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Abstract

We aim to estimate the depth of subsurface cavities from gravity data by a new method through a Multiple Adaptive Neuro Fuzzy Interference System (MANFIS); this method is an intelligent way to interpret microgravity data and gain an estimation of depth and shape of the most probable cavities. The MANFIS model was trained for two main models of cavities: sphere and cylinder in the related domains of radius and depth. We tested different MANFIS’s with different number of rules and obtained the optimum value for number of in the hidden layer. Then it was tested in the presence of 20% Gaussian noise and showed good robusnesst to noise. The method was also tested for real microgravity data from Bahamas Free Port. The results are in good agreement with ground-thruthed drilled values for the depth of subsurface cavities.

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/content/papers/10.3997/2214-4609.20144374
2011-09-12
2024-03-29
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.20144374
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