NPGNonlinear Processes in GeophysicsNPGNonlin. Processes Geophys.1607-7946Copernicus GmbHGöttingen, Germany10.5194/npg-22-579-2015Identification of magnetic anomalies based on ground magnetic data
analysis using multifractal modelling: a case study in Qoja-Kandi, East
Azerbaijan Province, IranMansouriE.FeiziF.feizi.faranak@yahoo.comKarbalaei RamezanaliA. A.Young Researchers and Elite Club, South Tehran Branch, Islamic Azad
University, Tehran, IranMine Engineering Department, South Tehran
Branch, Islamic Azad University, Tehran, IranF. Feizi (feizi.faranak@yahoo.com)7October201522557958710May201524July201523September201524September2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://npg.copernicus.org/articles/22/579/2015/npg-22-579-2015.htmlThe full text article is available as a PDF file from https://npg.copernicus.org/articles/22/579/2015/npg-22-579-2015.pdf
Ground magnetic anomaly separation using the reduction-to-the-pole (RTP)
technique and the fractal concentration–area (C–A) method has been
applied to the Qoja-Kandi prospecting area in northwestern Iran. The
geophysical survey resulting in the ground magnetic data was conducted for
magnetic element exploration. Firstly, the RTP technique was applied to
recognize underground magnetic anomalies. RTP anomalies were classified into
different populations based on the current method. For this reason, drilling
point area determination by the RTP technique was complicated for magnetic
anomalies, which are in the center and north of the studied area. Next, the
C–A method was applied to the RTP magnetic anomalies (RTP-MA) to
demonstrate magnetic susceptibility concentrations. This identification was
appropriate for increasing the resolution of the drilling point area
determination and decreasing the drilling risk issue, due to the economic
costs of underground prospecting. In this study, the results of C–A
modelling on the RTP-MA are compared with 8 borehole data. The results show
that there is a good correlation between anomalies derived via the C–A
method and the log report of boreholes. Two boreholes were drilled in
magnetic susceptibility concentrations, based on multifractal modelling data
analyses, between 63 533.1 and 66 296 nT. Drilling results showed
appropriate magnetite thickness with grades greater than 20 % Fe. The total
associated with anomalies containing andesite units hosts iron
mineralization.
Introduction
Mineral exploration aims at discovering new mineral deposits in a region of
interest (Abedi et al., 2013). These mineral deposits could be related to
magnetic anomalies which are situated within the underground. In the first
step of identification underground magnetic anomalies, a few boreholes should
be drilled after interpretation of ground magnetic data. Obviously, using new
methods could increase the resolution of the drilling point area
determination and decrease the drilling risk. A cursory look at magnetic maps
would present more information about the shape of such buried features.
However, the information acquired from maps can provide additional details
about the specification of underground magnetic anomalies, especially exact
locations. Magnetic anomaly depends on the inclination and declination of the
body's magnetization generally. Also, we know that the orientation of the
magnetic body depends on magnetic north. According to the mentioned issues,
Baranov (1957) and Baranov and Naudy (1964) proposed a mathematical approach
known as reduction-to-the-pole (RTP) for simplifying anomaly shape and
determining the exact anomaly location. As a result of increasing the
resolution of the RTP technique, the concentration–area (C–A) fractal
method was applied. Fractal geometry is a non-Euclidean geometry established
by Mandelbrot (1983) and has been applied in geosciences and mineral
exploration, especially in geophysical and geochemical exploration since the
1980s (Turcotte, 1989; Bolviken et al., 1992; Korvin, 1992; Cheng et al.,
1994; Agterberg et al., 1996; Cheng, 1999; Turcotte, 2004; Dimri, 2005; Shen
et al., 2009).
In this study, the concentration–area (C–A) fractal method was used to
grid the RTP data set, for better classification of the RTP map, which was
generated by the RTP technique. This procedure was applied to the ground
magnetic data of Qoja-Kandi, Zanjan Province, Iran.
The concentration–area fractal method
The concentration–area (C–A) method serves to illustrate the correlated
relationship between the obtained results. Its most useful features are the
easy implementation and the ability to compute quantitative anomalous
thresholds (Cheng et al., 1994).
RTP classification of magnetic anomalies based on the fractal
method.
