve method.

20

Appendix A describes details made for the inversion. The algorithms utilize a large number of

parameters for obtaining stable solutions to the inversion. Default values were used in many

cases.

3.3.2.2 Model resolution

Model resolution (Fig. 19) is a measure of how much the inversion resolves the subsurface

resistivity; ideally, the model resolution matrix should equal the identify matrix,

𝑅 = (𝐽𝑇𝐽 + 𝜆𝐹)−1𝐽𝑇𝐽 (14)

Visualization of the resolution matrix is done by displaying the values in the diagonal of the

matrix.

Figure 19. Model resolution displayed in RES2DINV for profile A.

3.3.2.3 Model sensitivity

Model sensitivity (Fig. 20) displays how much the model response changes if the model changes.

The higher the sensitivity value, the greater is the influence of the subsurface region. RES2DINV

calculates it by using the sum of absolute sensitivity values associated with the model cells and

commonly has the surface the highest sensitivity, decreasing at depth. If the model is changed in

areas with high sensitivity, such as the surface, the model response will be sensitive to this, making

the model response change significantly. With respect to inversion, if sensitivity is low, data

changes will not be seen in the model, meaning that high sensitivity indicates more reliable

model resistivity values (Loke, 2020).

Figure 20. Model sensitivity displayed in RES2DINV for profile A.

21

3.3.2.4 Depth of investigation

Depth of investigation (DOI) (Fig. 21) measures how deep the survey penetrates, indicating

what parts of the model are reliable or not (Geotomo Software Sdn Bhd, 2019), calculated in

RES2DINV as

𝑅(𝑥, 𝑧) =𝑚1(𝑥,𝑧)−𝑚2(𝑥,𝑧)

𝑚1𝑟−𝑚2𝑟 (15)

where 𝑚1𝑟,2𝑟 is the first and second reference model and 𝑚1,2(𝑥, 𝑧) represent model cell

resistivity from first and second inversion. Low DOI (R-value under 1.0) indicates reliable model

results, and higher DOI (R-value over 1.0) indicate less reliability. In the case of Smaltjärnen, a

normalized DOI value was used because the maximum DOI was less than 1.0,

𝑅𝑛 =𝑅

𝑅𝑚𝑎𝑥 (16)

Figure 21. Normalized DOI displayed in RES2DINV for profile A.

3.3.3 GPRSoft

GPRSoft is a post-processing and analysis software for radar data. 24 datafiles, 13 files from

Geoscanners and 11 from Malå Geoscience, were processed similarly for consistency, applying

surface correction processing, gain functions, and filtering.

Surface correction (time zero correction) corrects the time when the first air wave reaches the

receiver, and the gain function enhances visibility (Cassidy, 2009). A user-defined gain function

was used for Smaltjärnen, meaning that the gain function was created when the user applies

values for each gain point (Geoscanners AB, 2013). Three gain points were chosen, with 0 dB,

10 dB, and 10 dB, for all GPR data from Smaltjärnen. Each trace number were inspected to

confirm that no larger amplitude signals were seen after the third gain point of approximately

30 ns on the time-axis, this to make sure that not too much or too little gain was applied. The

points were set to 10 dB to enhance the GPR section's visibility (Fig. 22).

22

Figure 22. User-defined gain function in GPRSoft using data from Smaltjärnen 2019.

As a result of applying a gain function, long wavelengths (in the trace) and dark shadows (in

radargram) became visible (Fig. 23a), which is not applicable for interpretation. It was filtered

away using low- and high pass filters (LP- and HP filters). The LP filter was kept as default

calculated by GPRSoft, and the HP filter was set to 100 MHz. Since the antenna frequencies

used were 250 and 300 MHz, it is assumed that frequencies below 100 MHz are noise and need

to be removed. After filtering, the long wavelengths in trace view and dark areas in the GPR

section are gone, making the section clearer for interpretation (Fig. 23b).

Figure 23. Radargram and trace view in GPRSoft (a) The result after application of gain function showing radargram with dark shadows and trace with long wavelengths

(b) The result after filtering showing radargram without dark shadows and trace without long wavelengths

23

4 RESULTS AND INTERPRETATION

4.1 Self-potential

SP data from 2019 are processed in MATLAB and presented in Oasis Montaj/Geosoft software

by Thorkild M. Rasmussen. The processing involves a calculation of average and median values

of each recording of 5 s duration. Overall is the SP data and results difficult to interpret. Some

data have a very strong 50 Hz signal of external noise (Fig. 24a), most likely from the industry

site up north of the study site, while other data are less contaminated with noise (Fig. 24b). Note

the difference in amplitude values between Figure 24a and 24b.

Figure 24. SP data from MATLAB (a) Case with strong 50 Hz (b) Case without 50 Hz

The results from 2019 (Fig. 25 and 26) follow the same pattern seen in previous SP measurements

from 2016, but with lower SP effect values. Some locations were evaluated to be in error – most

likely due to bad contact between electrodes and subsurface and therefore removed for further

analysis. The SP effect is generally coherent in magnitude, with some negative and positive

anomalies. The aims of the SP measurements were to analyze and map

- Distribution of ions

- Oxidation

- Groundwater flow phenomena

The data are evaluated to be non-conclusive regarding the tasks mentioned above and are not

discussed further in this report.

