Geophysical Data Processing - Image and Sound Processing Lab

Understanding our planet is one of the most exciting and challenging tasks that researchers all around the world are facing.

We at ISPL are working on imaging the Earth’s subsurface applying the most recent advances in the signal processing community.

Seismic Imaging

Being the easy reservoirs already exploited, the oil and gas companies are looking for energy reservoirs in increasingly complex environments: new exploration scenarios require higher resolution and fidelity, posing more and more difficult challenges to the scientific community.

In order to infer a quantitative description of the rock layers, geologists need images of the subsurface, obtained by very expensive exploration campaigns:

The area of interest is investigated by means of artificial seismic waves (i.e., small artificial earthquakes).
A controlled source emits signals that propagate through the media; these waves are reflected and transmitted depending on the subsurface topology and lithology, so that an array of sensors records the wave-field. Acquisition files can occupy up to 10 TeraBytes of disk space.
The acquired data need to be processed in order to attenuate noise and remove distortions. These operations are very complex and thus require a great computational effort. Moreover, they do influence a lot the final outcomes of the exploration process.
The recorded data are compared with the known source signals in order to infer the position of reflectors, along with their coefficients. This process of relocating seismic events from the sensors positions is called migration and it requires several weeks of computations on modern supercomputing infrastructures (capable of 10¹⁵ operations per second).

ISPL is actively involved in two main research topics:

Least-Squares Reverse Time Migration, an iterative technique that has proven excellent performance in recovering the amplitude of reflectivity coefficients (very important in order to characterize the rock materials), providing high resolution images with low artefacts.
Full Waveform Inversion exploits the amplitude and phase information of the seismic waveforms in order to reconstruct the velocity models of the subsurfaces (i.e., to infer the rock layers properties).

Deep Learning for Geophysics

Inverse Problems in the field of Seismic Imaging have at least three distinctive features: their size, both in terms of data and model space; their strongly ill posed nature, and the absence of ground truth.
In the light of these considerations, it is possible to notice how many geophysical applications could benefit from the joint use of signal processing and machine learning paradigms. Indeed, the availability of huge datasets enables the use of data-driven approaches and not only model-based analysis, which helps the scientists relaxing strong assumptions on the physical phenomena under evaluation.

We can use CNNs to solve minimization problems, turning their overfitting downside into an asset. This means, rather than using the amount of available data for generalizing the application scenarios, specialize the architecture to the analysis of a single image.
We are also focusing on how to embed physical constraints into network design. Indeed, geophysical and computer vision applications do not share the same exact problems and thus the architectures coming from image processing applications must be accurately handled. As an example, the loss functions can be modified in order to satisfy some priors knowledge (e.g., to enforce sparsity in the solution), and the convolution layers can be turned into 3D operators.

Related publications

2022

W. Xu, V. Lipari, P. Bestagini, W. Chen, and S. Tubaro, “Interpolation of Missing Shots Via Plug and Play Method with Csgs Trained Deep Denoiser,” in 83rd EAGE Annual Conference & Exhibition, 2022, p. 1–5.
[Bibtex]

@inproceedings{xu2022interpolation,
title={Interpolation of Missing Shots Via Plug and Play Method with Csgs Trained Deep Denoiser},
author={Xu, W and Lipari, V and Bestagini, P and Chen, W and Tubaro, S},
booktitle={83rd EAGE Annual Conference \& Exhibition},
volume={2022},
number={1},
pages={1--5},
year={2022},
organization={EAGE Publications BV},
groups={geophysics}
}

W. Xu, V. Lipari, P. Bestagini, M. Ravasi, W. Chen, and S. Tubaro, “Intelligent seismic deblending through deep preconditioner,” IEEE Geoscience and Remote Sensing Letters, vol. 19, p. 1–5, 2022.
[Bibtex]