Class IDClass range (nT)Priority areas for drilling145 383–47 424.2Very low247 424.2–49 493.7Low349 493.7–56 493.7Moderate456 493.7–63 533.1High563 533.1–66 296Very high
Cheng et al. (1994) proposed the concentration–area (C–A) method for
separating geochemical anomalies from the background in order to characterize
the distribution of elemental concentrations. Equation (1) shows the general
form of this model:
A(ρ≤γ)∝ρ-α1;A(ρ≥γ)∝ρ-α2,
where A(ρ) denotes the area with concentration values greater than the
contour value ρ; υ represents the threshold; and α1
and α2 are
characteristic exponents. Pairs of estimated exponents and corresponding
optimum thresholds (α1 and α2) for this study are presented
in Table 2. The breaks between straight line segments in the C–A
log–log plot and the corresponding values of ρ are known as thresholds
for separating geophysical values into different components representing
different causal factors such as lithological differences, geochemical
processes and mineralizing events (Lima et al., 2003). Thus, applying the
C–A fractal model to the geochemical data improves the resolution of the
data, helping to explore the deposits. It seems that applying this model to
ground magnetic data improves the accuracy of magnetite deposit exploration.
The most useful feature of the C–A method is its capability to compute
anomaly thresholds (Goncalves et al., 2001). Using fractal theory, Cheng et
al. (1994) derived similar power-law relationships and equations in extended
form. The area A (ρ) for a given ρ is equal to the number of
cells multiplied by cell area, with concentration values greater than ρ. Average concentration values are used for those boxes containing more than
one sample. Area concentration A(ρ) with element concentrations greater
than ρ usually shows a power-law relation (Cheng et al., 1994).
Results obtained by using the power-law method and weights of
evidence procedure; α1 and α2 are the exponents of the
power-law relation for concentration values less and greater than the
threshold value (υ), respectively.
Total magnetic intensityPower law W. of Tυα1α2υRTP (nT)60 022 0.0116 0.0458 60 022 The study area and geological setting
Physiographic–tectonic zoning map of Iran's sedimentary basins
(Arian, 2013) and location of the study area.
The Qoja-Kandi area is located within the
Urumieh–Dokhtar magmatic arc in the northwest of
Iran (Fig. 1). This magmatic arc is the most important exploratory area for
metals, and hosts the majority of the larger metal deposits such as copper
and iron (Hassan-Nezhad and Moore, 2006). The investigated area is
characterized by Precambrian to Jurassic units and Oligo-Miocene volcanic
rocks. Different types of metal ore deposits, such as iron, have already been
documented near the studied area. The lithology of this part includes schist
and shale (Kahar formation), dolomite and limestone (Elika formation), shale,
sandstone and limestone (Shemshak formation), limestone, marl, sandstone,
conglomerate and andesite. A magnetite dyke which has outcrops in andesite
units has already been seen near the studied area. It seems that this
magnetite dyke has a presence in the Qoja-Kandi area.
Ground magnetic data analysis
Ground magnetic data are acquired in the region at 15 m spacing along lines
in the northern direction and spaced 10 m apart. GSM-19T proton collected
6997 geophysical ground data. The GSM-19T proton magnetometer has an absolute
accuracy of ±0.2 nT.
The TMI anomaly map
TMI map of Qoja-Kandi with ground magnetic data points.
The total-magnetic-intensity (TMI) map of the Qoja-Kandi area was obtained to
delineate the subsurface anomaly. Figure 2 indicates TMI with ground magnetic
data points. The ground magnetic anomalies range from 38 633 to 69 509 nT
and are characterized by both low and high frequencies of anomalies. The map
reveals that dipolar (anomalies having positive and negative components)
magnetic anomalies have a general E–W direction, which is in the
centre and north of the studied area.
There are three obvious dipolar magnetic anomalies (two anomalies in the east
and west of the centre and one anomaly in the north) in the Qoja-Kandi
prospecting area which are expected to depend on two magnetite dykes in
andesite units.
Reduction-to-the-pole technique
A difficulty in interpretation with TMI anomalies is that they are dipolar
(anomalies having positive and negative components) such that the shape and
phase of the anomaly depends on the part of magnetic inclination and the
presence of any remanent magnetization. Because of the magnetic anomaly
depending on the inclination and declination of the body's magnetization, the
inclination and declination of the local Earth magnetic field, and the
orientation of the body with respect to magnetic north, Baranov (1957) and
Baranov and Naudy (1964) proposed a mathematical approach known as reduction
to the pole for simplifying anomaly shapes.
The reduction-to-the-pole (RTP) technique transforms TMI anomalies to
anomalies that would be measured if the field were vertical (assuming there
is only an inducing field). This RTP transformation makes the shape of
magnetic anomalies more closely related to the spatial location of the source
structure and makes the magnetic anomaly easier to interpret, as anomaly
maxima will be located centrally over the body (provided there is no remanent
magnetization present). Thus, the RTP reduces the effect of the Earth's
ambient magnetic field and provides a more accurate determination of the
position of anomalous sources. It is therefore understood that the total
magnetization direction is equivalent to that of the current-inducing
field.