24

Figure 25. SP result from 2019 visualized using a linear color scale by supervisor Thorkild M. Rasmussen.

Figure 26. SP result from 2019 visualized using an equal histogram color scale by supervisor Thorkild M. Rasmussen.

25

4.2 Direct current resistivity

2D resistivity models produced by RES2DINV (Fig. 27a) and 3D models by Jingyu Gao (Gao,

Smirnov, & Egbert, 2020) (Fig. 27b) are presented here. Visualized in ParaView software

displaying resistivity on a logarithmic scale, with elevation in a positive upward direction

(Geotomo Software Sdn Bhd, 2019). Inversion parameters used to produce the 2D models are

found in Appendix A. The 2D models were chosen in dialog with supervisor Thorkild M.

Rasmussen. The reasoning as to why they were selected as results had to do with having

reasonable RMS, model resolution, model sensitivity, and DOI values.

A comparison of vertical resistivity variation at profile crossings shows consistency between

results obtained from different profiles.

Figure 27. DCR results (a) 2D resistivity sections from RES2DINV (b) 3D resistivity model (Gao, Smirnov, & Egbert, 2020)

26

4.2.1 Profile A

RES2DINV run 7 iterations for making the 2D model for profile A (Fig. 28), and the best-fitted

model gave an RMS of 3.75 %. Profile A is in a S-N direction, with the lake being in the south

and sparse forest and water creeks in the north. It intersects profile C and B at coordinates

(487436, 6655975) and (487465, 6655987).

L1 is interpreted as a layer consisting of mine tailings and underlying quaternary deposits with a

varying thickness of 15-20 m. L2 is interpreted to correspond to deposits (mine tailing and

quaternary), which is fully water-saturated and with a thickness of roughly 10 m. L3 is interpreted

as the bedrock at a depth of roughly 15-20 m below the surface. Within section L1, a weak

indication of slightly higher resistivities at the top compared to the bottom is noticed. This

division may correspond to dry material at the top and a partial water-saturated layer below. The

local low resistivity zone noted at coordinate 6656100 in the RES2DINV model is difficult to

interpret and is not that clear in the 3D model. Notice, however, that water is exposed about 5-

10 m towards the north of the profile.

Figure 28. DCR results for profile A. Data outside the black line is not reliable because of DOI. (a) 2D resistivity section by RES2DINV (b) 3D resistivity model (Gao, Smirnov, & Egbert, 2020)

27

4.2.2 Profile B

RES2DINV run 8 iterations when making the 2D model for profile B (Fig. 29), and the best-

fitted model gave an RMS of 2.00 %. Profile B is in a WSW-ENE direction, with the lake being

in the west-southwest and sparse forest and surface water in the east-northeastern part. It

intersects profile C, A, and D at coordinates (487436, 6655975), (487465, 6655987), and

(487605, 6656047).

L1 is interpreted as the mine tailings and underlying quaternary deposits with a varying thickness

of 15-20 m. L2 is interpreted to be the water-saturated soft layers with a thickness of roughly

10 m. L3 is interpreted as the bedrock at a depth of roughly 15-20 m below the surface.

Figure 29. DCR results for profile B. Data outside the black line is not reliable because of DOI. (a) 2D resistivity section by RES2DINV (b) 3D resistivity model (Gao, Smirnov, & Egbert, 2020)

28

4.2.3 Profile C

RES2DINV run 5 iterations when making the 2D model for profile C (Fig. 30), and the best-

fitted model gave an RMS of 3.84 %. Profile C is in a WNW-ESE direction, with the lake's

shore being in the west-northwest and sparse vegetation close to the lake in the east-southeast.

It intersects profile B, A, and D at coordinates (487436, 6655975), (487466, 6655966), and

(487568, 6655933).

L1 is interpreted as the mine tailings and quaternary deposits with a varying thickness of 15-

20 m. L2 is indicated to be partly water-saturated deposits with thickness 10-15 m. L3 is

interpreted as the bedrock at a depth of roughly 15-20 m below the surface.

Figure 30. DCR results for profile C. Data outside the black line is not reliable because of DOI. (a) 2D resistivity section by RES2DINV (b) 3D resistivity model (Gao, Smirnov, & Egbert, 2020)

29

4.2.4 Profile D

RES2DINV run 5 iterations when making the 2D model for profile D (Fig. 31), and the best-

fitted model gave an RMS of 3.02 %. Profile D is in a SW-NE direction, with the lake being in

the southwest and sparse forest and creeks in the northeast. It intersects profile C and B at

coordinates (487568, 6655933) and (487605, 6656047).

L1 is interpreted as the mine tailings and quaternary deposits with a varying thickness of 15-

20 m. L2 is indicated to be the (partly) water-saturated deposits with a thickness of roughly

10 m. L3 is interpreted as the bedrock at a depth of roughly 15-20 m below the surface.

