assessment of relative changes in tectonic activity in the...

Journal of Tethys: Vol. 3, No. 4, 297–310 ISSN: 2345–2471 ©2015

Heidari-Aghagolet al., 2015 Available online at http://jtethys.org

Assessment of relative changes in tectonic activity in the northern part of the fault Ardekul (Eastern Iran)

Masoud Heidari-Aghagol1,*, Mohammad Mehdi Khatib1,Ebrahim Gholami1, Nazanin Shahsavani1

1- Department of Geology, University of Birjand, Birjand, Iran.

* Corresponding Author: [email protected] Abstract

Ardekoul fault zone which is along NNW-SSE is located in eastern Iran and northern part of the Sistan subzone. Dextral strike-slip fault zone of Ardekuolis made of six main segments including Korizan (28 Km), BohnAbad (8.8 Km), Abiz (32 Km), Gazkoon (20 Km), Moein Abad (30.4 Km), GhalMaran (12 Km), and two minor segments including OlangMorgh (12.8 Km) and SehPestan (9.6 Km). The studied region in its northern part (From North to the South) is included of: Korizan, Bohn Abad and Abiz .The Parameters and their variation range are :SMF (Krizan:1.18, Bohn Abad:1.21 and Abiz:1.29), Facet%(0.725,0.771 and 0.941),S(1.04,1.13 and 1.16),V(0.97,0.83 and 0.63), SL(191.7,306.13 and 394.43), topography transverse symmetry index changes(T)(0.42,0.32 and 0.36), drainage basin asymmetry index changes(AF)(0.51,0.32 and 0.36), Reviewing uplift alluvial fan through satellite images and longitudinal profile, waterway fractal values ranging (1.982,1.946 and 1.902),Fractal fracture ranges(1.482,1.362 and 1.221). Neotectonic indices showed that the relative activity of each segment is different. The rate of activity has been reduced from middle part of each segment totheir terminal. Generally, Ardakul fault activity rate from north to south is on the rise. We then conclude that the energy dissipation on the Ardekoul fault is linear. Although it has different amount, but it generally has a linear distribution from the North to the South. According to the neotectonic indicators the section which is studied is placed in the A class (Tectonic activities are very high). Keywords:Gold and silver, Artificial neural network, Invasive weed optimization algorithm, Multiple linear regression.

1- Introduction

Gold is the main mineral resource in most countries because of its role in economic, industries and public culture. So, most exploration project in gold thread defined. The most important test that defines the presence or absence of mineralization is drilling. High cost of drilling and analysis of samples provide a model that has good agreement with reality. Sometimes it needs to decrease the cost and time exploration project implementation. Mineral prospectively mapping (MPM) and prediction of ore grade is main contributor to this work. Statistical methods such as

kriginginterpolation, weight of evidence and logistic regression has been applied in this field. Soft computing is also used as modern modeling in gold exploration projects. Skabar applied MPM to mapping gold mineralization potential in the Castlemaine region of Victoria, Australia, and results are compared with a method based on estimating probability density functions and using a range of geological, geophysical and geochemical input variables. Harris and Pan used probabilistic neural network (PNN) to classify mineralized and nonmineralized cells using eight geological, geochemical, and geophysical variables. This investigation demonstrates that a probabilistic neural network

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changes in them indicate these changes are new. Disconnection, displacement and rising of these features which are done by the fault, indicate area tectonically is being active (Amersonet al.,2007). Tectonic variables affect their construction and position (Bull, 1977; Harvey, 1987). Alluvials surface tilt is also under the influence of tectonic control. Morphological traits of the alluvial surface may be used as a sign of tectonical activities (Bull, 1972 and 2009,). At first, to study for fans, 20 fans were chosen that located near of the fault zone (Fig.4). All the quantitative data’s such as the area, tilt, height, oriented cone factor and Longitudinal and transverse profiles were calculated by the software and satellite pictures (Table 5). Pictures highlighted the alluvial right-handed rotation along the Abiz fault. By all these quantitative data we could conclude that the uplifting amount of all these alluvials increase forming of the north to the south, moreover they seem to be more in the middle of each section comparing with the its margin.