Before applying the methods, the total field anomaly data were converted to
RTP using a magnetic inclination of 55.43∘ and a declination of
4.93∘. RTP anomalies show three obvious magnetic anomalies (two
anomalies in the east and west of the south and one anomaly in the north) in
the studied area, elongated in an approximate E–W direction. The highest
class of RTP magnetic anomalies (RTP-MA) based on the reduction-to-the-pole
technique is > 55 370.7 nT with 24 941.79 square
metres in area. Also, RTP anomalies
were classified to different populations based on this method, as illustrated
in Fig. 3. Based on this method, drilling points' determination with the RTP
technique was complicated.
RTP map of Qoja-Kandi based on the reduction-to-the-pole technique.
Histogram of RTP-MA data in Qoja-Kandi.
Gaussian curve based on the RTP magnetic anomaly histogram in
Qoja-Kandi.
Application of C–A modelling on the RTP-MA
Log–log plot for RTP-MA data in Qoja-Kandi.
Multifractal models are utilized to quantify patterns such as geophysical
data. Fractal and multifractal modelling are widely applied to distinguish
the different mineralized zones (Cheng, 2007). Multifractal theory could be
interpreted as a theoretical framework that explains the power-law
relationships between areas enclosing concentrations below a given threshold
value and the actual concentration itself. To demonstrate and prove that data
distribution has a multifractal nature, an extensive computation is required
(Halsey et al., 1986). This method has several constraints, especially when
the boundary effects on irregular geometrical data sets are involved
(Agterberg et al., 1996; Goncalves, 2001; Cheng, 2007; Xie et al., 2010).
Multifractal modelling in geophysical and geochemical exploration helps to
find exploration targets and mineralization potentials in different types of
deposits (Yao and Cheng, 2011). The C–A method seems to be equally
applicable to all cases, which means that geophysical distributions mostly
satisfy the properties of a multifractal function. There is some evidence
that geophysical and geochemical data distributions have fractal
behaviour in nature, e.g. Bolviken et
al. (1992), Turcotte (1997), Goncalves (2001), Gettings (2005) and Li and
Cheng (2006). This theory improves the development of an alternative
interpretation validation and useful methods to be applied to geophysical
distribution analysis.
RTP map of Qoja-Kandi based on the C–A method.
RTP map of Qoja-Kandi based on the C–A method with drilled
boreholes.
Three-dimensional RTP map of Qoja-Kandi based on the C–A method
with pictures from magnetite zones in the surface of drilled boreholes 1 and
2, in addition to the mentioned boreholes' log plots.
In this study, 57 307 transformed RTP data were processed for identification
of magnetic anomalies. Statistical results reveal that the RTP-MA mean value
is 48 441 nT, as depicted in Fig. 4, and the RTP-MA domain shows a wide
range. C–A modelling overcomes the distortion effects of outliers on the
traditional techniques and makes it unnecessary to determine whether the
concentration data are drawn from a normal (i.e. Gaussian) distribution or
log–normal distribution, and this advances the analysis resolution of
anomalies (Fig. 5). The RTP-MA distribution map was generated with the
minimum curvature method. The estimated RTP-MA model in terms of RTP data
values was intended to build the C–A log–log plot for RTP-MA. Based on
the linear segments and breakpoints log–log plot, as shown in Fig. 6,
geophysical population were divided. RTP threshold values are 45 383,
47 424.2, 49 493.7, 56 493.7 and 635 331.1, which are very low, low,
moderate, high and very high intensity anomaly threshold values,
respectively, as illustrated in Table 1. Pairs of estimated exponents and the
corresponding optimum thresholds for RTP-MA are presented in Table 2. The
thresholds delineate anomalous areas. Comparison of the areas above and below
the threshold of 6022 nT on the contour map (Fig. 3) with the RTP map shows
significant spatial correlation between the areas with RTP-MA concentrations
above 6022 nT. These geophysical populations were determined based on the
breakpoints in the log–log plot. Actually, the length of the tangent
demonstrates the extents of geophysical populations in the fractal model. It
is mentioned that the number of the population in the fractal model could be
more or less than 5, but actually the extent of the last class population is
not highly dependent on the number of the population in the fractal model.
Hence, there are five populations for RTP-MA, which illustrates that the
fifth class of RTP-MA based on the fractal method is > 63 533.1 nT, with
very high priority for drilling. Consequently, the locations of RTP-MA (two
anomalies) based on the fractal method are situated in the east of the
southern part of the area, as depicted in Fig. 7.