Figure 31. DCR results for profile D. Data outside the black line is not reliable because of DOI. (a) 2D resistivity section by RES2DINV (b) 3D resistivity model (Gao, Smirnov, & Egbert, 2020)

30

4.2.5 Summary

The results gathered from the three indicated layers (L1, L2, and L3) of profile A, B, C, and D,

are summarized in Table 3.

Table 3. DCR results for profile A-D.

Profiles

A B C D

Approximate thickness of mine

tailings and quaternary deposits

(depth to bedrock)

15-20 m 15-18 m 15-20 m 15-20 m

Approximate thickness of water-

saturated deposits 10 m 10 m 10 m 15 m

The uppermost layer (L1) consists of mine tailings and quaternary deposits, with slightly higher

resistivities at the top compared to the bottom. The tendency is interpreted because of dry

materials being on the top and water-saturated materials (being conductive) at the bottom part.

The average thickness of L1 is 15-20 m. It is not easy to interpret the thickness of the actual

mine tailings layer because of the varying resistivity parts at the top and bottom seen in the

results. Regarding (Mulenshi, 2019), the tailings of Smaltjärnen have a thickness of

approximately 6 m.

The second layer between mine tailings and quaternary deposits, and bedrock, consists of water-

saturated deposits (L2). Similar trends followed in profile A-D, where water-saturated deposits

migrate under L1. This may be why the lower part of L1 is more conductive than the upper

part. Comparing the profiles with what is seen in the field, exposed water table is consistent with

results seen in 2D resistivity sections. Areas with more exposed water, results in more conductive

anomalies. At the site, exposed water is mostly present in the north and north-northeastern parts

and close to Smaltjärnen lake (see field map Fig. 14 and results Fig. 27 for the consistency

between conductive zones being closer to the lake). Lastly, layer L3 was indicated as bedrock

because it is the bottom-most layer and highly resistive compared to its surroundings. The

average depth to bedrock, using Table 3, is 15-20 m below the surface.

31

4.3 IP effect

The raw chargeability pseudo sections for profile A-D are displayed using Terrameter LS

Toolbox on a logarithmic scale of 0.1-20 mV/V. The pattern displayed in these pseudo sections

is erratic, with jumps between low and “high” chargeability, which together with the general

low amplitudes are indicating that the signal is mainly noise. In conclusion, no IP effect is present

at the Smaltjärnen tailings repository (Fig. 32).

As described in theory, without polarization caused by blocked pores or narrow pore space, ionic

charges still flow through—resulting in no IP effect. Very fine-grained materials, such as clay

(<2 µm (UNSW, 2007)), has narrower pore sizes, making IP effect more commonly seen in

those areas. Particle size for materials in Smaltjärnen lies between 100-1000 µm (Mulenshi,

2019). Based on this, interpretation is made that no IP effect is seen due to grains being too

coarse, allowing for no polarization.

Figure 32. Raw pseudosections display an erratic pattern, with jumps between low to “high” apparent chargeability, indicating an absence of IP effect.

32

4.4 Ground-penetrating radar

GPR results from 2019 are presented and interpreted to understand the internal structures and

water table. Data is presented in radargrams exported from GPRSoft® PRO, using a black-and-

white color scheme, with included coordinates, and presented with distance marks in meters.

4.4.1 The complexity of radar data interpretation

The tailings of Smaltjärnen consist of very thin layers (Fig. 33). GPR measurements using

250 MHz antenna from Malå Geoscience (Fig. 34a) were used above the tailings cross-section

to visualize the complexity with interpretation of reflecting waves. As seen in the radargram,

some layers are indicated, but it is difficult to interpret the very thin layers (Fig. 34b). Keep this

in mind when reading further about the results presented.

Figure 33. Detailed cross-section photos of Smaltjärnen tailings showing color variations and very thin layers.

Figure 34. (a) Cross-section of exposed tailings, approximately 4 m in height (b) Results from mapping exposed tailings

33

4.4.2 Comparison with borehole data

Because of time restrictions for this report, not all GPR data are discussed. Instead, results are

presented with focus on a comparison with drill core results (Mulenshi, 2019) (Fig. 35). The

map below includes all GPR data collected from 2019 with sampling sites for drill cores; all

points close to or with intersecting GPR data are compared to GPR in this part (Fig. 36).

The very thin tailings layers (Fig. 33) make it difficult to compare the radargrams to drill cores

strictly. Some of the presented results here have similarities with identified layers, but most do

not. Further comparison between drill core and GPR data is needed. However, this part works

as an introduction displaying some of the structures seen from GPR measurements at Smaltjärnen

2019. A GPR layer is here identified in white color with black color on either side of the layer.

Figure 35. Vertical results from drill core data indicate tailings layers in different colors (Mulenshi, 2019).

Figure 36. Drill core sampling locations (Mulenshi, 2019) and GPR data from 2019.

