UNIVERSITY OF DAR ES SALAAM (UDSM)
SCHOOL OF MINES AND GEOSCIENCES (SoMG)
DEPARTMENT OF GEOSCIENCES.
GY 405: INDEPENDENT PROJECT REPORT
TITLE: ASSESSMENT OF QUALITY ASSURANCE AND QUALITY CONTROL IN
REVERSE CIRCULATION DRILLING AT NYAMULILIMA OPEN PIT.
Student: IRUNDE ELISHA A.
2019 - 04 - 02652
B Sc. with Geology.
Year of study: FOURTH YEAR.
Supervisor: Mr. KARIM MTILI.
CERTIFICATION
The undersigned certify that they have read and here by recommends for the acceptance by
the University of Dar-es-salaam, a project entitled ASSESSMENT OF QUALITY ASSURANCE AND
QUALITY CONTROL IN REVERSE CIRCULATION DRILLING AT NYAMULILIMA OPEN PIT, in partial
fulfillments of the requirement for the bachelor of science with Geology of the University of
Dar-es-Salaam.
…………………………..
KARIM MTILI
(Supervisor)
Date……………….
i
DECLARATION AND COPYRIGHT
I, IRUNDE ELISHA A, declare that this project is my own work except where acknowledged. It
has not been presented and will not be presented to any other University, for a similar or any other
degree award.
Candidate
…………………
Date
………………..
ii
ACKNOWLEDGEMENT
First and foremost, praises to the God, the Almighty for his showers of blessings from the
beginning to the end of this work.
The collection, analysis, and putting together of different materials from different sources that lead
to accomplishment of this work has been a very tiring and exciting experience. I am grateful to a
number of people for their understanding and contribution to this project.
I would like to appreciate Mr. Karim Mtili for his guidance, advice, generosity throughout the
project program, his dynamism, vision, sincerity and motivation that deeply inspired me.
To the Geosciences department from the University of Dar es salaam for their wisdom and
influences they imparted us and for the opportunity they gave us to conduct our own independent
project.
Lastly, my deep appreciation goes to the whole team of the students that we cooperated together
to fulfill goals, for their understanding and taking up some of the responsibilities during the
difficult times and helping each other, I appreciate.
iii
ABSTRACT
Resource estimates depend heavily on the quality and representativeness of the geological data
collected during the numerous drilling campaigns commonly undertaken on new resources and at
operations.
Quality control tools have been developed over the years to assist in improving data quality and
reliability. They essentially focus on laboratory measures such as blanks, standards, grain size
checks and duplicates, which assist in monitoring the accuracy of the analytical technique and the
precision of the sampling protocol. However, they are susceptible to biases.
Here we show that the sampling biases arising from quality control measures based on the Reverse
Circulation drilling at Nyamulilima gold deposit. The paper assess all stages of a Reverse
Circulation drilling campaign including site preparation, rig, laboratory issues, and data validation.
By considering the efficient performance of quality control measures and, Grade variability/ bias
is observed between the primary samples and, duplicates as well as triplicates. The geological
contribution to the nugget effect cannot be removed, however, a sound knowledge of the geology
of the area under scrutiny will ensure that all the risks associated with the deposit are accounted
for when estimating the resource. Therefore, without the removal of outliers, overall statistical
analysis indicates a slight bias towards the primary sample at percentage difference of 4.7% and
10.8% for duplicate and triplicate respectively.
We anticipate this paper to be the continuation of more scientific studies on grade variability,
specifically on the grade variability caused by the nugget effect.
iv
Table of Contents
CERTIFICATION ............................................................................................................................................... i
DECLARATION AND COPYRIGHT ................................................................................................................... ii
ACKNOWLEDGEMENT ........................................................................................................................... iii
ABSTRACT................................................................................................................................................. iv
LIST OF TABLES; ...................................................................................................................................... vi
TABLE OF FIGURES: ................................................................................................................................ vi
LIST OF ABBREVIATIONS: .................................................................................................................... vii
CHAPTER ONE ........................................................................................................................................... 1
1.1 INTRODUCTION: ............................................................................................................................. 1
1.2 STATEMENT OF THE RESEARCH PROBLEM: ....................................................................... 3
1.3 AIM OF THE STUDY:................................................................................................................... 3
1.4 SPECIFIC OBJECTIVES:.............................................................................................................. 3
1.5 RESEARCH HYPOTHESIS: ......................................................................................................... 3
1.6 SIGNIFICANCE OF THE STUDY:............................................................................................... 4
1.7 LITERATURE REVIEW: .............................................................................................................. 5
CHAPTER TWO .......................................................................................................................................... 8
2.1 GEOLOGIC SETTING: ..................................................................................................................... 8
2.1.1 Regional geology: ........................................................................................................................ 8
CHAPTER THREE .................................................................................................................................... 12
3.1 METHODOLOGY: .......................................................................................................................... 12
CHAPTER FOUR....................................................................................................................................... 15
4.1 RESULTS: ........................................................................................................................................ 15
4.1.1 RIG MASS BALANCE: ............................................................................................................ 15
4.1.2 SAMPLE MASS ANALYSIS: .................................................................................................. 21
4.1.3 GRADE BIAS TESTING: ......................................................................................................... 23
.................................................................................................................................................................... 31
CHAPTER FIVE ........................................................................................................................................ 32
5.1 DISCUSSION: .................................................................................................................................. 32
CHAPTER SIX ........................................................................................................................................... 34
6.1 CONCLUSION AND RECOMMENDATION: ............................................................................... 34
7.0 REFERENCES: .................................................................................................................................... 35
v
LIST OF TABLES;
Table 1 Showing sample weights, depth of recovery and the Total Recovery results. ................ 18
Table 2 Summary stats Original - Rig 120 Weights. .......... Ошибка! Закладка не определена.