@article{xu2022intelligent,
title={Intelligent seismic deblending through deep preconditioner},
author={Xu, Weiwei and Lipari, Vincenzo and Bestagini, Paolo and Ravasi, Matteo and Chen, Wenchao and Tubaro, Stefano},
journal={IEEE Geoscience and Remote Sensing Letters},
volume={19},
pages={1--5},
year={2022},
publisher={IEEE},
groups={geophysics}
}

W. Xu, V. Lipari, P. Bestagini, W. Chen, S. Tubaro, and others, “Equivariant imaging for self-supervised regularly undersampled seismic data interpolation,” in SEG/AAPG International Meeting for Applied Geoscience & Energy, 2022.
[Bibtex]

@inproceedings{xu2022equivariant,
title={Equivariant imaging for self-supervised regularly undersampled seismic data interpolation},
author={Xu, Weiwei and Lipari, Vincenzo and Bestagini, Paolo and Chen, Wenchao and Tubaro, Stefano and others},
booktitle={SEG/AAPG International Meeting for Applied Geoscience \& Energy},
year={2022},
organization={OnePetro},
groups={geophysics}
}

2021

E. Biondi, G. Barnier, R. G. Clapp, F. Picetti, and S. Farris, “An Object-Oriented Optimization Framework for Large-Scale Inverse Problems,” Computers & Geosciences, vol. 154, 2021.
[Bibtex]

@Article{biondi2021object,
author = {Biondi, Ettore and Barnier, Guillaume and Clapp, Robert G. and Picetti, Francesco and Farris, Stuart},
title = {An Object-Oriented Optimization Framework for Large-Scale Inverse Problems},
doi = {10.1016/j.cageo.2021.104790},
volume = {154},
groups = {geophysics},
journal = {Computers & Geosciences},
year = {2021},
}

2020

F. Kong, V. Lipari, F. Picetti, P. Bestagini, and S. Tubaro, “A Deep Prior Convolutional Autoencoder for Seismic Data Interpolation,” in European Association of Geoscientists and Engineers (EAGE) Conference and Exhibition, 2020.
[Bibtex]

@InProceedings{kong2020deepa,
author = {Kong, F. and Lipari, V. and Picetti, F. and Bestagini, P. and Tubaro, S.},
booktitle = {European Association of Geoscientists and Engineers (EAGE) Conference and Exhibition},
title = {A Deep Prior Convolutional Autoencoder for Seismic Data Interpolation},
doi = {10.3997/2214-4609.202011461},
number = {1},
groups = {geophysics},
year = {2020},
}

F. Kong, V. Lipari, F. Picetti, P. Bestagini, and S. Tubaro, “Deep prior-based seismic data interpolation via multi-res U-net,” in Society of Exploration Geophysicists (SEG) Annual Meeting, 2020, pp. 3159-3163.
[Bibtex]

@InProceedings{kong2020deepb,
author = {Kong, F. and Lipari, V. and Picetti, F. and Bestagini, P. and Tubaro, S.},
booktitle = {Society of Exploration Geophysicists (SEG) Annual Meeting},
title = {Deep prior-based seismic data interpolation via multi-res U-net},
doi = {10.1190/segam2020-3426173.1},
number = {1},
pages = {3159-3163},
groups = {geophysics},
year = {2020},
}

F. Kong, F. Picetti, V. Lipari, P. Bestagini, X. Tang, and S. Tubaro, “Deep Prior Based Unsupervised Reconstruction of Irregularly Sampled Seismic Data,” IEEE Geoscience and Remote Sensing Letters (GRSL), 2020.
[Bibtex]

@article{kong2020deep,
author = {Kong, Fantong and Picetti, Francesco and Lipari, Vincenzo and Bestagini, Paolo and Tang, Xiaoming and Tubaro, Stefano},
doi = {10.1109/LGRS.2020.3044455},
groups = {geophysics},
journal = {IEEE Geoscience and Remote Sensing Letters (GRSL)},
title = {Deep Prior Based Unsupervised Reconstruction of Irregularly Sampled Seismic Data},
year = {2020},
Bdsk-Url-1 = {https://doi.org/10.1109/LGRS.2020.3044455}}