34

4.4.2.1 Borehole data 1_1 compared to GPR data 0065 collected by 250 MHz antenna

GPR measuring sequence DAT_0065_A1 collected with Malå Geoscience equipment is closest

to sampling point 1_1 (14.771031, 60.041669) (Fig. 37). This sequence is complicated to

interpret, and only a few of the structures are marked in radargram. Many hyperbolas are present,

which may indicate metal content? Perhaps from the metal-scrap at the site. Smaller hyperbolas

are also indicated, only half of the hyperbolas, within the layer structures. The top of the section

includes some non-continuous patterns, perhaps being a static shift due to metal scrap. This

because of inhomogeneities at the surface, which can result in a time shift. However, the terrain

and vegetation were varying, which may have contributed to this observed pattern also. In

conclusion, this section needs more interpretation (Fig. 38).

Figure 37. Map and detailed figure describing the closest GPR data to sampling point 1_1 (Mulenshi, 2019). Arrows indicate walking direction (radargram is presented in the same direction).

Figure 38. GPR section (DAT_0065_A1) compared to borehole 1_1 (Mulenshi, 2019). Marked coordinates describe the red dot's approximate location (Fig. 37).

35



to sampling point 2_1 (14.775531, 60.040194) (Fig. 39). Regarding (Mulenshi, 2019), three

layers down to one meter are indicated between 0-50, 50-75, and 75-100 cm in drill core 2_1

(Fig. 35). As seen below, boundaries between layers (white lines) are indicated at 50 m and at

75 m. The layer boundaries below are not horizontal. The two upper layers are continuous,

while the third layer is discontinuous. The layers are only marked as short sequences to make it

easier to see the structures in the radargram. The layering structures that dip towards Smaltjärnen

lake may indicate which direction the waste was deployed to the repository (Fig. 40).

Figure 39. Map and detailed figure describing the closest GPR data to sampling point 2_1 (Mulenshi, 2019). Arrows indicate walking direction (radargram is presented in the same direction).

Figure 40. GPR section (DAT_0068_A1) compared to borehole 2_1 (Mulenshi, 2019). Marked coordinates describe the red dot's approximate location (Fig. 39).

36


GPR measuring sequence profile11 collected with equipment from Geoscanners is closest to

sampling point 1_2 (14.775325, 60.041778) (Fig. 41). The radargram shows clear thickness

variations of the layers. There is a weak reflector at 80 cm depth, which is consistent with the

borehole data. Above this reflector are several other layers which are not reported from the

borehole data (Fig. 42).

Figure 41. Map and detailed figure describing the closest GPR data to sampling point 1_2 (Mulenshi, 2019). Arrows indicate walking direction (radargram is presented in same direction).

Figure 42. GPR section (profile11) compared to borehole 1_2 (Mulenshi, 2019). Marked coordinates describe the red dot's approximate location (Fig. 41).

37

4.4.2.4 Borehole data 3 compared to GPR data 0069 collected by 250 MHz antenna


to sampling point 3 (14.771242, 60.042189) (Fig. 43). Within the uppermost two meters, several

reflectors are observed. The most prominent is at 30 cm, which is not reported from borehole

data. Below 2 m, only weak reflectors are indicated. The layer boundary at 3 m is not visible in

the GPR data. Instead, a layer boundary is. Indicated at 2.75 cm (Fig. 44). It should be noted

that the depth conversion is only approximate since no detailed information about velocities is

available.

Figure 43. Map and detailed figure describing the closest GPR data to sampling point 3 (Mulenshi, 2019). Arrows indicate walking direction (radargram is presented in the same direction).

Figure 44. GPR section (DAT_0069_A1) compared to borehole 3 (Mulenshi, 2019). Marked coordinates describe the red dot's approximate location (Fig. 43).

38



to sampling point 4 (14.774017, 60.040769) (Fig. 45). Many hyperbolas are seen in radargram,

which may indicate metal scrap or perhaps something regarding the terrain? Several layers are

also present. The white layer to the southwest (left in radargram) may indicate the water table.

However, further interpretation using GPR data at the shore is needed.



39



to sampling point 5 (14.774878, 60.040069) (Fig. 47). Hyperbolas in radargram may be an

identification of metal scrap. More knowledge about metal content at Smaltjärnen tailings

repository is needed to understand the structures. Layers are seen, some dipping towards the

south, which most likely indicates which direction the tailings were deployed (Fig. 48).



40

4.4.2.7 Borehole data 7 and 6 compared to GPR data 0070 collected by 250 MHz antenna

GPR measuring sequence DAT_0070_A1 collected with equipment from Malå Geoscience is

closest to sampling points 7 (14.7749994, 60.0428998) and 6 (14.775217, 60.042656) (Fig. 49).

The result is complicated to understand. Some layers are seen dipping towards the south,

indicating the direction in which waste was pumped. Hyperbolas are also present, but more

interpretation is needed to understand the structures (Fig. 50).

Figure 49. Map and detailed figure describing the closest GPR data to sampling points 7 and 6 (Mulenshi, 2019). Arrows indicate walking direction (radargram is presented in the same direction).

Figure 50. GPR section (DAT_0070_A1) compared to borehole 6 and 7 (Mulenshi, 2019). Marked coordinates describe the red dots' approximate locations (Fig. 49).

