Investigation of The Distribution and Fe Content of Iron Sand at Wari Ino Beach Tobelo Using Resistivity Method with Werner-Schlumberger Configuration

Received: December 7th, 2019 Accepted: April 24th, 2020 Published: April 30th, 2020 This research aimed to investigate the distribution, volume, and concentration of iron sand at Wari Ino Beach Tobelo. The resistivity method with Werner-Schlumberger configuration was applied to investigate the iron sand distribution. The measurements were set-up on 3 lines that run parallel along the coast of Wari Ino Village. The length of each trajectory was 150 meters with a spacing of 10 meters for each electrode. Data acquisition was carried out by using geoelectric instruments to obtain current injec tion (I) and voltage (V). The analysis was carried out by using RES2DINV and ROCKWORK software to obtain 2-D and 3-D cross-section models for interpreting the distribution and volume of the iron sand. The analysis and interpretation were supported by geological data of the location. Furthermore, the Fe content was characterized by using X-Ray Fluorescence Spectroscopy (XRF). There results show that the volume of the iron sand in each trajectory was 109,355 m; 180,254 m; and 120,556 m. The total volume of iron sand along the three trajectories was up to 405,335 m 3 . The Fe content in the form of a free element is 67.41%, 57.12%, and 73.40%. The Fe content in the form of hematite mineral (Fe2O3) was 57.92%, 45.82%, and 65.47%.


INTRODUCTION
Iron sand contains abundant Fe element in the form of iron oxide minerals, i.e. magnetite (Fe 3 O 4 ), hematite (α-Fe 2 O 3 ), and maghemite (γ-Fe 2 O 3 ). Other minerals such as Aluminum oxide (Al 2 O 3 ), Silica dioxide (SiO 2 ), Phosphorus pentaoxide (P 2 O 5 ), Calcium oxide (CaO), Titanium dioxide (TiO 2 ), Vanadium pentaoxide (V 2 O 5 ), Chromium (III) oxide (Cr 2 O 3 ), and Manganese oxide (MnO) also have been investigated exist in the iron sand (Malega et al., 2018). Generally, the primary minerals contained in the iron sand are Magnetite  (Lawan et al., 2018). Those minerals can be separated into a pure monophase mineral for the specific applications. Hence, they have high economic value and impact on the development of the mining industry in Indonesia. The fabricated iron minerals in the form of iron metal and steel are very useful for many applications, such as fabrication of components for electronic devices, manufacturing, construction sector, building, vehicles industry as well as for the automotive sector. According to (The Indonesia Iron and Steel Industry Association, 2017), Indonesia has been experienced an increasing domestic steel consumption were 12.70 million tons in 2016 and projected to be 16. 00; 17.00; 18.00; 19.10; 20.20; and 21.40 million tons in 2020 to 2045 in its year in ASEAN, Indonesia has the lowest steel consumption In 2020, Indonesia's steel consumption is predicted achieving of 84 kg/capita that lower than ASEAN steel consumption about 157 kg/capita. This increase in steel consumption throughout its multiplier effect should be concerned by the government of Indonesia.
In a different case, the synthesized iron sand in the form of iron oxide into any kind of nano-materials also has a high economic impact. The synthesis of nanomaterials from iron sand is the basis for manufacturing new types of functional materials to support advance technologies, such as MRI contrast agent, drug delivery agent, biosensor optic, magnetic sensor, catalyst material, nanoadsorbent, antimicrobial, anticancer materials, as well as nano-imaging probe (Bolívar & González, 2019;Dang et al., 2018;Rahimnia et al., 2019;Sardana et al., 2018).
Therefore, the exploration of the iron sand is very important for the development of the iron sand industry in Indonesia in the future. Indonesia has a lot of iron sand sites where they can be explored to be able supplying the iron sand raw material. The iron sand industry will be a future strategic and potential sector for Indonesia as the last detail explanation.
In addition, Pusat Sumber Daya Geologi (PSGD) Ministry of Energy and Mineral Resources has reported in 2013 that there are 67 sites where the iron sand distributed in Indonesia (Suprapto & Sunuhadi, 2014). There are many sites elsewhere the iron sand deposits not explored yet, for an instant a site at Wari Ino Beach, Tobelo Halmahera Utara. A pilot study by (Malega et al., 2018) had conducted only for identifying the minerals contained in iron sand at Wari Ino Beach by using X-Ray Fluorescence (XRF) Spectroscopy method. The research reported that the concentration of Fe mineral is about 87.5% of the iron sand mass.
Up to the date no publication provides information about the distribution and volume of the iron sand in Wari Ino Beach. Consequently, we investigated the distribution and volume of the iron sand in this site. By knowing the distribution, volume, and Fe content will allow further researchers to do exploration and also being a database for local government, especially the Department of Natural Resources, Mines and Energy, Maluku Utara for further iron sand industry strategic plan in Halmahera Utara.
The According to a research paper by , the distribution and volume of the iron ore in Eastern Binangun Coastal Cilacap. Those data were interpreted base on the resistivity data obtained by using Werner configuration. Another paper by  had reported the distribution of the iron sand in the western coastal area of Nusawungu, Cilacap Regency by applying the resistivity method with Schlumberger configuration. The results showed that the iron sand deposits are at depths of 2.39-25.25 meters with resistivity values of 12.24-46.96 Ωm.
Werner configuration is only suitable for survey mapping of the iron sand distribution along the horizontal trajectory, but no more information about a sounding on the vertical direction. On the other hand, Schlumberger configuration is only suitable for identifying the vertical configuration of the iron sand .
In this research, we applied the Werner-Schlumberger configuration due to the configuration to find out the information both mapping and sounding of the iron sand volume and distribution under the surface instantaneously as explained by (Akmam et al., 2019;Jamaluddin & Umar, 2018). We have been successfully obtained the 2-D cross-section contour of lower rock surface resistivity along the trajectories. In addition, we also obtained the Pseudo 3-D crosssectional area of iron sand in the trajectories. These results are potentially useful for further consideration in exploring iron sand at Wari Ino Beach.