Table 3 Showing gold assay results of the primary, duplicates and the triplicates. ..................... 23
TABLE OF FIGURES:
Figure 1 geological map of the northern half of the Tanzania Craton showing the main geological
and tectonic units. ........................................................................................................................... 9
Figure 2 A local geological map of NYamulilima district. .......................................................... 11
Figure 3 RC drilling and Sampling set up. ................................................................................... 13
Figure 4 % Recovery versus Drill Depth (m) Rig 120 - Nyamulilima GC: ................................. 19
Figure 6 A scatter plot of primary vs. duplicate samples weight................................................. 20
Figure 5 A scatter plot of primary vs. triplicate samples weight. ................................................. 20
Figure 7 quantile plot metzke cyclone spliting behavior of primary sample weights against
duplicate sample weights. ............................................................................................................. 22
Figure 8 quantile plot metzke cyclone spliting behavior of primary sample weights against
triplicates sample weights. ............................................................................................................ 22
Figure 9 metzke cyclone sample weight histogram performance of primary sample weights against
triplicates sample weights. ............................................................................................................ 22
Figure 10 metzke cyclone sample weight histogram performance of primary sample weights
against duplicate sample weights. ................................................................................................. 22
Figure 11 Q -Q plot primary gold assays vs. duplicate gold assays rig 120. ................................ 30
Figure 12 scatter plot primary gold assays vs. duplicate gold assays rig 120. ............................. 30
Figure 13 a scatter plot primary gold assays vs. triplicate gold assays rig 120. .......................... 31
Figure 14 Q - Q plot primary gold assays vs. triplicate gold assays rig 120. ............................... 31
vi
LIST OF ABBREVIATIONS:
GGM – Geita Gold Mining.
RC – Reverse Circulation drilling
CRMs – Certified Reference Materials.
QA-QC – Quality Assurance & Quality Control.
BIFs – Banded Iron Formations.
AAS – Atomic absorption spectroscopy.
QQ Plots – Quantile Plots.
vii
CHAPTER ONE
1.1 INTRODUCTION:
Reverse circulation drilling is a method used for exploratory drilling in mining operations (Sarath
& Manuel, 2010). The drilling process involves collecting cuttings from the drill hole and
transporting them to the surface for analysis. However, the accuracy of data obtained from these
cuttings depends on how representative the sample obtained from the drilling process are (Dominy,
2003). Inaccurate samples can result in the underestimation or overestimation of mineral resources,
which can have significant economic implications for mining companies and its database centres
(Abzalov, 2014).
The estimation of mineral resources is critical to all mining operations irrespective of size or
commodity (C & Barry, 2012). The risks associated with mining are varied and complex, where
the dominant source of risk is the ore body itself (Dominy, 2003). Samples that are obtained during
drilling should be representative, and therefore Quality, Accuracy and precision of samples should
be maintained (Carras, 1990). Successful resource estimation and grade control process is about
minimizing the sources of error associated with gathering of data and its analysis (Abzalov, 2014).
It is not surprising that, in the process of obtaining samples from drill holes up to laboratory
analysis, grade bias occurs and quality control is the most useful tool in monitoring specific project
results (C & Barry, 2012). Quality determine if the results comply with relevant standards and it
identifies or recommend best ways to eliminate causes of unsatisfactory performance during
sampling and analysis processes (Trotel & Eramet, 2016). The use of Certified Reference Materials
(CRMs) that is samples of known or accepted value, are submitted to assess the accuracy of
laboratory performance (CANMET, 1998). A systematic difference from the expected CRM result
indicates a bias within or between assay batches (A particular set of samples). Standard samples
may be purchased commercially or may be prepared internally by the mining company itself and
it is always recommended to submit standards that span the practical range of likely assay values.
The data which is stored in the database should be clean data and accurate enough to give
geological confidence about the area and in turn be used to create resource models that will be
used to estimate the lifespan of the ore body in consistent manner (Abzalov, 2014) mineral resource
1
and ore reserve estimation. Precision and accuracy of gold analysis, specifically in exploration,
ensuring sample management during drilling is vital concern (Marinin, 1984). At Nyamulilima
open pit, Geita Gold Mine, there are procedures put across to ensure sample quality and clean data
from field as well as ensuring sample are representatives, which finally the data obtained from
these samples are used to create geological resource models at maximum efficiency. Quality and
sample management requirements (consistent standard procedures) to assist in determining
mineralization.
The aim of this study is to assess the representativity of the sample obtained from reverse
circulation drilling as well as assessing the grade bias extent occurring in the sample during the
sample analysis. This approach will seek to minimize the sources of errors during sampling of an
ore body that particularly can mislead the estimation of resources due to the uncontrolled and
poorly analysed data during sampling and from the laboratory results.
2
1.2 STATEMENT OF THE RESEARCH PROBLEM:
Geita gold mining company performs different RC drilling campaigns at Nyamulilima pit to
recover samples which are later analyzed and used for resources modelling and estimation,
specifically for gold resources. These drilling campaigns are all done according to procedures, that
is, following quality control and quality assurance (QA-QC) measures, so that to eliminate
unnecessary errors during the drilling and sampling processes in both field sampling and laboratory
analysis. However, the company still finds it difficult to obtain representative samples for resource
modelling and estimation due to sampling biases occurring during the sampling processes
undertaken during these drilling campaigns. This research will focus on examine the extent of
sampling biases in the assessment of gold deposits as well as recommending on their possible
causes, so that to influence feasibility and profitability of mining operations, as well as Resource
modelling and estimations.
1.3 AIM OF THE STUDY:
To assess the sampling biases arising from quality control mesures of the RC drilling samples at
Nyamulilima pit.
1.4 SPECIFIC OBJECTIVES:
To assess ore recovery of the RC sample mass at nyamulilima pit.
To study the performance of quality controls; Duplicates, standards, blanks.
1.5 RESEARCH HYPOTHESIS:
How was the recovery of the ore samples?
How was the performance of the quality control measures?
