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Ant colony optimisation inversion of surface and borehole magnetic data under lithological constraints

机译:岩性约束下地表和井壁磁数据的蚁群优化反演

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The ant colony optimisation algorithm has successfully been used to invert for surface magnetic data. However, the resolution of the distributions of the recovered physical property for deeply buried magnetic sources is not generally very high because of geophysical ambiguities. We use three approaches to deal with this problem. First, the observed surface magnetic data are taken together with the three-component borehole magnetic anomalies to recover the distributions of the physical properties. This cooperative inversion strategy improves the resolution of the inversion results in the vertical direction. Additionally, as the ant colony tours the discrete nodes, we force it to visit the nodes with physical properties that agree with the drilled lithologies. These lithological constraints reduce the non-uniqueness of the inversion problem. Finally, we also implement a K-means cluster analysis for the distributions of the magnetic cells after each iteration, in order to separate the distributions of magnetisation intensity instead of concentrating the distribution in a single area. We tested our method using synthetic data and found that all tests returned favourable results. In the case study of the Mengku iron-ore deposit in northwest China, the recovered distributions of magnetisation are in good agreement with the locations and shapes of the magnetite orebodies as inferred by drillholes. Uncertainty analysis shows that the ant colony algorithm is robust in the presence of noise and that the proposed approaches significantly improve the quality of the inversion results. (C) 2014 Elsevier B.V. All rights reserved.
机译:蚁群优化算法已成功用于表面磁数据的反演。但是,由于地球物理上的歧义,对于深埋的电磁源,恢复的物理性质的分布的分辨率通常不是很高。我们使用三种方法来解决此问题。首先,将观测到的表面磁数据与三分量钻孔磁异常结合起来,以恢复物理特性的分布。这种合作反演策略提高了垂直方向反演结果的分辨率。此外,随着蚁群巡视离散节点,我们迫使其访问具有与钻探岩性一致的物理特性的节点。这些岩性约束减少了反演问题的非唯一性。最后,我们还对每次迭代后的磁胞分布进行了K均值聚类分析,以分离磁化强度的分布,而不是将分布集中在单个区域中。我们使用综合数据测试了我们的方法,发现所有测试均返回了令人满意的结果。以中国西北部的芒库铁矿床为例,其磁化强度的分布与钻孔推断的磁铁矿体的位置和形状非常吻合。不确定性分析表明,蚁群算法在存在噪声的情况下是鲁棒的,并且所提出的方法显着提高了反演结果的质量。 (C)2014 Elsevier B.V.保留所有权利。

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