41

5 DISCUSSION

Due to time restrictions, not everything could be achieved and presented. The very thin layers

seen in the tailings cross-section contains many details and complex geology, which made the

objective of characterizing mine waste using geophysics challenging. Generally, when working

with geophysical data, the interpretation part is difficult. The reasoning as to why geophysics

was selected as a method of choice compared to other sampling methods used within geoscience

has to do with providing more continuous results than e.g., borehole or surface sampling

locations and also to provide information with depth. However, drill core data is useful for

detailed and local information of the tailings and for verification of geophysical interpretations.

Using geophysics in conjunction with geology, geochemistry, or other similar fields makes it

possible to distinguish the mine tailings from what is beneath them.

Results from self-potential measurements could not analyze the distribution of ions, oxidation,

and groundwater flow phenomena. This method gave the worst data quality among all methods

used due to being difficult to interpret and sensitive to noise and possibly bad electrode contact.

New SP data is needed to be collected to improve quality.

Direct current resistivity, compared to SP, provided more intuitive results. It is a useful and

applicable geophysical method for mapping depth to bedrock or providing knowledge about

water content. Three distinct internal structures are seen from the results, being the three layers

consisting of mine tailings and quaternary deposits and bedrock. This method also provides

information for interpretation of degree of water saturation within the deposits. These layers

were apparent in all profiles mapped and show consistency between each other in the 3D model

at profile crossovers. The thickness of actual tailings could not be estimated due to not being

able to distinguish the mine tailings from quaternary deposits. However, an average thickness of

layer consisting of mine tailings and quaternary deposits is calculated approximately 15-20 m.

From previous research conducted at Smaltjärnen, drill cores estimated a tailings thickness of

6 m, which is similar to results from DCR measurements. Water-saturated deposits are seen

closer to Smaltjärnen lake throughout the profiles, indicating where water is present. Though,

no depth calculation to water table could be done from DCR results. More detailed data is

needed to distinguish the different resistivity variations within layer of water-saturated deposits,

to actually see where the water table appears. Most likely due to large grain size at Smaltjärnen,

no IP effect was recorded.

Understanding internal structures and water table from radar data turned out to be rather

complicated due to the very thin layers within Smaltjärnen. The structures seen in models are

not easy to distinguish, or to understand. In comparison with borehole data from previous

research, some layers do coincide, and some layers noted in borehole measurements have

reflections in radargram. An indication of finer layering is noted but more difficult to compare

to borehole data. The study site did have irregularly placed metal scraps at some locations which

also are evident as reflection hyperbolas in the radargrams. Some profiles had an unexpected

dense occurrence of hyperbolas which is somewhat unlikely to be explained only by metal scrap.

More advanced GPR processing are needed to understand the reason and help interpretation.

42

6 CONCLUSIONS

The study demonstrates the applicability of using geophysical methods when characterizing mine

tailings in Yxsjöberg, Sweden.

• Self-potential data from 2019 did not manage to answer oxidation patterns, distribution

of ions, or groundwater flow phenomena within the tailings.

• Three main layers are indicated within the 2D resistivity sections and 3D resistivity

model. Resistivity increases at depth, and the most conductive zones correspond to

locations where surface water is present at the study site. The four vertical resistivity

sections correspond to each other at intersections, confirming consistency of layers.

• Water migrates under mine tailings and underlying quaternary deposits, seen in the 2D

resistivity and 3D resistivity model. This is interpreted to be the potential cause of slightly

higher conductivity seen at the bottom within the layer.

• No IP effect is recorded at Smaltjärnen, likely due to coarse grain size compared to areas

where IP effect is typically expected, i.e., being in areas with clay.

• Dipping structures are apparent from radar data, and this result is considered import for

evaluation of borehole data. These structures dip towards the lake, validating the

expectation that waste was pumped southwards from the processing plant and deployed

at the bottom of the repository during mining activities.

• Layer boundaries in radar results match some indicated layers from previous studies of

borehole data, confirming consistency between borehole and radar data.

• No information regarding depth to or structure of water table is concluded from results

and further investigations are needed. The modelling of data included Tikhonov

regularization with smoothness constraint which partly explain the difficulties in

identifying layer boundaries. A modelling based on using a smaller amount of discrete

layer should be carried out in the future.

43

7 FUTURE RESEARCH

Due to the complexity of interpretation and time restrictions, more data interpretation from

2019 is needed, both in conjunction with other research conducted at Smaltjärnen and in

comparison, to geophysical data from 2016 and 2017. Collected sand samples from Smaltjärnen

in 2019 are not yet analyzed and may contain useful information about the tailings.

New self-potential measurements are required to understand the distribution of ions, oxidation,

and groundwater flow phenomena of Smaltjärnen. Further interpretation of self-potential data

from 2019 is also of interest. It is beneficial to begin processing on-site to identify and minimize

measuring errors and issues regarding bad electrode contact. Making a more simplified resistivity

model using knowledge of the three indicated layers from 2D resistivity sections could be of

interest. More spatial data collected using direct current resistivity method are needed to make a

full 3D resistivity model. Advanced processing of radar data from 2019 may enhance structures

and help to understand uncertainties. Construction of a 3D radar model and visualization in a

VR environment is useful for understanding the internal structures and water table of

Smaltjärnen tailings. Measuring the tailings using a metal detector or magnetic field methods

might answer or explain the hyperbolas in radar data from 2019. Using lower frequency antenna

to get a deeper penetration depth is proposed. Small-scale seismic measurements can be of

interest if visiting Smaltjärnen to do field measurements; this to provide more information about

the internal structures. Also, high-frequency electromagnetics could be of interests. In particular,

high-frequency electromagnetics are fast compared to DCR. It is however noticed that the area

is contaminated with strong powerline noise, which could influence the data quality.

