عنوان مقاله [English]
نویسندگان [English]چکیده [English]
Bouguer anomaly fields are often characterized by a broad, gently varying, regional anomaly on which local anomalies with shorter wavelengths may be superimposed. In gravity surveying, it is usually the local anomalies that are of prime interest and the first step of interpretation is to remove the regional field to isolate the residual anomalies. Several analytical methods of regional field analysis are available which include the trend surface analysis (fitting a surface to the observed data in geophysical surveys) and low-pass filtering. Because the regional changes have a large extent, the regional trend is mostly smooth and uniform. As a result, the trend surface analysis is a good method for identifying regional and residual anomalies. Trend surface analysis is a method for removing regional anomalies in geophysical surveying. In this method, the coordinates of the points are independent variables and the dependent variable is the measured value. Such procedures must be used critically as fictitious residual anomalies and sometimes arise when the regional field is subtracted from the observed data due to the employed mathematical procedures. In this paper a trend surface method is proposed that uses Particle Swarm Optimization (PSO) algorithm for the most desirable surface. PSO algorithm is based on the individual (i.e., particles or agents) behavior of a swarm. Its roots are in zoologist’s modeling of the movement of individuals (e.g., fishes, birds, or insects) within a group. It has been noticed that members within a group seem to share information among them, a fact that leads to increased efficiency of the group. The PSO algorithm searches in parallel, using a group of individuals similar to other. The main idea in this method is to model and simulate the movement and behavior of birds in food searching. Each particle in Particle Swarm Optimization algorithm is one candidate for final result in problem's multidimensional space. The main advantages of the PSO algorithm are summarized as: simple concept, easy implementation, robustness to control parameters, and computational efficiency when compared with mathematical algorithm and other heuristic optimization techniques. The function that must be optimized is an equation for the nearest surface to the measured data as the objective function requires a standard length. For Description objective function need to standard lengths and norm is one of the length standards. The L1 norm is the best norm that can be used until gently resulting accurate. But in the methods used so far, L1 norm cannot be used because of employing derivatives in an optimization process. Procuring of the nearest result to the actual value of the regional anomalies is an advantage of this method. At first, this algorithm was coded in MATLAB software and then it was run on the gravity data measured during a gravity surveying around Neka and Ghaem Shahr cities in Mazandaran province. The results were compared by the least squares method and Geosoft software. The L2 norm is used in this method. Simulation results show a better convergence to the optimum surface of this algorithm rather than the least squares method. Providing the optimum surface with different norms and steps is another advantage of this algorithm.