Resistivity Method
The geoelectric method can be used well if there is a resistivity contrast between mediums. Contrast can be a medium that is relatively conducive to a non-conductive medium, or there are lithological differences. Each type of rock subsurface has different resistivity values. Each resistivity depends on the density, moisture content, minerals, salt content, and rock porosity . The work of the geoelectric method is by flowing direct current or alternating low frequency into the earth medium through two current electrodes, then measuring the potential difference arising through the two potential electrodes. (Naveen et al., 2015)explained that the distance between the electrodes can vary according to the area specification and the topography, so that the resistivity value can be calculated through Ohm's law. Suppose that R is the resistance (Ohm); A is the cross-sectional area of the medium (m 2 ), and L is the length of the medium (m), then according to Figure 1, the resistivity of the medium, ρ (Ωm) can be determined by using equation 1, According to Ohm's law that the resistance R of the medium mathematically can be calculated by using equation 2, such as Following the potential difference between edges of the medium ∆V and the injected current I, so the resistivity can be calculated by equation 3, Equation 3 applies to a homogeneous medium, so the result obtained is true resistivity. But in practice, the object measured is the earth or soil that is not homogeneous because of different types of resistors, so that the measured resistivity is apparent resistivity. The apparent resistivity value depends on the resistivity of the layers forming the formation and the configuration of the electrodes used. Pseudo resistivity ρ a is formulated by equation 4, Where K is the geometrical factor.

Wenner-Schlumberger Configuration
The Wenner-Schlumberger configuration is a configuration with a constant spacing system with a note that the comparison factor n for this configuration is the ratio of the distance between the current electrode (AB) and the potential (MN). If the potential electrode distance MN is a distance of the AB electrode is (2na + a) (Figure 2). This configuration is a combination of the Wenner configuration and the Schlumberger configuration. In measurements with a space factor n = 1, the Wenner-Schlumberger configuration is the same as the measurement in the Wenner configuration (distance between electrodes = a), but in measurements with n = 2 and so on, the Wenner-Schlumberger configuration is the same as the Schlumberger configuration (the distance between current electrode and potential electrode are greater than the distance between potential electrodes).
The Wenner-Schlumberger configuration has a penetration depth of up to one-third of the distance between C1 and C2. Commonly, the average penetration depth is up to 90 meters and Wenner configuration only reaches 80 meters. Variable n is multiple to show the observed layer's layers. The geometry factors of the Wenner-Schlumberger can be calculated by using equation 5, ( )a is the distance between the electrodes P 1 and P 2 , n is the ratio between the electrode distances C 1 -P 1 and P 1 -P 2 (e.g 4a, then n = 4) so that the resistivity value of all obtained from the measurement results is equal to equation 6, ( ) I V Δ a n π ρ a 1 + = (6) Generally, rock resistivity values have been obtained through various direct measurements and can be used as a reference in interpreting the results of field resistivity measurements; this is because certain resistivity values will be associated with geological conditions in the measurement area (Table 1).  According to (Sunarya et al., 2017), characteristics of iron sand deposits are generally stand-alone, but not infrequently also found in association with other rock minerals. The resistivity value of metal minerals can also be used as a reference in interpreting the results of measurements, where magnetic is a metal mineral with the highest Fe content, but the amount is small while hematite is the most needed iron mineral in the iron industry.