F. Picetti, F. Lombardi, V. Lipari, P. Bestagini, S. Tubaro, and M. Lualdi, “A Tool for Processing Seismic Images: Study on CycleGAN,” in 3rd Asia Pacific Meeting on Near Surface Geoscience & Engineering (NSGE), 2020.
[Bibtex]

@InProceedings{picetti2020tool,
author = {Picetti, Francesco and Lombardi, Federico and Lipari, Vincenzo and Bestagini, Paolo and Tubaro, Stefano and Lualdi, Maurizio},
booktitle = {3rd Asia Pacific Meeting on Near Surface Geoscience & Engineering (NSGE)},
title = {A Tool for Processing Seismic Images: Study on CycleGAN},
doi = {10.3997/2214-4609.202071085},
groups = {geophysics},
year = {2020},
}

2019

F. Picetti, V. Lipari, P. Bestagini, and S. Tubaro, “Seismic Image Processing through the Generative Adversarial Network,” Interpretation, vol. 7, 2019.
[Bibtex]

@article{picetti2019seismic,
author = {Picetti, Francesco and Lipari, Vincenzo and Bestagini, Paolo and Tubaro, Stefano},
doi = {10.1190/INT-2018-0232.1},
groups = {geophysics},
journal = {Interpretation},
title = {Seismic Image Processing through the Generative Adversarial Network},
volume = {7},
year = {2019},
Bdsk-Url-1 = {https://doi.org/10.1190/INT-2018-0232.1}}

C. Fortini, J. Panizzardi, N. Bienati, and V. Lipari, “Reflection FWI with Exponential Signal Encoding,” in European Association of Geoscientists and Engineers (EAGE) Conference and Exhibition, 2019.
[Bibtex]

@inproceedings{fortini2019reflection,
author = {Fortini, Carlo and Panizzardi, Jacopo and Bienati, Nicola and Lipari, Vincenzo},
booktitle = {European Association of Geoscientists and Engineers (EAGE) Conference and Exhibition},
doi = {10.3997/2214-4609.201901269},
groups = {geophysics},
title = {Reflection FWI with Exponential Signal Encoding},
year = {2019},
Bdsk-Url-1 = {https://doi.org/10.3997/2214-4609.201901269}}

F. Devoti, C. Parera, A. Lieto, D. Moro, V. Lipari, P. Bestagini, and S. Tubaro, “Wavefield compression for seismic imaging via convolutional neural networks,” in Society of Exploration Geophysicists International Exposition and Annual Meeting (SEG), 2019.
[Bibtex]

@inproceedings{devoti2019wavefield,
author = {Devoti, Francesco and Parera, Claudia and Lieto, Alessandro and Moro, Daniele and Lipari, Vincenzo and Bestagini, Paolo and Tubaro, Stefano},
booktitle = {Society of Exploration Geophysicists International Exposition and Annual Meeting (SEG)},
doi = {10.1190/segam2019-3216395.1},
groups = {geophysics},
title = {Wavefield compression for seismic imaging via convolutional neural networks},
year = {2019},
Bdsk-Url-1 = {https://doi.org/10.1190/segam2019-3216395.1}}

V. Lipari, F. Picetti, J. Panizzardi, N. Bienati, and S. Tubaro, “Approximate Least Squares RTM via matching filters and regularized inversion,” in Society of Exploration Geophysicists (SEG) Annual Meeting, 2019.
[Bibtex]

@inproceedings{lipari2019approximate,
author = {Lipari, Vincenzo and Picetti, Francesco and Panizzardi, Jacopo and Bienati, Nicola and Tubaro, Stefano},
booktitle = {Society of Exploration Geophysicists (SEG) Annual Meeting},
doi = {10.1190/segam2019-3215177.1},
groups = {geophysics},
title = {Approximate Least Squares RTM via matching filters and regularized inversion},
year = {2019},
Bdsk-Url-1 = {https://doi.org/10.1190/segam2019-3215177.1}}