44

8 REFERENCES

ABEM Instrument AB. (2016). Instruction Manual. Retrieved from Guideline Geo:

https://www.guidelinegeo.com/wp-content/uploads/2016/03/User-Guide-

Terrameter-LS-2016-03-14-1.pdf

ABEM Instrument AB. (2016). User Manual Terrameter LS Toolbox . Sweden.

Bérubé, A. P. (2004). Investigating the streaming potential phenomenon using electric

measurements and numerical modelling with special reference to seepage monitoring in

embankment dams. Luleå.

Cassidy, N. J. (2009). Ground Penetrating Radar Data Processing, Modelling and Analysis. In

H. M. Jol, Ground Penetrating Radar (GPR) Theory and Applications (pp. 141-172).

Elsevier Science.

Dahlin, T., Rosenqvist, H., & Leroux, V. (2010). Resistivity-IP for landfill applications. First

Break, 101-105.

Dentith, M., & Mudge, T. S. (2014). Geophysics for the Mineral Exploration Geoscientist.

Camebridge University Press.

Duncan, A. (2017). Advances in Ground and Borehole EM Survey Technology to 2017.

Paper 13: Ground and Borehole Geophysics, 169-182.

Gao, J., Smirnov, M., & Egbert, G. (2020). 3-D DC resistivity forward modeling using the

multi-resolution grid. Pure and Applied Geophysics, pp. 2803-2819.

Geoscanners AB. (2013). GPRSoft(R) User Manual ver En2.8.x. Boden.

Geoscanners AB. (2020). Geoscanners. Retrieved from Geoscanners geophysical survey

solutions: https://www.geoscanners.com/

GeoSci Developers. (2017). DC Resistivity. Retrieved from GPG:

https://gpg.geosci.xyz/content/DC_resistivity/index.html

GeoSci Developers. (2017). Ground Penetrating Radar. Retrieved from GPG:

https://gpg.geosci.xyz/content/GPR/index.html

GeoSci Developers. (2017). Induced Polarization. Retrieved from GPG:

https://gpg.geosci.xyz/content/induced_polarization/index.html

GeoSci Developers. (2017). Seeing Underground. Retrieved from GPG.

Geotomo Software. (n.d.). Geoelectrical Imaging 2D & 3D. Retrieved from Geotomo

Software: https://www.geotomosoft.com/index.php

Geotomo Software Sdn Bhd. (2019, May). Rapid 2-D Resistivity & IP inversion using the

least-squares method. RES2DINVx64 ver. 4.09 with multi-core and 64-bit support for

Windows 7/8/10.

Guideline Geo AB. (2016). Welcome to Guideline Geo | ABEM | MALÅ. Retrieved from

ABEM | MALÅ: https://www.guidelinegeo.com/

45

Hällström, L. P. (2018). Geochemical Characterization of Historical W, Cu and F Skarn

Tailings at Yxsjöberg, Sweden: With focus on scheelite weathering and tungsten (W)

mobility. Luleå: Luleå University of Technology.

Loke, M. (2020, August 9). Tutorial: 2-D and 3-D electrical imaging surveys.

Mulenshi, J. (2019). Geometallurgical Study of Historical Tailings from the Yxsjöberg

Tungsten Mine in Sweden – Characterization and Reprocessing Options. Luleå: Luleå

University of Technology.

Parasnis, D. (1986). Principles of Applied Geophysics. 4:a ed. New York: Chapman and Hall.

Salifu, M. (2020). Stable and radiogenic isotopes as tracers for geochemical processes in

mineralogically-complex mine waste environments. Insights from 13C, 2H, 18O, 34S

and 87Sr/86Sr. Luleå: Luleå University of Technology, Graphic Production 2020.

SGU. (2020, 11 24). Förekomst och mobilitet av högteknologiskt kritiska metaller i gruvavfall,

grundvatten och ytvatten. Retrieved from Sveriges geologiska undersökning:

https://www.sgu.se/om-sgu/verksamhet/forskning/forskningsstod/sgu-finansierade-

forskningsprojekt/aktuella-forskningsprojekt/forekomst-och-mobilitet-av-

hogteknologiskt-kritiska-metaller-i-gruvavfall-grundvatten-och-ytvatten/

SGU. (2020, March 02). Mines and environmental impact. Retrieved from Geological Survey

of Sweden: https://www.sgu.se/en/mineralnaring/mines-and-environmental-impact/

Tavakoli, S., Salifu, M., & Maack Rasmussen, T. (2018, December 22). Geophysical

characterisation of the tailings in Yxsjöberg. MinNorth. Luleå.