Were the duplicates and triplicates representative of the primary samples?
3
1.6 SIGNIFICANCE OF THE STUDY:
The study will be focusing on tracing sources of errors in sampling, specifically sampling biases.
This study will provide valuable insights into the limitations and potential improvements that
might be needed during drilling so that to be assured that the samples and data obtained from these
samples, is clean. It will be significant for Geita gold mining and other mining companies because
it will enhance better data quality (data precision and accuracy) and interpretation of gold deposit
at Nyamulilima pit and other deposits.
In the end, the study will suggest and recommend the possible optimization ways about what the
company should apply in order to eliminate errors as possible in reverse circulation sampling so
that to have reliable and representative samples for resources modeling and explorations.
4
1.7 LITERATURE REVIEW:
The risks associated with mining are varied and complex, where the dominant source of risk is the
ore body itself (Sarath & Manuel, 2010). Reverse circulation (RC) and diamond core drilling
methods are used extensively for the collection of samples from depth. These data generally form
the critical base for both geological and grade modelling, leading to the mineral resource estimate
and ultimately the ore reserve estimate (Sarath & Manuel, 2010). RC drilling can therefore
potentially provide an uncontaminated sample, whose down-hole position is exactly known
(usually within 1 m). Another advantage of RC drilling is that the compressed air can create high
pressure around the drill bit and prevent ground water entering in to the inner drill pipe. This
facilitates collection of dry samples even from below the groundwater level (Sarath & Manuel,
2010).
RC methods are sometimes applicable to the resource evaluation of alluvial/unconsolidated
deposits, though can be highly problematic when applied to gold deposits (Sarath & Manuel,
2010). Geological confidence is largely related to the level of drilling and sampling in the ore
body, to the geologist’s perceived level of confidence in his or her work, and to the continuity of
the mineralization. This can be achieved through the geological and technical factors that affect
core recovery, how core recovery is measured, the impact of poor recovery on the resource
estimate, and how to deal with lost core during estimation (Dominy, 2003). Core loss is a relatively
common occurrence during diamond drilling, though there is very little information in the
literature on how to deal with it (Dominy, 2003). If recovery cannot be maintained at high levels
due to technical or geological problems, then it is important that this fact is not concealed.
Intersections to be used in a resource estimate should have a total core recovery (TCR) value of at
least 85%, and preferably greater than 90% (Dominy, 2003).
It is natural to have confidence in one’s own measurements, with the perception being that
fieldwork and other analyses give clear and unambiguous data (Z.K. Shipton1* & Roberts). The
main recommendation is that analysts must be aware of the limitations of their data, so they are
able to assess the veracity of the data, and, hence, to properly understand the resulting
interpretations and models (Z.K. Shipton1* & Roberts). For instance mental models are limited
(being finite in size and incomplete, subject to biases, bounded by culture, and limited by how
complexity and uncertainty is handled by the brain), it is crucial that when they are externally
5
represented, the associated documentation and reporting allows for scrutiny, interrogation and
appropriate use by others (Z.K. Shipton1* & Roberts), (i.e. biases are recognized and, where
possible, accounted for in data analysis or data transfer) (Z.K. Shipton1* & Roberts). However,
historically mining companies have commonly employed analytical regimens that involve an
initial sample assay, the magnitude of which guides subsequent analysis (C & Barry, 2012). If the
assay reports an initial high grade, subsequent replicate assays may be undertaken and averaged to
obtain a better estimate of the true grade; however, if the assay reports an initial low grade, no
additional assays are made (C & Barry, 2012). Unfortunately, because samples are treated
differently depending on grade, a bias is introduced this bias can be significant, and is not
predictable unless a priori knowledge is available (C & Barry, 2012). As a result, this bias can
have important or even catastrophic consequences.
Grade bias in gold estimates depend heavily on the quality and representatives of the geological
data collected during the numerous drilling campaigns commonly undertaken on new resources of
gold and at operations (C & Barry, 2012). Quality control tools have been developed over the years
to assist in improving data quality and reliability. They essentially focus on laboratory measures
such as blanks, standards, grain size checks and duplicates, which assist in monitoring the accuracy
of the analytical technique and the precision of the sampling protocol (Kalondwa, 2022).
The variability of grade in samples encompassed by the nugget effect is partly attributed to the
geology of the ore body, a component that can be thought of as inherent (also referred to as smallscale variability or microstructures); and partly due to error introduced during sampling through
either poor design of the sampling campaign or the use of inappropriate techniques ( (Dominy,
2003). A nugget is considered a rare find, and a deposit will usually have a wide variety of grain
sizes, and variable concentrations of the precious metal/mineral being sought (Dominy, 2003).
During the spatial analysis of an ore body the nugget effect is defined by (Dominy, 2003) as, “a
quantitative term describing the level of variability between samples at very small distances apart.”
A deposit with a low nugget effect should have low variability with largely homogenous and
predictable distribution, in terms of lithology and grade (Gill, 2009). As the geometry and
distribution of the mineralization in a deposit becomes more complex and variable, the deposit
becomes harder to predict when estimating for areas where no samples are available (Gill, 2009).
6
In such a deposit the nugget effect is said to be high, and the distribution of mineralization trends
towards randomness (Dominy, 2003).
In essence, this is a term used to describe how well sampling results can be reproduced by repeated
sampling at the same location as, finely disseminated mineralization will tend to give easily
reproducible results but heterogeneous mineralization will be sensitive to the method of sampling
and could give variable results from a single location (S. C. Dominy & Platten , 2017).
7
CHAPTER TWO
2.1 GEOLOGIC SETTING:
2.1.1 Regional geology:
Archean cratons represent old (>2500 Ma) and stable continental crust characterized by low
geothermal gradients and thick lithospheric roots (Sanislav & Brayshaw, 2016). Tanzania craton
it exposed mostly on central and northern part of Tanzania. It is surrounded by young mobile belts,
the Kibarani, Ubendini, Usagarani and Mozambique belt to the East. Also, Tanzania craton
consists mostly of granite rocks (Sanislav & Brayshaw, 2016).