2018

S. Mandelli, F. Borra, V. Lipari, P. Bestagini, A. Sarti, and S. Tubaro, “Seismic Data Interpolation Through Convolutional Autoencoder,” in Society of Exploration Geophysicists International Exposition and Annual Meeting (SEG), 2018.
[Bibtex]

@inproceedings{mandelli2018seismic,
author = {Mandelli, Sara and Borra, Federico and Lipari, Vincenzo and Bestagini, Paolo and Sarti, Augusto and Tubaro, Stefano},
booktitle = {Society of Exploration Geophysicists International Exposition and Annual Meeting (SEG)},
groups = {geophysics},
title = {Seismic Data Interpolation Through Convolutional Autoencoder},
year = {2018}}

F. Picetti, V. Lipari, P. Bestagini, and S. Tubaro, “A Generative Adversarial Network For Seismic Imaging Applications,” in Society of Exploration Geophysicists International Exposition and Annual Meeting (SEG), 2018.
[Bibtex]

@InProceedings{picetti2018generative,
author = {Picetti, Francesco and Lipari, Vincenzo and Bestagini, Paolo and Tubaro, Stefano},
booktitle = {Society of Exploration Geophysicists International Exposition and Annual Meeting (SEG)},
title = {A Generative Adversarial Network For Seismic Imaging Applications},
doi = {10.1190/segam2018-2995439.1},
groups = {geophysics},
year = {2018},
}

C. Fortini, V. Lipari, N. Bienati, J. Panizzardi, and S. Tubaro, “Robust reflection Full Waveform Inversion with exponential signal encoding,” in Society of Exploration Geophysicists (SEG) Annual Meeting, 2018.
[Bibtex]

@inproceedings{fortini2018robust,
author = {Fortini, Carlo and Lipari, Vincenzo and Bienati, Nicola and Panizzardi, Jacopo and Tubaro, Stefano},
booktitle = {Society of Exploration Geophysicists (SEG) Annual Meeting},
doi = {10.1190/segam2018-2995437.1},
groups = {geophysics},
title = {Robust reflection Full Waveform Inversion with exponential signal encoding},
year = {2018},
Bdsk-Url-1 = {https://doi.org/10.1190/segam2018-2995437.1}}

2017

P. Bestagini, V. Lipari, and S. Tubaro, “A machine learning approach to facies classification using well logs,” in Society of Exploration Geophysicists International Exposition and Annual Meeting (SEG), 2017.
[Bibtex]

@inproceedings{bestagini2017machine,
author = {Bestagini, Paolo and Lipari, Vincenzo and Tubaro, Stefano},
booktitle = {Society of Exploration Geophysicists International Exposition and Annual Meeting (SEG)},
doi = {10.1190/segam2017-17729805.1},
eprint = {http://library.seg.org/doi/pdf/10.1190/segam2017-17729805.1},
groups = {geophysics},
title = {A machine learning approach to facies classification using well logs},
year = {2017},
Bdsk-Url-1 = {https://doi.org/10.1190/segam2017-17729805.1}}

S. Mandelli, V. Lipari, C. Fortini, and S. Tubaro, “First Break Tomography with TV Regularization and Structural Constraints,” in European Association of Geoscientists and Engineers (EAGE) Conference and Exhibition, 2017.
[Bibtex]

@inproceedings{mandelli2017first,
author = {Mandelli, Sara and Lipari, Vincenzo and Fortini, Carlo and Tubaro, Stefano},
booktitle = {European Association of Geoscientists and Engineers (EAGE) Conference and Exhibition},
doi = {10.3997/2214-4609.201701360},
groups = {geophysics},
title = {First Break Tomography with TV Regularization and Structural Constraints},
year = {2017},
Bdsk-Url-1 = {https://doi.org/10.3997/2214-4609.201701360}}

S. Mandelli, V. Lipari, C. Fortini, and S. Tubaro, “An investigation of uncertainty in velocity model building problems,” in Society of Exploration Geophysicists (SEG) International Exposition and Annual Meeting, 2017.
[Bibtex]