Control with borehole data
A method of investigating subsurface geology is, of course, drilling
boreholes. For a more accurate result about identification of magnetic
anomalies, the results of C–A modelling on the RTP-MA are compared with borehole data (Table 3). There are
eight drilled boreholes in this area that are used for identification of
magnetic anomalies obtained from boreholes (Fig. 8). The drilled boreholes
were analysed and studied by
geologists. Hence, the ranges of magnetite ores in each borehole were
obtained and documented as the log report in Table 2. The accepted lower
limit for the ore length is the grade 20 % Fe total.
Log report of boreholes with RTP classification based on the fractal
method.
BoreholeTotal coreMagnetite thickness (m)Ore/totalMagnetite Priority areasID(m)in total core (grades greatercorerange for drillingthan 20 % Fe total)(m) FromToBH1136.552.40.3819.325.2Very high60.785.2109.4131.4BH2171.247.20.27412.2Very high50.253.5130.6166.3BH3151.2320.2180102High112122BH410612.50.114448Moderate8189.5BH558.900––Very lowBH6136.530.026972LowBH7172140.084447Moderate61.563.5156164BH8157290.187090High133142
RTP transformed data based on ground magnetic anomaly data collected from
C–A moderate anomalies in the Qoja-Kandi prospecting area show a
magnetic susceptibility concentration between 63 533.1 and 66 296 nT, with
1957.64 m2 in area. This study shows that the areas with very high
priority obtained by the C–A method have a magnetite concentration with
the appropriate thickness. This point is significant that boreholes 1 and 2
were drilled in the mentioned places and confirmed the results of the
C–A model (Fig. 9) for increasing the resolution of drilling point
determination and decreasing the drilling risk. Figure 8 shows the 3-D RTP
map of Qoja-Kandi based on the C–A method with pictures from magnetite
zones in the surface of drilled boreholes 1 and 2, in addition to the
mentioned borehole log plots. It is necessary to mention that the TERRA
satellite has a back-looking telescope with a resolution of 15 m in the VNIR
that matches with the wavelength of the band 3 that is used to extract 3-D
information for the provided Fig. 9.
The results confirmed that there is an affirmative correlation between
anomalies derived via the C–A method and the log report of boreholes.
Furthermore, the ratio of the ore length and total core length is calculated
in Table 3. The number of this ratio is between ranges of 0 to 1. Whenever
this number is larger and close to 1, the resolution of the drilling point
determination increases and the drilling risk decreases. The results show a
positive correlation between the ratio of the ore and total core column, and
priority areas for the drilling column. Based on this study, anomalies
associated with andesite units host iron mineralization. Also, there is
no mineralization in other geological units such as limestones
and conglomerates in the northwest of the studied area. It should be noted
that magnetite ores have outcrops in andesite units (Fig. 9).
Conclusions
Separation of magnetic anomalies using a combination of the RTP technique and
C–A fractal modelling has been used in the Qoja-Kandi prospecting area
as a new geophysical method for increasing the resolution of the drilling
points' determination. This study demonstrates that the C–A method
utilized for ground magnetic anomaly separation is an appropriate method for
geophysical prospecting.
There was a multifractal model for RTP-MA, based on log–log plots in the
prospecting area. In this paper, RTP anomaly results from the C–A method
and the RTP technique were compared. Anomalies resulting from the RTP
technique show huge anomalies in three parts, but the C–A method shows
two small anomalies. RTP anomalies based on the RTP technique are similar to
anomalies from the C–A method because of normal distribution in the
Qoja-Kandi area. According to correlation between geological particulars and
RTP anomalies obtained by the C–A method, andesite units host the
anomalies in the studied area.
There is an appropriate correlation between the calculated anomalous
threshold values and ore thicknesses in total cores. Also, the ratio of the
ore length and total core length is related to an anomalous threshold,
calculated with the C–A method. Based on the RTP technique, three anomalies were identified (two RTP anomalies in the east and
west of the southern part of the area and one anomaly in
the northern part). Also,
according to the C–A method, two small anomalies are situated in the
east of the southern part of the prospecting area, with very high priority
for drilling. Boreholes 1 and 2 were drilled in the mentioned places and
confirmed the results of the C–A model for increasing the resolution of
drilling point determination and decreasing the drilling risk.
Hence, studying geophysical magnetic anomalies with the C–A method can
be a proper way for geophysicists to find targets with enriched magnetic
elements. Also, applying C–A log–log can increase the resolution of the
drilling point determination and decrease the drilling risk.
Acknowledgements
The authors would like to thank M. Sahandi for providing data. Also the authors would like to thank A. G. Hunt for his helpful suggestions for improving this paper.
Edited by: A. G. Hunt
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