The Tanzania craton stratigraphy has been divided in three main units, the Dodoman Supergroup,
Nyanzian Supergroup and the Kavirondian Supergroup in the order of their assumed relative ages
(Sanislav & Brayshaw, 2016). Also, Tanzania craton has been divided into two Regions, the Lake
Victoria Region which comprising the gold endowed East Lake Victoria Superterrane and Lake
Nyanza Superterrane separated by the gold poor, Mwanza Lake Eyasi Superterrane. The second
Region, Central Tanzania Region comprising Moyowosi-Manyoni Superterrane, Dodoma
Basement and Dodoma Schist Superterrane as well as East- Ubendian Mtera Superterrane (Kabete
& Groves, 2012).
The greenstone belts occur as irregular shaped lenses up to 30 kilometers wide and up to several
hundred kilometers long (Sanislav & Brayshaw, 2016). Six Greenstone belts, trending East-West
in Tanzania craton have been identified, Sukumaland Greenstone Belt, Shinyanga-Malita
Greenstone Belt, Musoma Mara Greenstone Belt, Kilimafedha Greenstone Belt, Nzega
Greenstone Belt and Iramba Sekenke Greenstone Belt (Borg, 1997).
Stratigraphically, the Sukumaland Greenstone Belt belongs to the Neoarchean (Borg, 1997)
Nyanzian Supergroup (Boniface & Abdul H. Mruma, 2012). The Nyanzian Supergroup consists
mainly of coarse-grained Conglomerate, grit and Quartzite (Sanislav & Brayshaw, 2016). The
Nyanzian Superterrane in the Sukumaland Greenstone Belt was subdivided into Lower Nyanzian
and Upper Nyanzian (Manya, 2004).
The Lower Nyanzian is dominated by tholeiitic mafic volcanics with minor felsic volcanics and
shale as well as Ironstone, Sandstone, chert, siltstone and mudstone (Borg, 1997). Dodoma
8
Supergroup is the oldest unit of high-grade metamorphic rocks, mafic and felsic granulite and
gneiss (Kabete & Groves, 2012).
Figure 1 geological map of the northern half of the Tanzania Craton showing the main geological
and tectonic units (modified from (Kabete & Groves, 2012)).
SU — Sukumaland Greenstone Belt; NZ — Nzega Greenstone Belt; SM — Shinyanga-Malita
Greenstone Belt; IS — Iramba-Sekenke Greenstone Belt; KF — Kilimafedha Greenstone Belt; MM
— Musoma-Mara Greenstone Belt. Super-terrane boundaries are as proposed by (Kabete &
Groves, 2012): ELVST — East Lake Victoria, MLEST — Mwanza Lake Eyasi, LNST — Lake Nyanza,
MMST — Moyowosi-Manyoni, DBST — Dodoma Basement, MAST — Mbulu-Masai, NBT —
Nyakahura-Burigi. Insert map of Africa showing the location of Archean blocks: (Kabete & Groves,
2012)
9
2.1.2 Local Geology.
The Geita Greenstone Belt is mainly composed of upper Nyanzian (2.5-2.8Ga) sediments
dominated by intermediate to felsic volcanoclastic rocks and BIF, forming a thick sedimentary
sequence. It has been subdivided into three domains. From East to West, the domains are the
Kukuluma, Central and Nyamulilima domains (Sanislav & Brayshaw, 2016).
The Nyamulilima domain contains three major gold deposits on an approximately NW-SE
mineralised trend. These are from SE to NW: Ridge 8, Star and Comet and Roberts. Individual
deposits occur along a series of N-S trending, steeply dipping, left stepping en-echelon fault zones
that cut across the ironstone-rich sediments and granite-granodiorite-tonalite intrusions.
Mineralization is preferentially localized along fault zones where they cut the ironstone-granitoid
contacts. The mineralization is associated with secondary pyrite and minor pyrrhotite, silica,
carbonate and actinolite alteration (Sanislav & Brayshaw, 2016). Also, In the Nyamulilima domain
a tuff horizon within a metavolcanic sequence associated with banded iron formation (BIF) was
dated at 2771 ± 15 Ma which suggests that some meta-ironstones in the Geita greenstone belt could
belong to a separate, possibly older, stratigraphic unit (Borg, 1997).
The Geita deposits are hosted in Achaean-age rock formations characterized by Banded Iron
Formation (BIF), some felsic volcanic and Diorite lithology. Gold mineralization is generally
associated with these thick ironstone formations (BIFs) which often rise above the plains to form
a series of different ridges. Banded Iron Formation (BIF) outcrops covers large ground in the area
while felsic volcanic occurs in the lower edges of the ridges and sometimes are either inter-bedded
within the Banded Iron Formation (BIF) or occur dominated in the particular area. Mineralization
of the area is controlled by different structure such as along the shear zone, along the veins infill
such as calcite vein infill and quartz, gold mineralization is also related to ductile to brittle-ductile
deformation at late stages. The presence of mineralization in these structures is the interaction of
carbon Condie, (Sanislav & Brayshaw, 2016).
10
Figure 2 A local geological map of NYamulilima district.
11
CHAPTER THREE
3.1 METHODOLOGY:
3.1.1Sampling set-up in reverse circulation drilling:
reverse circulation is achieved by blowing air down between the inner and outer tubes, with the
differential pressure creating air lift of the water and cuttings up the inner tube inside each rod
(Sarath & Manuel, 2010). The ejected material reaches the bell at the top of the hole and then
moves through a sample hose which is attached to the top of a cyclone. The drill cuttings travel
around the inside of the cyclone until they fall through an opening at the bottom and are collected
in a large sample bags (Sarath & Manuel, 2010).