@inproceedings{mandelli2017investigation,
author = {Mandelli, Sara and Lipari, Vincenzo and Fortini, Carlo and Tubaro, Stefano},
booktitle = {Society of Exploration Geophysicists (SEG) International Exposition and Annual Meeting},
doi = {10.1190/segam2017-17774951.1},
groups = {geophysics},
title = {An investigation of uncertainty in velocity model building problems},
year = {2017},
Bdsk-Url-1 = {https://doi.org/10.1190/segam2017-17774951.1}}

V. Lipari, D. Urbano, E. Spadavecchia, J. Panizzardi, and N. Bienati, “Regularized tomographic inversion with geological constraints,” Geophysical Prospecting, vol. 65, iss. 1, pp. 305-315, 2017.
[Bibtex]

@Article{lipari2017regularized,
author = {Lipari, V. and Urbano, D. and Spadavecchia, E. and Panizzardi, J. and Bienati, N.},
title = {Regularized tomographic inversion with geological constraints},
doi = {10.1111/1365-2478.12374},
number = {1},
pages = {305-315},
volume = {65},
bdsk-url-1 = {https://doi.org/10.1111/1365-2478.12374},
booktitle = {European Association of Geologists and Engineers (EAGE) Conference and Exhibition},
groups = {geophysics},
journal = {Geophysical Prospecting},
year = {2017},
}

2015

E. Spadavecchia, J. Panizzardi, V. Lipari, and D. Urbano, “Well-tie constrained tomography in TTI media,” in European Association of Geologists and Engineers (EAGE) Conference and Exhibition, 2015, pp. 1485-1489.
[Bibtex]

@inproceedings{spadavecchia2015well,
author = {Spadavecchia, E. and Panizzardi, J. and Lipari, V. and Urbano, D.},
booktitle = {European Association of Geologists and Engineers (EAGE) Conference and Exhibition},
doi = {10.3997/2214-4609.201412570},
groups = {geophysics},
pages = {1485-1489},
title = {Well-tie constrained tomography in TTI media},
year = {2015},
Bdsk-Url-1 = {https://doi.org/10.3997/2214-4609.201412570}}

2014

V. Lipari, C. Fortini, E. Spadavecchia, and N. Bienati, “Internal multiple attenuation by Kirchhoff extrapolation,” Geophysical Prospecting, vol. 62, iss. 6, pp. 1376-1388, 2014.
[Bibtex]

@article{lipari2014internal,
author = {Lipari, Vincenzo and Fortini, Carlo and Spadavecchia, E. and Bienati, Nicola},
doi = {10.1111/1365-2478.12137},
groups = {geophysics},
journal = {Geophysical Prospecting},
number = {6},
pages = {1376-1388},
title = {{Internal multiple attenuation by Kirchhoff extrapolation}},
volume = {62},
year = {2014},
Bdsk-Url-1 = {https://doi.org/10.1111/1365-2478.12137}}

2013

C. Fortini, D. Maggi, V. Lipari, and M. Ferla, “Particle swarm optimization for seismic velocity analysis,” in Society of Exploration Geophysicists (SEG) Annual Meeting, 2013, pp. 4864-4868.
[Bibtex]

@inproceedings{fortini2013particle,
author = {Fortini, Carlo and Maggi, D. and Lipari, Vincenzo and Ferla, M.},
booktitle = {Society of Exploration Geophysicists (SEG) Annual Meeting},
doi = {10.1190/segam2013-0850.1},
groups = {geophysics},
pages = {4864-4868},
title = {Particle swarm optimization for seismic velocity analysis},
year = {2013},
Bdsk-Url-1 = {https://doi.org/10.1190/segam2013-0850.1}}

E. Spadavecchia, V. Lipari, N. Bienati, and G. Drufuca, “Water-bottom multiple attenuation by Kirchhoff extrapolation,” Geophysical Prospecting, vol. 61, iss. 4, pp. 725-734, 2013.
[Bibtex]

