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Interpretation of gravity anomalies of Idealised bodies using global Particle swarm optimization technique

机译:用全球粒子群优化技术解读理想体的重心异常

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Amplitude coefficient factor (ACF), Shape factor (SF) and depth are standard qualitative parameter for interpreting various geometrical bodies. There are several optimization methods are available to invert the gravity anomaly caused by various geometrical bodies. But still the interpretation of gravity anomalies needs more accurate and realistic model parameter for better interpretation. To overcome the above problem, global Particle swarm optimization (PSO) is validated to synthetic gravity anomalies over various geometrical bodies and finally applied to field gravity data of various terrains to find the gravity parameters ACF, SF and depth of the geometrical bodies. Number of exercises has made to optimize the parameters such as ACF, SF and depth using various iterations. The optimized parameters have been compared with published results obtained by different methods that show a significantly good agreement with real model parameter. Thus PSO is an efficient and more robust technique to achieve optimal solution with minimal error.
机译:幅度系数因子(ACF),形状因子(SF)和深度是用于解释各种几何体的标准定性参数。有几种优化方法可用于反转由各种几何体引起的重力异常。但仍然对重力异常的解释需要更准确和现实的模型参数,以便更好地解释。为了克服上述问题,全局粒子群优化(PSO)被验证到各种几何体上的合成重力异常,最后应用于各种地形的场重力数据,以找到Geachity参数ACF,SF和地几何体的深度。使用各种迭代,练习数量是为了优化ACF,SF和深度等参数。已经将优化的参数与来自不同方法获得的公开结果进行了比较,该方法与真实模型参数显着良好的协议。因此,PSO是一种有效且更强大的技术,可以实现最小的误差。

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