It is important that as much of the sample cuttings as possible for a given drilled interval (usually
1 m) are collected (Trotel & Eramet, 2016). To ensure the sample cuttings are being completely
collected, the hole is sealed at its collar using a plugging material so that the sample is forced to
travel through the drill stem and into the collector at the top of the rods (Dominy, 2003). The
sample tube (hose) is connected to a cyclone, which is designed to settle most of the fine dust
particles which would otherwise blow away. Different sample collecting procedures can be
applied. A representative sample from the large sample bag representing every meter of depth
drilled was collected and washed using a 1 mm sieve. The resulting chips were used to identify the
rock type and the mineralization, if any. The chips were stored in plastic chip trays marked with
the depths.
12
Figure 3 RC drilling and Sampling set up.
3.1.2 Drill rig inspection:
Drill Rig inspection sheets framed for Grade Control and Exploration Reverse Circulation (RC)
drilling were completed during Rig 120 operation at Nyamlilima Grade Control by the drilling
Quality Control supervisor, collaboration with safety team. The Drill Rig inspection includes Drill
Rig set up, Drill Site, Personal safety, Sampling system recovery, Machinery inspection, Safety
Equipment, Drilling practices and environmental consideration. Sampling recovery system is
inspected to make sure that there is no sampling error originating from the sample recovery system.
Aimed at all the areas drilling quality requirements were adhered to.
13
3.1.3 Fire assay:
This method was applied so that to determine the concentration of gold and it involved the
following key processes:
Sample Preparation: after sampling during RC drilling, preparation of the sample is done. The
sample should be representative of the material being analyzed and is typically obtained through
drilling, sampling from a mine, or crushing and pulverizing larger bulk samples. Preparation was
done to ensure homogeneity and minimize contamination.
Fusion: The prepared sample is mixed with fluxes, such as litharge (lead oxide), borax, and silica,
in a crucible. The mixture is heated at high temperatures, typically around 1,100 to 1,200 degrees
Celsius, in a furnace. During fusion, the fluxes aid in the separation of precious metals from other
elements and impurities.
Cupellation: After fusion, the resulting molten material is poured into a mold to form a lead button.
The lead button contains the precious metals and other impurities. The lead button is then placed
in a cupel, which is a porous, absorbent material made of bone ash or magnesia. The cupel is heated
in a furnace at temperatures around 950 to 1,050 degrees Celsius. As the cupel absorbs the lead, it
oxidizes and forms litharge (lead oxide). The litharge absorbs impurities, leaving behind the
precious metals as small beads or prills.
Parting: this is performed to separate gold and silver. The prills obtained from cupellation are
treated with nitric acid to dissolve the silver, leaving behind the gold. The gold is then rinsed,
dried, and weighed.
Final Analysis and Quantification: The precious metals obtained from cupellation or parting are
further analyzed to determine their concentrations. Atomic absorption spectroscopy (AAS) was
used.
14
CHAPTER FOUR
4.1 RESULTS:
4.1.1 RIG MASS BALANCE:
A study to monitor the performance of drill rig 120 was conducted on eight grade control holes at
1430mRL, drill block RT0010 Nyamulilima open pit.
The rig mass balance studies involve collecting all sample material per drilled meter over the
length of a hole. The following samples were collected: Sample A = Cone splitter - Primary
Sample B = Cone splitter - Duplicate
Sample C = Cone splitter - Triplicate
Sample D = Cone splitter - Reject
For each drilled interval, the mass of the primary sample A is weighed and compared to that of
their corresponding sub-samples B (duplicate) and C (triplicate). The results are then plotted to
show downhole sample recovery.
The table below show the sample weight results of the primary, duplicate and triplicate samples,
as well as the recovery of the 8 drill holes of the study.
15
16
17
RRT004852
0.50
1.50
0.7
0.92
0.84
RRT004852
1.50
2.50
1.44
1.46
1.28
37
RRT004852
2.50
3.50
1.72
1.70
1.62
47
RRT004852
3.50
4.50
1.96
2.30
2.14
60
RRT004852
4.50
5.50
1.98
2.28
2.66
64
RRT004852
5.50
6.50
2.16
2.52
2.60
68
RRT004852
6.50
7.50
3.00
2.80
2.60
80
RRT004852
7.50
8.50
2.62
2.76
2.40
71
RRT004852
8.50
9.50
2.32
2.70
2.84
73
RRT004852
9.50
10.50
2.74
2.98
2.78
80
RRT004852
10.50
11.50
2.66
3.20
3.28
82
RRT004852
11.50
12.50
2.44
3.22
2.58
73
105
105
105
76
3.013
2.864
3.035
Table 1 Showing sample weights, depth of recovery and the Total Recovery results.
18
% Recovery versus Drill Depth (m) Rig 120 - Nyamulilima GC:
Average recovery – 76% Recovery.
Figure 4 % Recovery versus Drill Depth (m) Rig 120 - Nyamulilima GC:
19
Scatter plots for Rig 120
Duplicate
10,00
9,00
8,00
7,00
6,00
5,00
4,00
3,00
2,00
1,00
0,00
0,00
y = 0,7999x + 0,4539
R² = 0,8396
1,00
2,00
3,00
4,00
5,00
Primary
6,00
7,00
8,00
9,00 10,00
Figure 5 A scatter plot of primary vs. duplicate
samples weight.
y = 1,0121x - 0,014
R² = 0,7874
Triplicate
10,00
9,00
8,00
7,00
6,00
5,00
4,00
3,00
2,00
1,00
0,00
0,00
1,00
2,00
3,00
4,00
5,00
Primary
Figure 6 A scatter plot of primary vs.
triplicate samples weight.
20
6,00
7,00
8,00
9,00 10,00
Therefore, overall sample recovery down the hole is observed to be good. Versus a target recovery
of 90%, the average recovery was 76%. Sample mass recovery for the eight holes indicate no
apparent bias between primary, duplicate and triplicate samples collected.