@article{spadavecchia2013water,
author = {Spadavecchia, E. and Lipari, V. and Bienati, N. and Drufuca, G.},
doi = {10.1111/j.1365-2478.2012.01131.x},
groups = {geophysics},
journal = {Geophysical Prospecting},
number = {4},
pages = {725-734},
title = {Water-bottom multiple attenuation by Kirchhoff extrapolation},
volume = {61},
year = {2013},
Bdsk-Url-1 = {https://doi.org/10.1111/j.1365-2478.2012.01131.x}}

2012

C. Fortini, V. Lipari, and N. Bienati, “Multiples prediction from migrated section,” in Society of Exploration Geophysicists (SEG) International Exposition and Annual Meeting, 2012.
[Bibtex]

@inproceedings{fortini2012multiples,
author = {Fortini, Carlo and Lipari, Vincenzo and Bienati, Nicola},
booktitle = {Society of Exploration Geophysicists (SEG) International Exposition and Annual Meeting},
doi = {10.1190/segam2012-0979.1},
groups = {geophysics},
title = {Multiples prediction from migrated section},
year = {2012},
Bdsk-Url-1 = {https://doi.org/10.1190/segam2012-0979.1}}

2011

C. Fortini, V. Lipari, E. Spadavecchia, and N. Bienati, “Internal Multiple Attenuation by Kirchhoff Extrapolation,” in European Association of Geoscientists and Engineers (EAGE) Conference and Exhibition, 2011, pp. 2051-2055.
[Bibtex]

@InProceedings{fortini2011internal,
author = {Fortini, Carlo and Lipari, Vincenzo and Spadavecchia, E. and Bienati, Nicola},
booktitle = {European Association of Geoscientists and Engineers (EAGE) Conference and Exhibition},
title = {Internal Multiple Attenuation by Kirchhoff Extrapolation},
doi = {10.3997/2214-4609.20149269},
pages = {2051-2055},
bdsk-url-1 = {https://doi.org/10.3997/2214-4609.20149269},
groups = {geophysics},
year = {2011},
}

2009

N. Bienati, C. Andreoletti, F. Perrone, V. Lipari, and M. Giboli, “Limited aperture migration in the angle domain,” in European Association of Geologists and Engineers (EAGE) Conference and Exhibition, 2009, pp. 2443-2447.
[Bibtex]

@inproceedings{bienati2009limited,
author = {Bienati, N. and Andreoletti, C. and Perrone, F. and Lipari, V. and Giboli, M.},
booktitle = {European Association of Geologists and Engineers (EAGE) Conference and Exhibition},
doi = {10.3997/2214-4609.201400363},
groups = {geophysics},
pages = {2443-2447},
title = {Limited aperture migration in the angle domain},
volume = {4},
year = {2009},
Bdsk-Url-1 = {https://doi.org/10.3997/2214-4609.201400363}}

2007

M. Giboli, V. Lipari, G. Drufuca, C. Andreoletti, and N. Bienati, “Multi-Arrival Kirchhoff Migration‚ Wavefronts Separation and Equalization,” in European Association of Geoscientists and Engineers (EAGE) Conference and Exhibition, 2007.
[Bibtex]

@inproceedings{giboli2007multi,
author = {Giboli, Matteo and Lipari, Vincenzo and Drufuca, Giuseppe and Andreoletti, C. and Bienati, Nicola},
booktitle = {European Association of Geoscientists and Engineers (EAGE) Conference and Exhibition},
doi = {10.3997/2214-4609.201401927},
groups = {geophysics},
title = {Multi-Arrival Kirchhoff Migration‚ Wavefronts Separation and Equalization},
year = {2007},
Bdsk-Url-1 = {https://doi.org/10.3997/2214-4609.201401927}}

2004

V. Lipari, C. Andreoletti, G. Bernasconi, N. Bienati, L. Cazzola, and G. Drufuca, “Some practical issues related to migration in the angle domain,” in Society of Exploration Geophysicists (SEG) Annual Meeting, 2004.
[Bibtex]

