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首页> 外文期刊>Pure and Applied Geophysics >3-D Gravity Anomaly Inversion Based on Improved Guided Fuzzy C-Means Clustering Algorithm
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3-D Gravity Anomaly Inversion Based on Improved Guided Fuzzy C-Means Clustering Algorithm

机译：基于改进的引导模糊C型聚类算法的3-D重力异常反转

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摘要

The geophysical inversion with combining prior information is very important for resource exploration and studies of the Earth's internal structure. Guided fuzzy C-means clustering inversion (FCM) is normally applied for the Tikhonov regularized inversion, but has the shortcoming of uniform model parameter shrinkage, leading to inaccuracy. In this paper, an improved guided fuzzy clustering algorithm is proposed by adding a fuzzy entropy term to the original guided FCM. This method not only enforces the discrete values to a high degree of approximation by guiding the recovered model to cluster tightly around the known petrophysical property values, but also calculates the distributed characteristics of the model parameter set. Based on this method, the shortcoming of uniform shrinkage of the original guided FCM clustering algorithm is improved, and more accurate inversion results are obtained, making the FCM method more efficient and broadly applicable. Furthermore, a new parameter search algorithm is proposed to accelerate the search speed. The results recovered by using this method with three kinds of theoretical gravity anomaly data show more accurate density anomalies compared with the results generated from the original guided FCM clustering inversion and greater efficiency in the parametric search process when using the new parameter search algorithm. The improved FCM clustering algorithm could enable more extensive and efficient use of gravity inversion.
机译：结合事先信息的地球物理反演对于地球内部结构的资源勘探和研究非常重要。导向模糊C-Means聚类反演（FCM）通常适用于Tikhonov正规反转，但具有均匀模型参数收缩的缺点，导致不准确。在本文中，通过将模糊熵术语添加到原始引导FCM来提出改进的引导模糊聚类算法。该方法不仅通过将恢复的模型引导到围绕已知的岩石物业值紧密地集群来强制执行离散值，而且计算模型参数集的分布式特性。基于该方法，提高了原始引导FCM聚类算法的均匀收缩的缺点，获得了更准确的反演结果，使FCM方法更有效和广泛适用。此外，提出了一种新的参数搜索算法来加速搜索速度。通过使用三种理论重力异常数据使用该方法恢复的结果与使用新参数搜索算法的原始引导FCM聚类反演和参数搜索过程中的更高效率相比，比较了更精确的密度异常数据。改进的FCM聚类算法可以实现更广泛而有效的重力反演。

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