SCHOOL OF MINES AND MINERAL SCIENCES
NAMES
: CHISALA NG’UNI(18134911)
GETRUDE SIAPOLYA(18125672)
PROG
:
MINING AND EXPLORATION GEOLOGY
COURSE
:
GM420
TASK
: ASSIGNMENT ONE
DUE DATE
: 8TH FEBRUARY,2022
LECTURER : MR TREVOR MWANAMUCHENDE
QUESTION ONE
Subject the data to QAQC analysis to increase the accuracy of geological interpretations and
resource estimates through quality control
The data should be independent of each other
Compare ranges with detection limits, the detection limits must be divided by 2
Remove internal standards from data set so that you solely use data from the actual survey
Check for errors. The presence of missing data may result in biased estimates
Use blanks to check for bias that is to know whether the sample is contaminated or not
Can use duplicate to find the precision of the sample.Duplication of sampling at each reduction
and splitting stage is used to determine error magnitudes
Replication of sampling and analyses to increase sample grade precision
Check integrity of the in house standard
Using Standards for referencing the sample to find the accuracy and precision of the sample
The data established must be distributed
Development of sample homogenization and splitting procedures
QUESTION TWO
Column1
Mean
Standard Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
59.1351
4.5036
34.5
26
69.91468
4888.062
13.98611
3.295459
543
13
556
14251.56
241
The mean is the average set of the data
Standard error is the estimate of that of standard deviatiom
Median the is value that separate the higher half from the lower half of the data set
Standard deviation is the measure of the amount of variation of the data set
Mode shows how often the values appear of the data set
Sample variance measures the of dispersion or the degree of spread in the data set
Kurtosis measure used to describe the degree to which scores cluster in the tail or the peak of
the frequency distribution
Skewness measure the asymmetry of the probability distribution of a real valued random
variable about the mean
Range is the difference between the largest and smallest values of the data set
Minimum is the lowest value of the data set
Maximum is the highest value of the data set
Sum is the sum total of all the values in the data set
Count is the value of how many number of data values are observed
b. The gaussian distribution is the probability distribution that is symmetric about the mean
showing that data near the mean are more frequent in occurrence that data far from the mean
c. The coefficient of variation is 1.180
d. NO . because the mean and median are not equally distributed
QUESTION THREE
a.45,77ppm
77,109ppm
141,173ppm
205,237ppm
301,333ppm
b.The estimation of threshold values is affected due lack of statistics and sensitivity to binning
by the histogram.
QUESTION FOUR
QQ PLOT
300
250
200
150
100
50
0
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
a. It has four sub-populations
From (0-50ppm) these are background values by natural elemental abundance
From(50-150ppm)these are anomalous values due to mineral alteration
From(150-200ppm) these are are also nomalous values due to hydrothermal processes
and geological structures
From (200-240ppm) these could be outliers caused by erosion
b. The threshold is at ;
150ppm
200ppm
230ppm
The threshold of the QQ plot are higher than that of the histogram
c .QQ enhances interpretation because it employs a normal distribution unlike histogram
were the number of bins are estimated and QQ can define population from our observation
d.The data for copper anomalies are positively skewed as a result of higher concetration values
are along the positive axes
QUESTION FIVE
BOX AND WHISKER PLOT
MINIMUM
13
MAXIMUM
556
MEDIAN
35.5
LOWER QUARTILE
26
UPPER QUARTILE
48
a.maximum value: this is the greatest possible copper anomaly from the data
minimuma:this is the least or lowest copper anomaly value from data set
upper quartile:this is the first quarter of copper anomaly data
medium: the central tendency within seen copper anomalies
lower quartile: the third quarter of the copper anomalies from the observed data
b.yes they are outliers. According to all the three statistical charts it purely shows that they have
outliers
QUESTION SIX
a. The 95th percentile is =198ppm
b. Threshold =mean +2*SD =198.96ppm
The 95th threshold calculated is nearly the same as the threshold calculated from mean+2*SD
The mean+2SD is only applied to data with normal distribution and the copper in this case is
positively skewed .the 95th percentile assume that only 5% of the data is anomalous and this
in certain circumstances is not true it could be that the materials have been eroded or might
be associated to the ore deposit
QUESTION SEVEN
For the diagram find the attached book
.Inclusion of special character such “<,>” to the DL, these were replaced with (<DL/2) for
all values with the character.
These correlation implies that elements that have a strong correlation can used as pathfinders
in exploration for target element related to the pathfinder for example Ni and Cu have a strong
correlation in this dataset therefore Ni can be used as pathfinder element for Cu.
QUESTION EIGHT
Column1
Mean
Standard Error
Median
Mode
Standard
Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
b.
69.63158
7.743893
60
20
47.73656
2278.78
-0.69345
0.490179
171
9
180
2646
38
The outlier is marked in yellow
C .QQ PLOT
Copper sorted
200.000
180.000
160.000
140.000
120.000
100.000
80.000
60.000
40.000
20.000
0.000
-3
-2.5
-2
-1.5
-1
The outlier is marked in yellow
The outliers influence the distribution in such a way that they influence the data
-0.5
0