Meysam Argany; Amir Ramezani; Sadegh Elyasi
Abstract
Extended Abstract
Introduction
Remote sensing science is one of the most powerful tools for the mineral explorations and mineral resource estimation. With regard to this science, any type of rocks with structural characteristics and mineral constituents has a special spectral signature, thus, using ...
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Extended Abstract
Introduction
Remote sensing science is one of the most powerful tools for the mineral explorations and mineral resource estimation. With regard to this science, any type of rocks with structural characteristics and mineral constituents has a special spectral signature, thus, using remote sensing techniques, different types of rocks in a particular area can be recognizable based on their reflective characteristics. Remote sensing techniques are considered as one of the standard methods in geological studies due to the identification of spatial patterns of rocks as well as their speed and economic price. Pervious geological studies indicate that the study area mostly contains basalt, limestone and marble, which has resulted in physical and chemical degradation of basalt stones under the influence of some geological events. Some parts containing basalt have lost their qualities due to these degradations. Therefore, the classification and separation of high-quality basalt zones from low-quality zones is the main objective of this paper.
Materials and method
The main objective of this study is to identify high-quality basalt zones in the Dir-o-Morreh mine located 50 kilometers from Tehran city near the lake of Hoz-e-Soltan. Basalt is a dark-colored and fine-grained igneous rock composed mainly of plagioclase and pyroxene minerals. Typically, this type of rock is formed externally or in the presence of air, such as the flow of lava, and these rocks can also take form intrusively like igneous dikes or narrow pillars. The basalt in the Dir-o-morreh mine is of igneous dike basalt type. In this study, the ASTER satellite multi-spectral images were used. These images allow us to have a good spatial and spectral resolution with regard to the objectives. However, reflectance conversion and atmospheric corrections were carried out on these images before using them, in order to enhance the accuracy of the project. Aerosols contained in the atmosphere are liquid or solid particles suspended in the air, which are very important in the evaluation of satellite imagery for remote sensing. After applying pre-processing, Basalt Exploration Index (BEI) was introduced and used to identify the basalt. The BEI index has been extracted using various sources, including the basalt spectral signature provided by the department of applied mathematics and statistics of Johns Hopkins University, ASTER satellite behavior (defined by the space team of NASA and Japan) and the Earth’s data which were collected to validate the results. This index has been able to identify different basalt zones, including major extraction zones and other potentially possible zones. Moreover, this index is able to completely separate the basalt zones from the surrounding areas (mainly limestone, marble and clay rocks). At the next step, convolution and morphology filters have been applied to separate high-quality Basalt zones from the low-quality. The amount of the brightness of an output pixel from the Convolution filters is a function of weighted average of the brightness of its surrounding pixels. Using convolution with the selected kernel in satellite imagery returns a new filtered spatial image. High-pass Standard convolution filter was used in this study, which eliminates low frequencies of an image by retaining the high frequencies. The morphological nuclei used in this study are only the structural elements of this project and should not be confused with convolution kernels. In order to control the obtained results, the classified zones were double-checked on the field.
Results
The results obtained from the field studies and the identified zones are appropriately consistent with each other using the proposed index. Supervised classification was applied to improve the level of assurance and accuracy. Supervised classification is based on the idea that the user can select sample pixels in an image representing certain classes and then use image processing software using these educational samples as the referral for the classification of all other pixels in the image. This classification algorithm can be very effective and accurate and classifies satellite images in pixel-based or object-oriented form. Supervised classification can result in the preparation of two maps in two different classifications, which is has been done by using the Maximum Likelihood Algorithm. MaxVer or Maximum Likelihood is a statistical classification method that takes the weight of average value of the distance between the classes into consideration, using statistical parameters. To achieve sufficient accuracy, this algorithm requires a number of educational samples or pixels (more than 30). The primary classification includes 5 types of rocks or classes: high-quality basalt, low-quality basalt, limestone, marble stone, and clay which are designated on the map. In order to increase accuracy of the proposed method, the second map was prepared with 3 different classes (low-quality basalt, High-quality basalt, and surrounding rocks) in the second stage.
Conclusion
These maps help us in preparing a new BEI which is more accurate and more capable. It was also able to prove its capability in the latest ground operations and determining the most zones with high-quality basalt.
