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首页> 外文期刊>Journal of Applied Geophysics >Model parameter estimations from residual gravity anomalies due to simple-shaped sources using Differential Evolution Algorithm
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Model parameter estimations from residual gravity anomalies due to simple-shaped sources using Differential Evolution Algorithm

机译:使用差分演化算法从简单形状源产生的剩余重力异常中估算模型参数

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An efficient approach to estimate model parameters from residual gravity data based on differential evolution (DE), a stochastic vector-based metaheuristic algorithm, has been presented. We have showed the applicability and effectiveness of this algorithm on both synthetic and field anomalies. According to our knowledge, this is a first attempt of applying DE for the parameter estimations of residual gravity anomalies due to isolated causative sources embedded in the subsurface. The model parameters dealt with here are the amplitude coefficient (A), the depth and exact origin of causative source (zo and xo, respectively) and the shape factors (q and 17). The error energy maps generated for some parameter pairs have successfully revealed the nature of the parameter estimation problem under consideration. Noise-free and noisy synthetic single gravity anomalies have been evaluated with success via DE/best/1/bin, which is a widely used strategy in DE. Additionally some complicated gravity anomalies caused by inultiple source bodies have been considered, and the results obtained have showed the efficiency of the algorithm. Then using the strategy applied in synthetic examples some field anomalies observed for various mineral explorations such as a chromite deposit (Camaguey district, Cuba), a manganese deposit (Nagpur, India) and a base metal sulphide deposit (Quebec, Canada) have been considered to estimate the model parameters of the ore bodies. Applications have exhibited that the obtained results such as the depths and shapes of the ore bodies are quite consistent with those published in the literature. Uncertainty in the solutions obtained from DE algorithm has been also investigated by Metropolis-Hastings (M-H) sampling algorithm based on simulated annealing without cooling schedule. Based on the resulting histogram reconstructions of both synthetic and field data examples the algorithm has provided reliable parameter estimations being within the sampling limits of M-H sampler. Although it is not a common inversion technique in geophysics, it can be stated that DE algorithm is worth to get more interest for parameter estimations from potential field data in geophysics considering its good accuracy, less computational cost (in the present problem) and the fact that a well-constructed initial guess is not required to reach the global minimum. (C) 2016 Elsevier B.V. All rights reserved.
机译:提出了一种基于差分演化(DE)的基于残差重力数据估计模型参数的有效方法,该方法是一种基于随机矢量的元启发式算法。我们已经证明了该算法在综合和现场异常中的适用性和有效性。根据我们的知识,这是将DE用于残余重力异常的参数估计的首次尝试,这是由于埋入地下的孤立原因引起的。此处处理的模型参数是振幅系数(A),致病源的深度和确切来源(分别为zo和xo)和形状因数(q和17)。为某些参数对生成的误差能量图已成功揭示了所考虑的参数估计问题的性质。已经通过DE / best / 1 / bin成功评估了无噪声且有噪声的合成单重力异常,这是DE中广泛使用的策略。此外,还考虑了由不完整的源体引起的一些复杂的重力异常,并且所获得的结果表明了该算法的有效性。然后,使用在合成实例中应用的策略,考虑了在各种矿物勘探中观察到的一些野外异常,例如铬铁矿(古巴卡马圭区),锰矿(印度纳格普尔)和贱金属硫化物矿床(加拿大魁北克)。估算矿体的模型参数。应用已经表明,所获得的结果,例如矿体的深度和形状与文献中公开的结果是相当一致的。通过Metropolis-Hastings(M-H)采样算法基于模拟退火而无需冷却时间表,还研究了从DE算法获得的解决方案的不确定性。基于合成和现场数据示例的直方图重构,该算法提供了可靠的参数估计值,该估计值在M-H采样器的采样范围内。尽管它不是地球物理学中的常见反演技术,但可以说DE算法值得从地球物理中的潜在场数据中获取参数估计的更多兴趣,因为它具有良好的准确性,较低的计算成本(在当前问题中)以及事实不需要结构合理的初始猜测即可达到全局最小值。 (C)2016 Elsevier B.V.保留所有权利。

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