4.1.2 SAMPLE MASS ANALYSIS:
In addition to downhole recovery studies, duplicate and triplicate sub-sampling was conducted on
the eight drill holes to monitor the extent of imprecision or random error in the sample splits and
sub-sampling system.
A scatter plot of primary versus duplicate and triplicate sample (figure 9 & 10), demonstrates
insignificant differences in sample split behavior, particularly for scatter plot between primary
verses triplicate sample masses. The scatter plot for primary verses duplicates sample masses
indicates a slight displacement towards a heavier primary, resulting in sample averages of 3.01kg
and 2.86kg respectively for the plotted data. The average weight for triplicate sample mass was
3.04kg.
The statistical graphs (figure 11, 12, 13 & 14) complement the scatter plots above, with a clear
indication of uniformity between the primary, duplicate and triplicate sample masses. This is also
evident in the histogram graphs, both having normal skewness of the curve indicating that samples
(primary, duplicates & triplicates)are obtained from the same distribution.
This implies that the rig mounted static cone splitter was largely functioning correctly in splitting
the samples.
21
QUANTILE QUANTILE PLOT METZKE
CYCLONE SPLITING BEHAVIOR
(RIG 120 AT GC)
7
Triplicate Sample (wt)
Duplicate Sample (wt)
7
QUANTILE QUANTILE PLOT METZKE
CYCLONE SPLITING BEHAVIOR
( RIG 120 AT GC)
6
6
5
5
4
4
3
3
2
2
1
1
0
0
0
0
1
2
3
4
5
Primary Sample (wt)
6
C…
7
Figure 7 quantile plot metzke cyclone spliting
behavior of primary sample weights against
duplicate sample weights.
Metzke Cyclone Sample Weight histogram
Perfomance Rig 120 Grade Control
12
10
Frequency
12
Frequency
14
10
Mass B
6
Mass A
7
Figure 8 quantile plot metzke cyclone spliting
behavior of primary sample weights against
triplicates sample weights.
Metzke Cyclone Sample Weight Histogram
Perfomance Rig 120 Grade Control
8
1
2
3
4
5
6
Primary Sample (wt) C…
8
Mass A
6
Mass C
4
4
2
2
0
0
Bin
Bin
Figure 10 metzke cyclone sample weight
histogram performance of primary sample
weights against duplicate sample weights.
Figure 9 metzke cyclone sample weight histogram
performance of primary sample weights against
triplicates sample weights.
22
4.1.3 GRADE BIAS TESTING:
The grade bias study involve collecting all sample material per drilled meter over the length of a
hole, and the samples were then taken to the laboratory for gold concentration determination. The
following samples were collected and then taken to the laboratory: Sample A = Cone splitter - Primary
Sample B = Cone splitter - Duplicate
Sample C = Cone splitter – Triplicate.
The laboratory assay results for gold are shown below:
Table 2 Showing gold assay results of the primary, duplicates and the triplicates.
BHID
ASSAY A
ASSAY B
ASSAY C
RRT004555
0.29
0.26
0.31
RRT004555
0.05
0.07
0.06
RRT004555
0.005
0.03
0.02
RRT004555
0.04
0.04
0.05
RRT004555
0.11
0.1
0.14
RRT004555
0.15
0.15
0.13
RRT004555
0.16
0.2
0.14
RRT004555
0.07
0.1
0.1
RRT004555
0.11
0.1
0.08
RRT004555
0.03
0.06
0.03
RRT004555
0.04
0.04
0.05
RRT004555
0.04
0.05
0.03
23
RRT004555
0.07
0.03
0.03
RRT004555
0.03
0.02
0.03
RRT004741
0.43
0.03
0.18
RRT004741
0.34
0.54
0.38
RRT004741
2.39
0.15
0.2
RRT004741
0.36
0.75
0.87
RRT004741
0.33
0.39
0.32
RRT004741
0.33
0.16
0.14
RRT004741
0.29
0.57
0.53
RRT004741
0.13
0.34
0.35
RRT004741
0.12
0.1
0.1
RRT004741
0.17
0.11
1.01
RRT004741
0.14
0.17
0.14
RRT004741
0.2
0.17
0.2
RRT004741
0.17
0.32
1.31
RRT004556
0.49
0.27
1.23
RRT004556
0.68
0.17
0.41
RRT004556
1.34
0.36
0.15
RRT004556
2.71
0.52
0.55
RRT004556
0.96
2.42
0.19
RRT004556
0.26
0.55
0.55
RRT004556
0.69
0.41
0.41
24
RRT004556
0.99
0.23
0.18
RRT004556
0.5
0.59
0.68
RRT004556
0.43
0.67
0.4
RRT004556
1.6
0.21
1.23
RRT004556
0.26
0.39
0.81
RRT004556
0.26
0.58
0.43
RRT004556
0.25
0.72
0.13
RRT004560
1.85
0.21
0.03
RRT004560
0.9
0.23
0.02
RRT004560
1.1
1.38
1.64
RRT004560
1.32
0.44
0.86
RRT004560
3.03
1.18
1.08
RRT004560
0.83
1.28
1.18
RRT004560