Fan comicality index describes the difference between the shape of fans in study area and shape of ideal one (Mokhtariet al., 2003). The mentioned ration has been calculated for the Korizan, Bohn Abad and Abiz faults which

clearly show that the activity decreases from the middle to the margin of the uplifting area and generally it is increasing from the north to the south (Fig. 5). The mean of rising for three segments Korizan, Bohn Abad and Abiz is respectively 1193, 1205 and 1214 (Table 5).

Table 4) Neo-tectonic indexes calculated in each region.

Index Abiz Bohn Abad Korizan Smf 1.29 1.21 1.18 %Facet 0.725 0.771 0.9491 Vf 0.27 0.137 0.1219 V 0.63 0.83 0.97 S 1.26 1.13 1.06 Sl 394.43 306.13 191.7

In the tectonically active areas Alluvial fans are also affected and go under the uplifting process accuse to the uplifting’s cause by the tectonical movements. Variation of rising rate in fans can indicate variation of rate of tectonic activity (of the fault) in the area. The histograms of the alluvial fans of the each section clearly show that the uplifting in the middle of each section is much more than its margins. The height mean line in the columns shows that the uplifting amount from the Korizan region (Northern Part) to the Abiz (Southern part) is increasing (Fig.4).

Figure 4) Elevation changes in three Korizan changes (Blue column), Bohn Abad (red column), and Abiz (gray column).

301



Table 5) the calculated index for the studied Alluvial fans.

Height Radius (Km)

Cone shrinkage ratio Average slope Environment Area

(Km2) No Fault

1180 1.2 %78 %5.55 12.23 8.65 1

Korizan

1178 1.48 %84 %5.85 9.6 5.68 2

1194 0.24 %81 %6.85 1.42 0.09 3

1207 0.42 %81 %8.4 2.48 0.24 4

1221 0.48 %78 %10.8 3.35 0.48 5

1218 0.66 %83 %5.95 4.48 1.02 6

1186 0.35 %82 %14.3 2.08 0.15 7

1194 0.76 %88 %12.3 4.4 0.98 8

1177 0.54 %92 %13.3 3.1 0.39 9

1180 0.74 %89 %9.45 3.72 0.75 10

1185 2.7 %96 %8.4 17.8 20.5 11

Bohn Abad 1227 0.52 %91 %10.7 16.7 18.6 12

1213 0.82 %83 %12.4 14.3 15.2 13

1198 1.8 %97 %14.4 15.7 13 14

1196 1.52 %86 %7 12.6 6.2 15

Abiz

1188 1.53 %87 %9.7 12.01 6.9 16

1268 1.9 %84 %7.4 1.0.82 6.5 17

1230 3.4 %82 %7.04 17.1 18.9 18

1205 1.89 %90 %4.6 11.6 7.9 19

1201 2.1 %91 %6.4 17.1 19.1 20

The β and also the Cone oriented index (Fig. 5) diagram shows that the β changes are more from the Korizan to the Abiz part , while the mean line of this index changes shows the decrease from the Korizan to the Abiz region. The Cone oriented index changes diagram indicates less change than the β diagram.

4. Fractal analysis of Ardekoul fault

Based on Euclidean geometry, Geometric elements are point, line, page and volume which are represented by the geometric dimensions (that represent infinite ones) in order 0, 2, 1 and 3, however, all these parameters are definitely finite in the materialist nature. Therefore, the dimensions of Euclidean geometry can well reflect the characteristics of the phenomena and compare them with each other. While the fractal dimension can be decimals, so there would be no limitation on the measuring of any natural phenomena.