Meysam Argany; Farid Karimipour; Fatemeh Mafi
Abstract
Extended Abstract ...
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Extended Abstract Introduction Wireless Sensor Networks (WSNs) are widely used for monitoring and observation of dynamic phenomena. A sensor in WSNs covers only a limited region, depending on its sensing and communicating ranges, as well as the environment configuration. For efficient deployment of sensors in a WSN, the coverage estimation is a critical issue. Probabilistic methods are among the most accurate models proposed for sensor coverage estimation. However, most of these methods are based on raster representation of the environment for coverage estimation which limits their quality. In this paper, we propose a probabilistic method for estimation of the coverage of a sensor network based on raster models, and 3D vector representation of the environment. Then, the performance of global approaches are evaluated, and the 3D vector model is used as an appropriate model. Materials and Methods Recent advances in electro mechanical and communication technologies have resulted in the development of more efficient, low cost and multi-function sensors. These tiny and ingenious devices are usually deployed in a wireless network to monitor and collect physical and environmental information such as motion, temperature, humidity, pollutants, traffic flow, etc. The information is then communicated to a process center where they are integrated and analyzed for different application. Deploying sensor networks allows inaccessible areas to be covered by minimizing the sensing costs compared to the use of separate sensors to completely cover the same area. Sensors may be spread with various densities depending on the area of application and details and quality of the information required. Despite the advances in sensor network technology, the efficiency of a sensor network for collection and communication of the information may be constrained by the limitations of sensors deployed in the network nodes. These restrictions may include sensing range, battery power, connection ability, memory, and limited computation capabilities. These limitations have been addressed by many researchers in recent years from various disciplines in order to design and deploy more efficient sensor networks. Efficient sensor network deployment is one of the most important issues in sensor network filed that affects the coverage and communication between sensors in the network. Nodes use their sensing modules to detect events occurring in the region of interest. Each sensor is assumed to have a sensing range, which may be constrained by the phenomenon being sensed and the environment conditions. Hence, obstacles and environmental conditions affect network coverage and may result in holes in the sensing area. Communication between nodes is also important. Information collected from the region should be transferred to a processing center, directly or via its adjacent sensor. In the latter case, each sensor needs to be aware of the position of other adjacent sensors in their proximity. In recent years, Wireless Sensor Networks (WSN) has been studied in several applications such as monitoring and control different criteria from smart cities and intelligent transportation to land use planning and environmental monitoring. Sensor deployment for achieving the maximum coverage is one of the important issues in WSN. Hence, several optimization algorithms to achieve maximum coverage are used in the majority of researches. Discussion and Results In a general classification, optimization algorithms for the sensor deployment with the aim of increasing coverage, are divided into local and global optimization algorithms. The feature of global algorithms is their randomness based on an evolutionary process. In all of these algorithms, the calculation of the sensor network coverage is essential as a target function. In fact, coverage improvement is done according to the coverage calculation method. In the previous researches, a simple model was considered as the environmental model for network sensors. In this research, raster and vector modeling in 2 and 3-dimensional spaces and the optimization algorithms of global performance for optimizing the sensor layouts were compared evaluated. errorIn this study, two-dimensional and three-dimensional vector models were used as a precise environmental model. Most of the models in the previous studies considered the coverage to be binary (i.e. a point is covered by a sensor or not). For realistic modeling, this study considers the coverage as an issue, which means that the amount of coverage obtained based on parameters such as distance and angle of the sensor is expressed as a percentage between zero and one hundred. errorIn fact, all sensors are not sensed in the same way and will vary according to their various parameters. Since the purpose of this study is to compare the performance and ability of global optimization algorithms, it is therefore assumed that the study area has equal conditions. In this paper, several optimization methods such as genetic algorithms, L-BFGS, VFCPSO and CMA-ES have been implemented to optimize the location of sensors. In this study, various sensor sensing types such as omnidirectional binary sensing model, directional sensing model and probabilistic sensing model have been used and tested for the aforementioned optimization algorithms in different Raster and Vector study areas. Conclusion This paper was focused on comparing the performance of four global optimization algorithms to optimize deployment of sensors in environment using more spatial details compared to previous approaches. The innovation of this study was to use 3D raster and vector data and to implement the global optimization methods using probabilistic sensing model to optimize sensor network placement. Finally, promising results have been presented and discussed and future methods were introduced.