0.13
1.49
3.28
RRT004560
0.18
1.09
0.87
RRT004560
0.21
0.11
0.18
RRT004560
0.09
1.21
0.2
RRT004560
0.04
0.32
0.25
RRT004560
0.03
0.08
0.09
RRT004560
0.04
0.05
0.04
RRT004560
0.01
0.02
0.02
RRT004562
0.9
0.03
0.31
25
RRT004562
0.38
0.03
0.09
RRT004562
1.52
4.29
0.07
RRT004562
4.84
0.48
1.13
RRT004562
0.42
1.04
0.44
RRT004562
0.26
0.2
1.08
RRT004562
0.32
1.87
0.36
RRT004562
1.88
0.08
1.94
RRT004562
0.22
0.42
0.43
RRT004562
0.06
0.05
0.05
RRT004562
0.07
0.07
0.1
RRT004562
0.03
0.06
0.04
RRT004720
1.13
1.4
0.98
RRT004720
1.24
1.15
0.57
RRT004720
1.44
0.51
0.51
RRT004720
1.22
1.45
0.48
RRT004720
1.34
0.44
0.37
RRT004720
1.36
0.32
0.51
RRT004720
1.51
0.31
0.42
RRT004720
0.3
0.39
2.18
RRT004720
0.29
5.19
0.31
RRT004720
0.26
0.32
0.19
RRT004720
0.25
1.41
1.4
26
RRT004720
0.24
0.35
1.19
RRT004731
0.35
0.16
0.15
RRT004731
0.28
0.17
0.14
RRT004731
0.14
0.23
0.14
RRT004731
0.14
0.18
0.13
RRT004731
0.17
0.31
0.3
RRT004731
0.13
0.2
0.19
RRT004731
0.17
0.12
0.19
RRT004731
0.17
0.18
0.22
RRT004731
0.23
0.23
0.28
RRT004731
0.1
0.14
0.12
RRT004731
0.41
0.38
0.36
RRT004731
0.41
0.42
0.51
RRT004731
0.52
0.31
0.43
RRT004552
0.44
0.64
0.39
RRT004552
0.22
0.21
0.53
RRT004552
0.14
0.39
0.42
RRT004552
0.14
0.28
0.85
RRT004552
0.17
0.19
0.24
RRT004552
0.16
0.11
0.12
RRT004552
0.08
0.09
0.25
RRT004552
0.1
0.12
0.1
27
RRT004552
0.07
0.09
0.08
RRT004552
0.1
0.1
0.1
RRT004552
0.1
0.22
0.18
RRT004552
0.19
0.25
0.1
RRT004552
0.2
0.14
0.25
RRT004552
0.12
1.05
0.11
RRT005011-001
0.35
0.23
0.48
RRT005011-003
0.35
0.32
0.38
RRT005011-004
0.38
0.89
1.38
RRT005011-005
0.2
0.34
0.42
RRT005011-006
0.34
0.19
0.39
RRT005011-007
0.02
0.13
0.17
RRT005011-008
0.2
0.31
0.38
RRT005011-009
0.26
0.21
0.23
RRT005011-010
0.29
0.43
0.33
RRT005011-011
0.31
0.37
0.26
RRT005011-012
0.34
0.32
0.31
RRT005011-013
0.32
0.32
0.58
RRT005011-014
0.92
0.4
0.56
RRT005135
0.01
0.05
0.02
RRT005135
0.06
0.04
0.01
RRT005135
0.03
0.03
0.1
28
RRT005135
0.07
0.09
0.08
RRT005135
0.2
0.12
0.1
RRT005135
0.03
0.04
0.02
RRT005135
0.02
0.17
0.15
RRT005135
0.06
0.81
0.67
RRT005135
0.12
0.05
0.11
RRT005135
0.03
0.02
0.02
RRT005135
0.03
0.03
0.02
RRT005135
0.11
0.02
0.01
RRT005135
0.14
0.16
0.2
RRT005135
0.04
0.03
0.02
29
A grade bias test was conducted on primary samples versus duplicate and triplicate samples to
assess the bias arising from RC rig cone splitter performance, laboratory sample preparation plus
the inherent grade variability or nugget effect. 133 assays (mostly with grades close to detection
limit) were used on plots to assess for grade bias. The data is presented as scatter plots, QQ ploys
and summary statistics (Figure 15, 16, 17, & 18).
The scatter plot exhibit partial data dispersion with some outlier values. Without the removal
outliers, overall statistical analysis indicates a slight bias towards the primary sample at percentage
difference of 4.7% and 10.8% for duplicate and triplicate respectively. This is supported by the
Regression coefficient of 0.0452 on Scatter plot (no relationship among the data plotted), which
portrays a clear bias towards primary assays, particularly for grades greater than 1g/t, portrayed on
the QQ plot.
SCATTER PLOT PRIMARY VS DUPLICATE
ASSAYS RIG 120
Q -Q PLOT PRIMARY VS DUPLICATE
ASSAYS RIG 120
6
Au (g/t) Duplicate
6
Au (g/t) Duplicate
5
4
3
2
y = 0,2163x + 0,332
R² = 0,0457
1
5
4
3
2
1
0
0
1
2
3
4
5
Au (g/t) primary
Scatter plot
Colu…
0
6
0
1
2
3
Au(g/t)Primary
4
5
6
Figure 11 Q -Q plot primary gold assays vs.
duplicate gold assays rig 120.
Figure 12 scatter plot primary gold assays vs.
duplicate gold assays rig 120.
30
SCATTER PLOT PRIMARY VS TRIPLICATE ASSAYS RIG 120
Au (g/t) Triplicate
6
5
4
3
2
y = 0,2114x + 0,3066
R² = 0,0867
1
0
0
1
2
3
4
5
Au (g/t) primary
Scatter plot
6
Figure 13 a scatter plot primary gold assays
vs. triplicate gold assays rig 120.
Q - Q PLOT PRIMARY VS TRIPLICATE ASSAYS RIG 120
6
Au (g/t) Triplicate
5
4
3
2
1
0
0
1
2
Au(g/t)Primary
3
Figure 14 Q - Q plot primary gold assays vs.
triplicate gold assays rig 120.