General relation of the fractal dimension is:

Nn=c/rnD (2)

In this formula Nn is the number of known variables for a phenomena, rnthe specific line, C constant and D the fractal. Torket square method (1998) is used as the most efficient method to measure Fractal distribution fault and also the fractal (D). In this method, at first the desired structure for the fractal, like waterway fault, seismic data measured. Then, the studied region is reticulated in the squares with the different scales; after that the squares with any kind of scale (Ns) will be counted and finally set as a table. In order to calculate the fractal, we have to count the (Ns) amount in at least five networks with the different length. Finally, the fractal will be counted by the formula of the Logarithm-Logarithm diagram:

Log (Ns) =aldol (I/S) (3)

that means, D line ration is the fractal (Mandelbort, 1983).

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, 297–310

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6- Conclusions

According to the information obtained from neo-tectonic indicators, quantitative studies related to alluvial fans, fractal fracture, waterways, drainage density maps and seismic data we can conclude that energy dissipation on the Abiz fault in performing in a linear way, although the depreciation has different values, it generally has a linear distribution from the south to the north. Seismic exploration carried out shows that most release energy focuses on segments of the fault are located in middle part of them. This study specifies that each piece operates separately, although in the terminal connection part of the Korizan fault to Dasht- e- Bayaz fault the seismic data accuse to the very same operation of the two faults increases. But in general, the density scales of the centers from the north to the south increases which accordingly increase the activity rate from the north to the south.

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Received: 24September 2015 / Accepted: 25November 2016 / Published online: 29November 2015

EDITOR–IN–CHIEF:

Dr. VahidAhadnejad: Payame Noor University, Department of Geology.PO BOX 13395–3697, Tehran, Iran. E–Mail: [email protected]

EDITORIAL BOARD:

Dr. Jessica Kind: ETH Zürich Institut für Geophysik, NO H11.3, Sonneggstrasse 5, 8092 Zürich, Switzerland E–Mail: [email protected] Prof. David Lentz University of New Brunswick, Department of Earth Sciences, Box 4400, 2 Bailey Drive Fredericton, NB E3B 5A3, Canada E–Mail: [email protected] Dr. Anita Parbhakar–Fox School of Earth Sciences, University of Tasmania, Private Bag 126, Hobart 7001, Australia E–Mail: [email protected] Prof. Roberto Barbieri Dipartimento di Scienzedella Terra e Geoambientali, Università di Bologna, Via Zamboni 67 – 40126, Bologna, Italy E–Mail: [email protected] Dr. Anne–Sophie Bouvier Faculty of Geosciences and Environment, Institut des science de la Terre, UniversitédeLausanne, Office: 4145.4, CH–1015 Lausann, Switzerland E–Mail: Anne–[email protected] Dr. MatthieuAngeli The Faculty of Mathematics and Natural Sciences, Department of Geosciences, University of Oslo Postboks 1047 Blindern, 0316 OSLO, Norway E–Mail: [email protected] Dr. MilošGregor Geological Institute of DionysStur, MlynskaDolina, Podjavorinskej 597/15 DubnicanadVahom, 01841, Slovak Republic E–Mail: [email protected] Dr. Alexander K. Stewart Department of Geology, St. Lawrence University, Canton, NY, USA E–mail: [email protected] Dr. Cristina C. Bicalho

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Environmental Geochemistry, Universidade Federal Fluminense – UFF, Niteroi–RJ, Brazil E–mail: [email protected] Dr. LenkaFindoráková Institute of Geotechnics, Slovak Academy of Sciences, Watsonova 45,043 53 Košice, Slovak Republic E–Mail: [email protected]

Dr. Mohamed Omran M. Khalifa Geology Department, Faculty of Science, South Valley, Qena, 83523, Egypt E–Mail: [email protected] Prof. A. K. Sinha D.Sc. (Moscow), FGS (London). B 602, VigyanVihar, Sector 56, GURGAON 122011, NCR DELHI, Haryana, India E–Mail: [email protected]

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