Technical Procedures
The research was conducted by direct observation and measurement in the field.
The research location is along the coast of Wari Ino Village, TobeloSubdistrict, North Halmahera Regency. Measurements were made on 3 lines that run parallel along the coast of Wari Ino Village, the length of each trajectory is 150 meters with a spacing of 10 meters for each electrode (Figure 3). The research process was started from the preparation, study of literature, data acquisition, processing, analysis, and interpretation by software. Graphically, the research procedure can be displayed in Figure 4. The preparation phase includes preparation of tools and study materials, literature study includes a review of previous research that is geological data on location and Fe content data based on a microstructural synthesis of Fe 3 O 4 in the iron sand of Wari Ino beach area, Tobelo sub-district Halmahera Utara, the acquisition of data includes the implementation of data collection activities in the field, in this case, uses a geoelectric tool to obtain current injection value (I) and voltage value (V), current and voltage injection value data is then processed to obtain resistivity values which are then reprocessed using RES2DINV software to obtain 2-D cross-section models (Jamaluddin & Umar, 2018) and ROCKWORK software forget a 3-D crosssection model. This software works based on the inversion technique related to the last square method (Akmam et al., 2019; E. S. Pratiwi et al., 2019). The RES2DINV is an appropriate algorithm that considered good for 2D resistivity data inversion, and there is great scientific literature where the RMS of 2D electrical imaging is greater than 10% (Satriani et al., 2012). The results of the processing in the form of a cross-section are then analyzed and interpreted to determine the distribution and estimation of the volume of iron sand along the coast of Wari Ino, Tobelo sub-district, North Halmahera district, this analysis and interpretation are supported by geological data of the location.
The Fe concentration in each iron sand sample is characterized by X-Ray Fluorescence Spectroscopy (XRF). The concentration of Fe in the form of Fe element and its compound can be identified by using this technique as reported by (Malega et al., 2018).

Data Collection & Acquisition
Processing data using RES2DIV Software

2-D Cross-section & Resistive Inversion Model
Making Initial Model using RockWork Software

RESULTS AND DISCUSSION Geology of Research Location
The physiography of the research area is divided into 3 parts, West Halmahera Mandala, East Halmahera Mandala, and Quaternary Volcano (Chandra & Hall, 2016). Rock dominance in the study area includes Alluvium, Tufa, Andesite, Sediment, Clays, Napal, Conglomerate, Ultramafic, Breccia, and Metamorphic Rocks ( Figure 5).

