Integration of Geoelecrtical information layers by fuzzy method to choose the best point for drilling: a case study Hamyj, Birjand


1 Ph.D. Student of Mining Exploration, Amir Kabir University, Tehran, Iran

2 M.Sc. Student of Mining Exploration, Sahand University of Technology, Tabriz, Iran

3 Ph.D. Student of Mining Exploration, Sahand University of technology, Tabriz, Iran


Resistivity and induced polarization methods are used in exploration of porphyry metals for years. The resistivity method is used in the study of horizontal and vertical discontinuities in the electrical properties of the ground. The induced polarization method makes use of the capacitive action of the subsurface to locate zones where conductive minerals are disseminated within their host rocks. The simplicity of the equipment, the lower cost of the survey compared to the other methods and the abundance of interpretation methods make it a popular method. There are many methods to interpret resistivity and induced polarization data. Inversion method is one of the most popular methods. This method was reported as early as the 1930s. Visual and analytical methods are used for the interpretations which are used for simple structure such as faults. These methods require a certain degree of symmetry and are suitable only for simple geological situations. Complex resistivity distributions cannot be solved by analytical methods and must numerical techniques be used. But these data alone cannot determine the location of anomalies precisely. One of the main concerns of interpreters is the selection of optimum drilling points by the results of surveys. The selection of optimum drilling point have an important impact on the reduction the cost and risk, The selection of these points can be done by integration of geoelectrical and structural data. By the progress of the computer science, many methods for processing of geophysical data were also developed. These methods include fuzzy, neural networks and genetic algorithms methods. Fuzzy method is based on following steps: 1-layer fuzzy information, 2- fuzzy inference, 3- deterministic output. Fuzzy layers of information are done by membership function, and there are different kinds of membership functions.
In this study resistivity and induced polarization data have been collected using dipole-dipole array in mining index of Hamyj mine located in Birjand. Hamyj mine index that is the result of remote sensing and economic geology surveys show promising mineralization in this mine. This area comprises of volcanic rocks as andesit, dasite, volcanic breccia, metamorphic tuff. The dasit extensions in the study area are more frequent. For in detail study and modeling of the mineralization the geoelctrical study with Electrod spacing of 20m has been used. Then inversion of geoelecrtical data for a profile based on Gaussian- Newton and Newton’s methods were carried out. In this study, the models have been integrated based on knowledge-driven fuzzy logic method in a GIS environment. The best profile was chosen based on the Spriman correlation. Because the resistivity and induced polarization have negative correlation, so the profile 3 was selected as the best profile by highest negative correlation. Results of inverse modeling show that in this profile the resistivity of the left and right side of this profile are different. This difference in resistivity represents the chalcopyrite in the study area. The boundary of the difference is an attribute to a fault that is located 230m from the left side. Also induced polarization data have shown a high value of chalcopyright. In fact the fault existence in the mentioned location was approved by geological surveys. The effect of layers information of resistivity and induced polarization a decision, fuzzy functions were selected for them. Also a large membership function was used for the induced polarization. Also, small membership function was used for resistivity data. For faults in this study a 5 meters distance as buffer zone was applied. The fuzzy gamma operator was also used. This operator is a combination of multiplication and addition operator. The value of fuzzy gamma operator was selected 0.85 for the integration of layers information. The result of the fuzzy map, was converted to binary map and on this map, decision maker could easily select the optimum area. Finally, based on the results of the fuzzy modeling, the best point for drilling was proposed. Using this type of integration in order to determine the best drilling points in for porphyry copper exploration, appears to be a trusty method. Based on the fuzzy modeling for the study area the best drilling point was chosen near the fault in the study area. The results show that the optimum exploration borehole location is the point that is located at a distance 240m from the left side of the profile. Also in this study the maximum depth of drilling was calculated 50m. The applied fuzzy method in this surveyed profile with 400m length and proposed a 60m length as a promising mineralization area with high confidence. Also, in this method expert viewpoint could be applied in the procedure of integration. This method has high flexibility in integration of also information layers and could simultaneously use several information layers. This technique has simple mathematical relationship. By the results of this study this method can be used for other geophysical data.