3-D Gravity Anomaly Inversion Based on Improved Guided Fuzzy C-Means Clustering Algorithm

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 p...

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Veröffentlicht in:Pure and applied geophysics Jg. 177; H. 2; S. 1005 - 1027
Hauptverfasser: Liu, Sheng, Jin, Shuanggen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.02.2020
Springer Nature B.V
Schlagworte:
Algorithms
Anomalies
Approximation
Clustering
Earth
Earth and Environmental Science
Earth Sciences
Entropy
Geophysics
Geophysics/Geodesy
Gravity
Gravity anomalies
Mathematical models
Parameters
Property values
Real estate
Resource exploration
Search algorithms
Search process
Shrinkage
discrete-valued inversion
Fuzzy entropy
parameter search
fuzzy
gravity inversion
means algorithm
ISSN:0033-4553, 1420-9136
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Zusammenfassung: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.
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ISSN:0033-4553
1420-9136
DOI:10.1007/s00024-019-02306-0