Ground magnetic anomaly separation using the reduction-to-the-pole (RTP)
technique and the fractal concentration–area (

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 (

In this study, the concentration–area (

The concentration–area (

RTP classification of magnetic anomalies based on the fractal method.

Cheng et al. (1994) proposed the concentration–area (

Results obtained by using the power-law method and weights of
evidence procedure;

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 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

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.

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

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.

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

RTP map of Qoja-Kandi based on the

RTP map of Qoja-Kandi based on the

Three-dimensional RTP map of Qoja-Kandi based on the

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.

A method of investigating subsurface geology is, of course, drilling
boreholes. For a more accurate result about identification of magnetic
anomalies, the results of

Log report of boreholes with RTP classification based on the fractal method.

RTP transformed data based on ground magnetic anomaly data collected from

The results confirmed that there is an affirmative correlation between
anomalies derived via the

Separation of magnetic anomalies using a combination of the RTP technique and

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

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

Hence, studying geophysical magnetic anomalies with the

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