Publication Date:
2017-04-04
Description:
In this paper an attempt is made to estimate depth and shape parameters of subsurface cavities from
microgravity data through a new soft computing approach: the locally linear model tree, known as the
LOLIMOT algorithm. This method is based on locally linear neuro-fuzzy modelling, which has
recently played a successful role in various applications over non-linear system identification.
A multiple-LOLIMOT neuro-fuzzy model was trained separately for each of the three most common
shapes of subsurface cavities: sphere, vertical cylinder and horizontal cylinder. The method was then
tested for each of the cavity shapes with synthetic data. The model’s suitability for application to real
cases was analysed by adding random Gaussian noise to the data to simulate several levels of uncertainty
and the results of LOLIMOT were compared to both multi-layer perceptron neural network and leastsquares
minimization methods. The results showed that the LOLIMOT algorithm is more robust to noise
and is also more precise than either the multi-layer perceptron or least-squares minimization method.
Furthermore, the method was tested with microgravity data over a selected site located in a major
container terminal at Freeport, Grand Bahamas, to estimate cavity depth and was compared to the
results achieved by least-squares minimization and multi-layer perceptron methods. The proposed
method can estimate cavity parameters more accurately than the least-squares minimization and
multi-layer perceptron methods.
Description:
Published
Description:
221 - 234
Description:
2.6. TTC - Laboratorio di gravimetria, magnetismo ed elettromagnetismo in aree attive
Description:
JCR Journal
Description:
restricted
Keywords:
neuro-fuzzy
;
05. General::05.01. Computational geophysics::05.01.02. Cellular automata, fuzzy logic, genetic alghoritms, neural networks
Repository Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Type:
article
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