UNSW. (2007). Particle size fractions. Retrieved from terraGIS:

http://www.terragis.bees.unsw.edu.au/terraGIS_soil/sp_particle_size_fractions.html

46

APPENDIX A: Inversion parameters

Inversion parameters used for 2D inversion in RES2DINV are presented here. Parameters

marked as bold are relevant for Smaltjärnen, while italic ones are not.

Inversion settings

Initial damping factor (0.01 to 1.00)

0.1500

Minimum damping factor (0.001 to 0.75)

0.0200

Local optimization option (0=No, 1=Yes)

1

Convergence limit for relative change in RMS error in percent (0.1 to 20)

5.0000

Minimum change in RMS error for line search in percent (0.5 to 100)

0.5000

Number of iterations (1 to 30)

30

Vertical to horizontal flatness filter ratio (0.25 to 4.0)

1.0000

Model for increase in thickness of layers (0=default 10%, 1=default 25%,

2=user defined)

2

Number of nodes between adjacent electrodes (1, 2 or 4)

4

Flatness filter type, Include smoothing of model resistivity (0=model

changes only,1=directly on model)

1

Reduce number of topographical data points? (0=No,1=Yes. Recommend leave at

0)

0

Carry out topography modeling? (0=No,1=Yes)

1

Type of topography trend removal (0=Average,1=Least-squares,2=End to end)

2

Type of Jacobian matrix calculation (0=Quasi-Newton, 1=Gauss-Newton,

2=Mixed)

1

Increase of damping factor with depth (1.0 to 2.0)

1.0500

Type of topographical modeling (0=None, 1=No longer supported so do not

use, 2=uniform distorted FEM, 3=underwater, 4=damped FEM, 5=FEM with

inverse Swartz-Christoffel)

4

Robust data constrain? (0=No, 1=Yes)

1

Cutoff factor for data constrain (0.0001 to 0.1))

0.0500

Robust model constrain? (0=No, 1=Yes)

1

Cutoff factor for model constrain (0.0001 to 1.0)

0.0050

Allow number of model parameters to exceed data points? (0=No, 1=Yes)

1

Use extended model? (0=No, 1=Yes)

1

Reduce effect of side blocks? (0=No, 1=Slight, 2=Severe, 3=Very Severe)

1

Type of mesh (0=Normal,1=Fine,2=Finest)

47

0

Optimise damping factor? (0=No, 1=Yes)

1

Time-lapse inversion constrain (0=None,1&2=Smooth,3=Robust)

3

Type of time-lapse inversion method (0=Simultaneous,1=Sequential)

0

Thickness of first layer (0.25 to 1.0)

0.3418

Factor to increase thickness layer with depth (1.0 to 1.25)

1.1000

USE FINITE ELEMENT METHOD (YES=1,NO=0)

0

WIDTH OF BLOCKS (1=NORMAL WIDTH, 2=DOUBLE, 3=TRIPLE, 4=QUADRAPLE,

5=QUINTIPLE)

1

MAKE SURE BLOCKS HAVE THE SAME WIDTH (YES=1,NO=0)

1

RMS CONVERGENCE LIMIT (IN PERCENT)

1.00

USE LOGARITHM OF APPARENT RESISTIVITY (0=USE LOG OF APPARENT RESISTIVITY,

1=USE RESISTANCE VALUES, 2=USE APPARENT RESISTIVITY)

0

TYPE OF IP INVERSION METHOD (0=CONCURRENT,1=SEQUENTIAL)

0

PROCEED AUTOMATICALLY FOR SEQUENTIAL METHOD (1=YES,0=NO)

0

IP DAMPING FACTOR (0.01 to 1.0)

0.250

USE AUTOMATIC IP DAMPING FACTOR (YES=1,NO=0)

0

CUTOFF FACTOR FOR BOREHOLE DATA (0.0005 to 0.02)

0.00010

TYPE OF CROSS-BOREHOLE MODEL (0=normal,1=halfsize)

0

LIMIT RESISTIVITY VALUES(0=No,1=Yes)

1

Upper limit factor (10-50)

50.000

Lower limit factor (0.02 to 0.1)

0.020

Type of reference resistivity (0=average,1=first iteration)

0

Model refinement (1.0=Normal,0.5=Half-width cells)

1

Combined Combined Marquardt and Occam inversion (0=Not used,1=used)

0

Type of optimisation method (0=Gauss-Newton,2=Incomplete GN)

2

Convergence limit for Incomplete Gauss-Newton method (0.005 to 0.05)

0.005

Use data compression with Incomplete Gauss-Newton (0=No,1=Yes)

0

Use reference model in inversion (0=No,1=Yes)

1

Damping factor for reference model (0.0 to 1.0)

0.01000

Use fast method to calculate Jacobian matrix. (0=No,1=Yes)

0

Use higher damping for first layer? (0=No,1=Yes)

0

48

Extra damping factor for first layer (1.0 to 100.0)

5.00000

Type of finite-element method (0=Triangular,1=Trapezoidal elements)

1

Factor to increase model depth range (1.0 to 5.0)