The Distribution of Iron Sand
Measurement data of observations were obtained a current (I), voltage (∆V), apparent resistivity (ρ), datum point data, spaces, number of layers. The processing by RES2DINV software for the three trajectories that run parallel along the coast of Wari Ino village. The results of 2-D inversion then saved into a format (.xyz). Data with the format (.xyz) consists of the accumulation of electrode distances, resistivity values, depth of current penetration, and subsurface conductivity based on measurement results.
Trajectory 1: inversion of resistivity data were presented in 2-D cross-section ( Figure  6). Based on the color resistivity contrast of the surface, the blue to light green has a resistivity value (ρ) smaller than 10 Ωm, this layer interpreted as a layer of iron sand deposition at a depth of 2.5 -25 m below the surface, forming spots deposits are scattered at a distance of 15-110 m with an average thickness of 10 m. Also, variations in yellow and orange color with a range of resistivity values between 20 Ωm -50 Ωm, the layer was at a depth of 7 -12 m below the surface and the rock layer is interpreted as a clay rock. Then, the red to purple with a resistivity range between 55 Ωm -150 Ωm at a depth of 15-25 m below the surface was interpreted as a layer of breccia rocks scattered throughout the study area Based on RES2DINV processing data, it can be seen that the distribution of iron sand deposits in trajectory 1 is spread along the research track forming sediment spots. The processing data in the form of format (.xyz) is reprocessed using ROCKWORK software to find out the volume (Figure 7), it appears that the distribution of iron sand deposits processed by ROCKWORK has synchronized with the results shown by RES2DINV processing results. Based on the calculation of the volume with a resistivity value limit between 0-10 Ωm the volume of iron sand trajectory 1 is 109,355 m 3 . Trajectory 2: based on the inversion results of the 2-D cross-sectional resistivity data (Figure 8), visible resistivity contrast with dark blue to light blue is interpreted as a layer of iron sand deposition (ρ <10 Ωm) on trajectory 2 the distribution of iron sand deposits is not seen as spots, but spread evenly along the second track starting from a distance of 15-130 m, at a depth of 2.5-15 meters below the surface. Variation of resistivity contrast of the second trajectory indicated the clay rock at a depth of 12-18 m below the surface, with a green color (30 μm <ρ<80 μm). Also, breccia rocks spreading along the trajectory, shown based on resistivity contrast with yellow, red to purple (ρ> 100 Ωm). The resistivity data of pseudo-crosssection 3-D based on RES2DINV (Figure 9) shows the existence of the same spread pattern between 2-D cross-section inversion results and 3-D Cross-section, where the deposition of iron sand is spread evenly along the general track on the surface and not forming spots. The volume of iron sand deposits has a greater amount of about 180,254 m 3 . Trajectory 3: processing the 2-D crosssection inversion (Figure 10) shows the presence of iron sand deposits spread along path 3 by forming spots at a depth of 2.5-15 m below the surface, based on contrasting resistivity values with blue to light blue image (ρ <10 Ωm). Cross-section 2-D inversion cross-section also shows the variation of resistivity contrast with a range of values between 20 Ωm -60 Ωm (dark green to an orange color), it interpreted as a clay rock layer that spread along the track at a depth of 2.5 -25 meters below the surface. Line 3 also shows indications of breccia rocks forming spots at a depth of 25 meters below the surface with resistivity values between 70 Ωm -150 Ωm (red to a purple).  Combined 3-D Model: to obtain the overall volume of iron sand spread in the form of sediment along the coast of Wari Ino, it is necessary to combine resistivity contrast values in the form of inversion format (.xyz) into the 3-D form using ROCKWORK software ( Figure 12). The results of the combination of these three tracks will produce a pseudo-3-D model. Therefore, we can easily see the pattern of distribution of iron sand deposits along the coast of Wari Ino. Based on the 3-D crosssection model, a low resistivity value contrast with a range of ρ <10 Ωm values is interpreted as an iron sand precipitate, besides, it also shows a high resistivity contrast value (10 Ωm<ρ <52 Ωm). Thus clearly visible distribution patterns of iron sand deposits that trending from west to east of the study area. Figure 13 is a profile of a 3-D pseudo section that already has a limit value of ρ <10 Ωm which is interpreted as the distribution of iron sand deposits with the total volume reaching 405,335 m 3 . In short, the Wenner-Schlumberger configuration has been successfully applied to investigate the distribution and volume of the iron sand both for mapping and sounding methods. This method is powerful to obtain a 2D-model for the distribution and the 3D-model of the volume of the iron sand, as reported by (Jamaluddin & Umar, 2018;Sunarya et al., 2017).

The Fe Content in Iron Sand Samples
The elemental composition of raw iron sand samples for each trajectory can be presented in Figure 14. These results only a general profile of the Fe content in samples that are taken only in one location for every trajectory. In this research, the iron sand samples were collected only from the surface of the beach. The XRF spectra ( Figure 15)   According to Figure 14 and Figure 15, other elements and their compounds have been also identified in iron sand samples. Silica (Si) in the form of free element and compound of SiO 2 has a large composition after Fe. Other elements such as Al, P, Ca, K, Ti, V, Cr, Mn, Cu, Zn, Rb, Eu, Rb, and Br have smaller composition than 6.0%. These results are in agreement with the previous research reported by (Malega et al., 2018).

CONCLUSION
The investigation of the distribution and concentration of iron sand at Wari Ino Beach Tobelo has carried out by using the resistivity method with the Werner-Schlumberger configuration. The total volume of iron sand deposit distributed along 450 m of the Wari Ino beach line and under 25 m in deep is 405,335 m 3 . The iron sand volume for each trajectory is 109,355 m 3 ; 180,254 m 3 ; and 120,556 m 3 , consecutively for trajectory 1, 2, and 3. The Fe content in the form of a free element is 67.41%, 57.12%, and 73.40%. Furthermore, the Fe content in the form of Fe 2 O 3 for the consecutive trajectories is 57.92%, 45.82%, and 65.47%. These novel results confirm that a potential exploration of the iron sand might be done and a database has been obtained for local government, especially the Department of Mining and Energy Service, Maluku Utara for further iron sand industry strategic plan in Halmahera Utara.

ACKNOWLEDGMENTS
Authors would like thank to the Government of Indonesia especially to the Ministry of Research and the Higher Education Republic of Indonesia toward PDP Research Scheme contract no. 118/SP2H/LT/DRPM/ 2019. Further, the author also thanks Universitas Halmahera toward LPPMP and Agency of the Environment North Halmahera for the facilities during the research.

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