31
4
5
Column I
6
CHAPTER FIVE
5.1 DISCUSSION:
The rocks recovered in all 8 holes were observed to have density ranging from 2.2 to 2.7 g/cm3
where by volcanic rocks, tonalite and BIF with densities of 2.7g/cm3 and the intrusive rocks with
density of 2.2 g/cm3 were recovered. The overall sample recovery down the hole is observed to be
good. Versus a target recovery of 90%, the average recovery was 76% (figure 8). This indicates
that the RC drilling was working noticeably efficient, providing good recovered and clean samples
that are free from contamination. Apparently there is no any bias that is reported to arise from
sample recovery using RC drilling technique at Nyamulilima open pit, this is clearly evidenced on
graph showing boreholes recovery which were plotted using recovery depth against the total
sample mass weights of the primary, duplicates and triplicate samples. Therefore, the analysis of
our results regarding the quality control measures implemented during sampling, both the sampling
process and core recovery demonstrated good performance, indicating the reliability of the
physical extraction of samples from the target area.
Sampling errors can arise from various sources, such as inadequate sample collection techniques,
improper sample handling (contamination), or spatial variability within the deposit. To consider
the potential sampling errors that might have been introduced during the sampling process,
duplicate and triplicate sub-sampling was conducted on the eight drill holes to monitor the extent
of imprecision or random error arising from the sample splits and sub-sampling system. The
statistical scatter graphs (figure 11, 12) indicate that the samples were split quietly in similar
manner such that graphs portray that the samples (primary, duplicates and triplicates) belong to
similar distribution. This is also evidenced on the histogram graphs (figure 13, 14), which portray
normal curve distribution indicate that the samples are related and belong to the same distribution.
Sample mass recovery for the eight holes indicate no apparent bias between primary, duplicate and
triplicate samples collected, with a clear indication of uniformity between the primary, duplicate
and triplicate sample masses portrayed on the statistical graphs. Hence, this implies that the rig
mounted static cone splitter was largely functioning correctly.
However, upon closer examination, it becomes evident that there is a grade bias of gold between
the primary samples, duplicates, and triplicates. Grade bias testing, the statistical scatter plot
exhibit partial data dispersion with some outlier values. Consider the statistical graphs in figure
32
15, 16, 17 & 18. Without the removal outliers, overall statistical analysis indicates a slight bias
towards the primary sample at percentage difference of 4.7% and 10.8% for duplicate and triplicate
respectively on the scatter plots in figure 16 & 18. This is supported by the QQ plot (figure 17 &
15), which portrays a clear bias towards primary assays, particularly for grades greater than 1g/t.
This indicates a failure in duplicates and the triplicates subsamples quality control measure, as they
both are not representative of the primary samples.
The presence of grade bias raises concerns about the representativeness and accuracy of our
sampling strategy. One possible explanation for this bias is the Nugget effect, a phenomenon that
brings about the natural variability or irregular distribution of mineralization within a deposit. The
Nugget effect can have a substantial impact on sampling outcomes, leading to inconsistent grades
observed across different samples. To address these issues and improve the reliability of our
sampling approach, several actions can be taken. Firstly, increasing the number of samples
collected and analyzing large number of samples within the target area, conducting a thorough
analysis of the sampling errors, and, considering the application of geostatistical techniques to
account for the spatial variability of gold grades within the deposit ( Inverse distance method and
kriging method), would help mitigate the influence of the Nugget effect. By expanding the sample
size, we can obtain a more comprehensive representation of the grade distribution and minimize
the impact of localized variations.
33
CHAPTER SIX
6.1 CONCLUSION AND RECOMMENDATION:
In conclusion, while the quality control measures and core recovery demonstrate good
performance, the presence of grade bias between primary samples, duplicates, and triplicates poses
a significant concern. Considering the Nugget effect and potential sampling errors, it is imperative
to take corrective actions to improve the reliability of our sampling approach. Increasing sample
size, conducting thorough error analysis, implementing stringent quality control measures, and
applying geostatistical techniques will contribute to more accurate and representative grade
estimations for the deposit.
However, no concrete conclusion can be made on grade variability between the primary sample
versus collected sub-samples as studies were carried out on ultra-low-grade margins (with more
than 70% of assay data reporting below 0.2g/t). Therefore, we recommend more studies to be
conducted on medium to higher grade holes to have a concrete conclusion on grade variability
between sub-samples generated from the cyclone.
34
7.0 REFERENCES:
Abzalov. (2014). The resource database.
Boniface, N., & Abdul H. Mruma. (2012). Structural analysisi, Metamorphism, and Geochemistry
of the Archean GRANITOIDS-greenstones of the Sukumaland greenstone Belt around
Geita Hills, Northen Tanzania.
Borg, G. (1997). The Tanzania and NE Zaire Cratons in Greenstone Belts.
Bumstead. (1984).
C, R. S., & Barry, W. (n.d.). Estimation bias of mineral deposits.
CANMET. (1998). Assessment of laboratory performance with Certified Reference Materials.
Carras. (1990).
Dominy, A. E. (2003). Core recovery and quality: important factors in mineral resource.
Gill, S. J. (2009). The Nugget Effect: In describing the variability of an ore deposit.
Kabete, J., & Groves, D. (2012). A new tectonic and temporal frameworkfor the Tanzanian shield:
Implication for gold metallogeny and undiscovered endowment.
Kalondwa. (2022). Underground Grade Control RC Sampling Procedures.
Manya, S. (2004). Geochemistry and petrogenesis of volcanic rocks of the Neoarchean
Sukumaland Greenstone Belt, northwestern Tanzania.
Marinin, M. B. (1984). Assessing of Losses and Dilution Impact on the Cost Chain:.
S. C. Dominy, & Platten , I. (2017). Grade and geological continuity in high-nugget effect.
Sanislav, I. V., & Brayshaw, M. (2016). The structural history and mineralisation control of the
world class Geita Hill gold deposit, Geita greenstone belt, Tanzania.
Sarath, H., & Manuel, J. (n.d.). A Comparison of Drilling and Sampling . 2O10.
Trotel, F., & Eramet. (2016). A comprehensive QA-QC methodology designed to .
Z.K. Shipton1*, & Roberts, J. (n.d.). Fault Fictions: Systematic biases in the conceptualization of
fault zone architecture.
35
36