1.050

Reduce model variations near borehole (0=No, 1=Yes)

0

Factor to control the degree variations near the boreholes are reduced (2

to 100)

5.0

Factor to control variation of borehole damping factor with distance (0.5

to 5.0)

1.0

Floating electrodes survey inversion method (0=use fixed water layer,

1=Incorporate water layer into the model)

1

Resistivity variation within water layer (0=allow resistivity to vary

freely,1=minimise variation)

1

Use sparse inversion method for very long survey lines (0=No, 1=Yes)

0

Optimize Jacobian matrix calculation (0=No, 1=Yes)

0

Automatically switch electrodes for negative geometric factor (0=No, 1=Yes)

1

Force resistance value to be consistant with the geometric factor (0=No,

1=Yes)

0

Shift the electrodes to round up positions of electrodes (0=No, 1=Yes)

0

Use difference of measurements in time-lapse inversion (0=No,1=Yes)

0

Use active constraint balancing (0=No,1=Yes)

0

Type of active constraints (0=Normal,1=Reverse)

0

Lower damping factor limit for active constraints

0.4000

Upper damping factor limit for active constraints

2.5000

Water resistivity variation damping factor

8.0000

Use automatic calculation for change of damping factor with depth

(0=No,1=Yes)

0

Type of IP model transformation (0=None, 1=square root, 3=range)

3

Model Chargeability Lower Limit (mV/V) for range

0.00

Model Chargeability Upper Limit (mV/V) for range

900.00

Use IP model refinement (0=No, 1=Yes)

1

Weight for IP data (0.1 to 10)

1.0000

IP model damping factor (0.05 to 1.0)

0.2500

Use program estimate for IP model damping factor (0=No, 1=Yes)

0

Type of IP smoothness constraint (1=Same as resistivity, 0=Different)

49

1

Joint or separate IP inversion method (1=Separate, 0=Joint)

1

Apparent IP cutoff value (300 to 899 mV/V)

899.00

Use diagonal filter (0=No, 1=Yes)

0

Diagonal filter weight (0.2 to 5.0)

1.00

Limit range of data weights from error estimates? (0=No, 1=Yes)

0

Lower limit of data weights (0.2 to 0.5)

0.30

Upper limit of data weights (2.0 to 5.0)

3.00

Use same data weights from error estimates for different time series?

(0=No, 1=Yes)

0

Calculate model resolution? (0=No, 1=Yes)

1

Use L curve method? (0=No, 1=Yes)

1

Use same norms in L curve method? (0=No, 1=Yes)

1

Allow damping factor in increase in L curve method? (0=No, 1=Yes)

1

Type of borehole damping method (0=Horizontal distance from nearest

borehole, 1=Distance from nearest active electrode)

0

Use fast Jacobian calculation for dense data sets? (0=No,1=Yes)

0

Use higher damping factors at sides of model? (0=No,1=Yes)

1

Adjust damping factors for distances between the blocks in the model?

(0=No,1=Yes)

1

Number of electrodes in segment for sparse inversion method for very long

survey lines.

250

Time-lapse damping factor.

0.25

Reduce time-lapse damping with each iteration? (0=No,1=Yes)

1

Filter input data using gemetric factor? (0=No,1=Yes)

0

Automatically remove negative apparent resistivity values? (0=No,1=Yes)

0

Automatically remove Gamma type arrays? (0=No,1=Yes)

0

Topography distortion damping factor (0.1 to 2.0)

0.750

Use zero reference IP model value? (0=No, 1=Yes)

0

Use apparent IP data in resistivity inversion. (0=No, 1=Yes)

1

Delete temporary files during inversion? (0=No, 1=Yes)

1

50

APPENDIX B: Example L1- and L2- norm

L1- and L2- data norms are calculated using equations 11 and 12. The example below describes

L1 and L2 when outliers are included and answer why an L1 norm is robust.

Table 4 shows apparent resistivity data without outliers. If changing 1.2 (Table 4) to 3 Ωm

(Table 5), something happens with the norm. As seen, L1-norm increases from 0.3 to 2.1 Ωm,

and L2-norm increases from 0.04 to 4 Ωm. The robust L1-norm does not change as much as the

L2-norm does. If summarizing in the x-direction, the sum of L1-norm increases from 0.2 to

0.88 Ωm, and L2-norm from 0.036 to 1.172 Ωm. The larger the change is, the more sensitive,

less robust is the norm for outliers; this is why an L1-norm is to be preferred when working with

data, including outliers.

Table 4. No outliers

apparent resistivity 1 0.9 1.2 1.3 0.9 sum

Data

nor

m

L1-norm 0.1 0.1 0.3 1.7 0.4 0.2

L2-norm 0.01 1.2326E-32 0.04 0.16 1.69 0.036

Table 5. Outliers

apparent resistivity 1 0.9 3.0 1.3 0.9 sum

Data

nor

m

L1-norm 0.1 0.1 2.1 1.7 0.4 0.88

L2-norm 0.01 1.2326E-32 4 0.16 1.69 1.172

Documents

Evaluation of the applicability of geophysical methods