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Hajian, Alireza
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Hajian, Alireza
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- PublicationRestrictedDepth estimation of cavities from microgravity data using a new approach: the local linear model tree (LOLIMOT)(2012-06)
; ; ; ; ; ;Hajian, A.; Department of Geophysics, Science and Research Branch, Islamic Azad University, Tehran, Iran ;Zomorrodian, H.; Department of Geophysics, Science and Research Branch, Islamic Azad University, Tehran, Iran ;Styles, P.; Applied and Environmental Geophysics Group, Keele University, Keele ST5 5BG, UK ;Greco, F.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Lucas, C.; Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Department, University of Tehran, Iran; ; ; ; 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.358 62 - PublicationOpen AccessMonitoring magma migration at Mt. Etna using the Seismic Amplitude Ratio Analysis methodThe Seismic Amplitude Ratio Analysis method (SARA) was applied to data recorded during six days before the May 13, 2008 eruption of Mt. Etna to test its potential as a forecasting attribute. By using this method, the magma migration path, as well as the seismic migration, can be determined with the amplitude of continuous data recorded at least at one pair of stations from a seismic network near the eruption site. Due to the sudden changes in the seismic amplitude ratio calculated for each pair of stations, the seismic migration trend, as well as the magma path at depths, were clearly detected before the main eruption. The start and end times of the seismic swarms were also determined. The standard practice to achieve similar results is to use volcanic tremors, which must be pre-selected thus reducing efficiency and increasing the time needed. By using the whole seismic signal, the method provides a simpler semi-automated alternative, especially for places where it is not possible to record tremors continuously. This simple method is useful to reduce uncertainties relative to hazardous magma propagation during volcanic unrest, as it helps to improve the accuracy of locating seismic swarms and it allows determining the direction of magma movement at depth before the eruption. We also analyzed the amplitude ratio trend using Mann- Kendall and Sen’s estimator test. The results of these tests confirmed a positive and increasing trend from the day before the eruption in most pairs of stations.
103 52 - PublicationOpen AccessClassification of Mount Etna (Italy) Volcanic Activity by Machine Learning Approaches(2019)
; ; ; ; ; ; ; Assessment of the ongoing activity of volcanoes is one of the key factors to reduce volcanic risks. In this paper, two Machine Learning (ML) approaches are presented to classify volcanic activity using multivariate geophysical data, namely the Decision Tree (DT) and K-Nearest Neighbours (KNN). The models were implemented using a data set recorded at Mount Etna (Italy), in the period 01 January 2011 – 31 December 2015, encompassing lava fountain events and intense Strombolian activity. Here a data set consisting of five geophysical features, namely the root-mean-square of seismic tremor (RMS) and its source depth, counts of clustered infrasonic events, radar RMS backscattering power and tilt derivative, was considered. Model performances were assessed by using a set of statistical indices commonly considered for classification approaches. Results show that between the investigated approaches the DT model is the most appropriate for classification of volcano activity and is suitable for early warning systems applications. Furthermore, the comparison with a different classifier approach, reported in literature, based on Bayesian Network (BN), is performed.658 96