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Application of Global Particle Swarm Optimization for Inversion of Residual Gravity Anomalies Over Geological Bodies with Idealized Geometries

机译:全局粒子群优化在地质体与理想几何形状中剩余重力异常反演的应用

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摘要

A global particle swarm optimization (GPSO) technique is developed and applied to the inversion of residual gravity anomalies caused by buried bodies with simple geometry (spheres, horizontal, and vertical cylinders). Inversion parameters, such as density contrast of geometries, radius of body, depth of body, location of anomaly, and shape factor, were optimized. The GPSO algorithm was tested on noise-free synthetic data, synthetic data with 10% Gaussian noise, and five field examples from different parts of the world. The present study shows that the GPSO method is able to determine all the model parameters accurately even when shape factor is allowed to change in the optimization problem. However, the shape was fixed a priori in order to obtain the most consistent appraisal of various model parameters. For synthetic data without noise or with 10% Gaussian noise, estimates of different parameters were very close to the actual model parameters. For the field examples, the inversion results showed excellent agreement with results from previous studies that used other inverse techniques. The computation time for the GPSO procedure is very short (less than 1 s) for a swarm size of less than 50. The advantage of the GPSO method is that it is extremely fast and does not require assumptions about the shape of the source of the residual gravity anomaly.
机译:开发了全局粒子群优化(GPSO)技术,并应用于由埋地的物体引起的剩余重力异常的反演,具有简单的几何形状(球形,水平和垂直圆柱体)。优化了反演参数,例如几何形状的密度对比,身体半径,身体深度,异常位置,形状因子,以及形状因子。 GPSO算法在无噪声合成数据,具有10%高斯噪声的合成数据中测试,以及来自世界不同部分的五个现场示例。本研究表明,即使在优化问题中允许改变形状因子,GPSO方法也能够准确地确定所有模型参数。然而,该形状被固定为先验以获得各种模型参数的最一致的评估。对于没有噪声或10%高斯噪声的合成数据,不同参数的估计非常接近实际模型参数。对于现场示例,反演结果表明,与先前研究的结果表明,使用其他逆技术的结果。 GPSO过程的计算时间非常短(小于1 s),群体尺寸小于50. GPSO方法的优点是它非常快,不需要关于源的形状的假设残余重力异常。

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