Works matching AU Aleardi, Mattia
Results: 26
Analysis of Different Statistical Models in Probabilistic Joint Estimation of Porosity and Litho-Fluid Facies from Acoustic Impedance Values.
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- Geosciences (2076-3263), 2018, v. 8, n. 11, p. 388, doi. 10.3390/geosciences8110388
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Ensemble-Based Electrical Resistivity Tomography with Data and Model Space Compression.
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- Pure & Applied Geophysics, 2021, v. 178, n. 5, p. 1781, doi. 10.1007/s00024-021-02730-1
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Application of Reversible Jump Markov Chain Monte Carlo Algorithms to Elastic and Petrophysical Amplitude-Versus-Angle Inversions.
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- Pure & Applied Geophysics, 2020, v. 177, n. 7, p. 3335, doi. 10.1007/s00024-020-02436-w
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Reservoir Characterization Through Target-Oriented AVA-Petrophysical Inversions with Spatial Constraints.
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- Pure & Applied Geophysics, 2019, v. 176, n. 2, p. 901, doi. 10.1007/s00024-018-2009-4
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Application of a global–local full‐waveform inversion of Rayleigh wave to estimate the near‐surface shear wave velocity model.
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- Near Surface Geophysics, 2023, v. 21, n. 1, p. 21, doi. 10.1002/nsg.12243
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Machine learning‐accelerated gradient‐based Markov chain Monte Carlo inversion applied to electrical resistivity tomography.
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- Near Surface Geophysics, 2022, v. 20, n. 4, p. 440, doi. 10.1002/nsg.12211
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Stochastic electrical resistivity tomography with ensemble smoother and deep convolutional autoencoders.
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- Near Surface Geophysics, 2022, v. 20, n. 2, p. 160, doi. 10.1002/nsg.12194
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A hybrid residual neural network–Monte Carlo approach to invert surface wave dispersion data.
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- Near Surface Geophysics, 2021, v. 19, n. 4, p. 397, doi. 10.1002/nsg.12163
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A geostatistical Markov chain Monte Carlo inversion algorithm for electrical resistivity tomography.
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- Near Surface Geophysics, 2021, v. 19, n. 1, p. 7, doi. 10.1002/nsg.12133
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Transdimensional and Hamiltonian Monte Carlo inversions of Rayleigh‐wave dispersion curves: a comparison on synthetic datasets.
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- Near Surface Geophysics, 2020, v. 18, n. 5, p. 515, doi. 10.1002/nsg.12100
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Towards the Identification of an In Vitro Tool for Assessing the Biological Behavior of Aerosol Supplied Nanomaterials.
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- International Journal of Environmental Research & Public Health, 2018, v. 15, n. 4, p. 563, doi. 10.3390/ijerph15040563
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Estimating petrophysical reservoir properties through extended elastic impedance inversion: applications to off-shore and on-shore reflection seismic data.
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- Journal of Geophysics & Engineering, 2018, v. 15, n. 5, p. 1, doi. 10.1088/1742-2140/aac54b
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Application of different classification methods for litho-fluid facies prediction: a case study from the offshore Nile Delta.
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- Journal of Geophysics & Engineering, 2017, v. 14, n. 5, p. 1, doi. 10.1088/1742-2140/aa7301
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A feasibility study on the expected seismic AVA signatures of deep fractured geothermal reservoirs in an intrusive basement.
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- Journal of Geophysics & Engineering, 2014, v. 11, n. 6, p. 1, doi. 10.1088/1742-2132/11/6/065008
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A probabilistic full waveform inversion of surface waves.
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- Geophysical Prospecting, 2024, v. 72, n. 9, p. 3448, doi. 10.1111/1365-2478.13595
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A computationally efficient Bayesian approach to full‐waveform inversion.
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- Geophysical Prospecting, 2024, v. 72, n. 2, p. 580, doi. 10.1111/1365-2478.13437
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Stochastic inversion of time‐lapse electrical resistivity tomography data by means of an adaptive ensemble‐based approach.
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- Geophysical Prospecting, 2024, v. 72, n. 1, p. 268, doi. 10.1111/1365-2478.13464
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Probabilistic inversions of electrical resistivity tomography data with a machine learning‐based forward operator.
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- Geophysical Prospecting, 2022, v. 70, n. 5, p. 938, doi. 10.1111/1365-2478.13189
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A gradient‐based Markov chain Monte Carlo algorithm for elastic pre‐stack inversion with data and model space reduction.
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- Geophysical Prospecting, 2021, v. 69, n. 5, p. 926, doi. 10.1111/1365-2478.13081
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Combining discrete cosine transform and convolutional neural networks to speed up the Hamiltonian Monte Carlo inversion of pre‐stack seismic data.
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- Geophysical Prospecting, 2020, v. 68, n. 9, p. 2738, doi. 10.1111/1365-2478.13025
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Markov chain Monte Carlo algorithms for target‐oriented and interval‐oriented amplitude versus angle inversions with non‐parametric priors and non‐linear forward modellings.
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- Geophysical Prospecting, 2020, v. 68, n. 3, p. 735, doi. 10.1111/1365-2478.12876
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Using orthogonal Legendre polynomials to parameterize global geophysical optimizations: Applications to seismic‐petrophysical inversion and 1D elastic full‐waveform inversion.
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- Geophysical Prospecting, 2019, v. 67, n. 2, p. 331, doi. 10.1111/1365-2478.12726
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Comparing the performances of four stochastic optimisation methods using analytic objective functions, 1D elastic full‐waveform inversion, and residual static computation.
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- Geophysical Prospecting, 2017, v. 65, p. 322, doi. 10.1111/1365-2478.12532
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Comparing the performances of four stochastic optimisation methods using analytic objective functions, 1D elastic full-waveform inversion, and residual static computation.
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- Geophysical Prospecting, 2017, v. 65, n. 1, p. 322, doi. 10.1111/1365-2478.12532
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1D elastic full-waveform inversion and uncertainty estimation by means of a hybrid genetic algorithm-Gibbs sampler approach.
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- Geophysical Prospecting, 2017, v. 65, n. 1, p. 64, doi. 10.1111/1365-2478.12397
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Estimation of reservoir properties from seismic data through a Markov Chain Monte Carlo-AVA inversion algorithm.
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- Exploration Geophysics, 2018, v. 49, n. 5, p. 688, doi. 10.1071/EG17077
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