Three-dimensional Joint Inversion of Gravity and Magnetic Anomalies Using Fuzzy C-means Clustering

Teranishi, Yutaka; Mikada, Hitoshi; Goto, T.; Takekawa, Junichi

doi:10.3997/2214-4609.20130955

Cited by 3 publications

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“…The difference from the formulation of Teranishi et al (2013) is that two smoothness regularization parameters  g and  m are integrated into one parameter  which controls weight of membership smoothness in the FCM clustering. To optimise regularization parameters  and  in the FCM clustering, we applied the L-curve criterion (Arai, 2003) to the FCM clustering. We remark that incorporating smoothness regularization into FCM clustering enables us to save inversion computation time considerably.…”

Section: Joint Inversion With Fcm Clustering Constraintsmentioning

confidence: 99%

“…In this study, we set q = 2.0, which is widely accepted as a good choice (Hathaway and Bezdek, 2001). Teranishi et al (2013) formulated the joint inversion algorism by incorporateing equation (1) with (2) and considered a coupling measure based on the FCM clustering as a petrophysical similarity regularization term which combined two model parameters and they obtained the following equation;…”

Section: M) and V I Is The I Th Cluster Center (Vmentioning

confidence: 99%

“…The FCM clustering technique has an advantage that it does not necessitate any empirical equation but deals with a linear combination of the influence from multiple clusters given a piece of data to belong to plural clusters in the parameter space formed by the petrophysical parameters. Teranishi et al (2013) also indicates the effectiveness of joint inversion of gravity and magnetic data using the FCM clustering technique.…”

Section: Introductionmentioning

confidence: 99%

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Three-Dimensional Joint Inversion of Gravity and Magnetic Anomalies Using Fuzzy C-means Clustering

Teranishi¹,

Mikada²,

Goto³

et al. 2014

Recent Advances in Exploration Geophysics

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We present a 3D joint inversion method to estimate two physical parameters, density and magnetization of subsurface materials using field intensity measurements. In the method, we introduce the fuzzy c-means (FCM) clustering technique to relate gravity with magnetic data. In the approach, the subsurface structure is discretized to a set of rectangular prisms. For estimating the density and magnetization of each prism, we minimize the quadratic norm of the residuals between the observed data and the calculated responses from inversion model. Two regularization terms, i.e. the roughness and the similarity of the two physical parameters, are introduced in our joint inversion to control the degree of model roughness and similarity. We determine their regularization parameters using the L-curve criterion. We apply our method to a numerical model which represents submarine massive sulphides (SMS). The joint inversion results, which have the advantages of both gravity and magnetic inversion, show better accuracy and resolution than the individual ones.

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Section: Joint Inversion With Fcm Clustering Constraintsmentioning

confidence: 99%

Section: M) and V I Is The I Th Cluster Center (Vmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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