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

Teranishi, Y.; Mikada, Hitoshi; Goto, T.; Takekawa, J.

doi:10.3997/2352-8265.20140160

Cited by 1 publication

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“…There are several applications of FCM to geophysical inversion (in which FCM is imposed in the objective function as a constraint term) applied to synthetic data of seismic topography and gravity data Li 2011, 2015;Lelièvre, Farquharson and Hurich 2012;Carter-McAuslan, Lelièvre and Farquharson 2015), seismic reflection and magnetotelluric (MT) data (Kieu 2017) and gravity and magnetic data (Teranishi et al 2013;Li and Sun 2016;Li 2016, 2017;Maag and Li 2018). FCM has been used as a tool to analyse multiple data sets in order to build a unified geological model (Paasche and Tronicke 2007;Paasche, Tronicke and Dietrich 2010).…”

Section: Introductionmentioning

confidence: 99%

Seismic‐impedance inversion with fuzzy clustering constraints: an example from the Carlin Gold District, Nevada, USA

Kieu

Kepic

2019

Geophysical Prospecting

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The seismic reflection method provides high-resolution data that are especially useful for discovering mineral deposits under deep cover. A hindrance to the wider adoption of the seismic reflection method in mineral exploration is that the data are often interpreted differently and independently of other geophysical data unless common earth models are used to link the methods during geological interpretation. Model-based inversion of post-stack seismic data allows rock units with common petrophysical properties to be identified and permits increased bandwidth to enhance the spatial resolution of the acoustic-impedance model. However, as seismic reflection data are naturally bandlimited, any inversion scheme depends upon an initial model, and must deal with non-unique solutions for the inversion. Both issues can be largely overcome by using constraints and integrating prior information. We exploit the abilities of fuzzy c-means clustering to constrain and to include prior information in the inversion. The use of a clustering constraint for petrophysical values pushes the inversion process to select models that are primarily composed of several discrete rock units and the fuzzy c-means algorithm allows some properties to overlap by varying degrees. Imposing the fuzzy clustering techniques in the inversion process allows solutions that are similar to the natural geologic patterns that often have a few rock units represented by distinct combinations of petrophysical characteristics. Our tests on synthetic models, with clear and distinct boundaries, show that our methodology effectively recovers the true model. Accurate model recovery can be obtained even when the data are highly contaminated by random noise, where the initial model is homogeneous, or there is minimal prior petrophysical information available. We demonstrate the abilities of fuzzy c-means clustering to constrain and to include prior information in the acoustic-impedance inversion of a challenging magnetotelluric/seismic data set from the Carlin Gold District, USA. Using fuzzy c-means guided inversion of magnetotelluric data to create a starting model for acoustic-impedance proved important in obtaining the best result. Our inversion results correlate with borehole data and provided a better basis for geological interpretation than the seismic reflection images alone. Low values of the acoustic impedance in the basement rocks were shown to be prospective by geochemical analysis of rock cores, as would be predicted for later gold mineralization in weak, decalcified rocks.

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