@inproceedings{lipari2004some,
author = {Lipari, V. and Andreoletti, C. and Bernasconi, G. and Bienati, N. and Cazzola, L. and Drufuca, G.},
booktitle = {Society of Exploration Geophysicists (SEG) Annual Meeting},
doi = {10.1190/1.1851081},
groups = {geophysics},
title = {Some practical issues related to migration in the angle domain},
year = {2004},
Bdsk-Url-1 = {https://doi.org/10.1190/1.1851081}}

PoliMine – Humanitarian Demining System

Buried landmines and unexploded remnants of war are a constant threat for the population of many countries that have been hit by wars in the past years. The huge amount of casualties has been a strong motivation for the research community toward the development of safe and robust techniques designed for landmine clearance.

Nonetheless, being able to detect and localize buried landmines with high precision in an automatic fashion is still considered a challenging task due to the many different boundary conditions that characterize this problem (e.g., several kinds of objects to detect, different soils and meteorological conditions, etc.).

We propose a novel technique for buried object detection tailored to unexploded landmine discovery. The proposed solution exploits a specific kind of convolutional neural network (CNN) known as autoencoder to analyze volumetric data acquired with ground penetrating radar (GPR) using different polarizations. This method works in an anomaly detection framework, indeed we only train the autoencoder on GPR data acquired on landmine-free areas. The system then recognizes landmines as objects that are dissimilar to the soil used during the training step. Experiments conducted on real data show that the proposed technique requires little training and no ad-hoc data pre-processing to achieve accuracy higher than 93% on challenging datasets.

Anomaly detection scheme that leverages the autoencoder latent space.

Related publications

2020

P. Bestagini, F. Lombardi, M. Lualdi, F. Picetti, and S. Tubaro, “Landmine Detection Using Autoencoders on Multipolarization GPR Volumetric Data,” IEEE Transactions on Geoscience and Remote Sensing (TGRS), pp. 1-14, 2020.
[Bibtex]

@Article{bestagini2020landmine,
author = {P. {Bestagini} and F. {Lombardi} and M. {Lualdi} and F. {Picetti} and S. {Tubaro}},
title = {Landmine Detection Using Autoencoders on Multipolarization GPR Volumetric Data},
doi = {10.1109/TGRS.2020.2984951},
pages = {1-14},
groups = {polimine},
journal = {IEEE Transactions on Geoscience and Remote Sensing (TGRS)},
year = {2020},
}

F. Lombardi, M. Lualdi, F. Picetti, P. Bestagini, G. Janszen, and L. A. Di Landro, “Ballistic Ground Penetrating Radar Equipment for Blast-Exposed Security Applications,” Remote Sensing, vol. 12, iss. 4, p. 717, 2020.
[Bibtex]

@article{lombardi2020ballistic,
author = {Lombardi, Federico and Lualdi, Maurizio and Picetti, Francesco and Bestagini, Paolo and Janszen, Gerardus and Di Landro, Luca Angelo},
doi = {http://dx.doi.org/10.3390/rs12040717},
groups = {polimine},
journal = {Remote Sensing},
number = {4},
pages = {717},
title = {Ballistic Ground Penetrating Radar Equipment for Blast-Exposed Security Applications},
volume = {12},
year = {2020},
Bdsk-Url-1 = {http://dx.doi.org/10.3390/rs12040717}}

2019

F. Lombardi, M. Lualdi, F. Picetti, and P. Bestagini, “Identification and Recognition of Landmine Internal Structure Scattering Contribution from GPR Data,” in European Meeting of Environmental and Engineering Geophysics, 2019.
[Bibtex]

@InProceedings{lombardi2019identification,
author = {Lombardi, Federico and Lualdi, Maurizio and Picetti, Francesco and Bestagini, Paolo},
booktitle = {European Meeting of Environmental and Engineering Geophysics},
title = {Identification and Recognition of Landmine Internal Structure Scattering Contribution from {GPR} Data},
doi = {10.3997/2214-4609.201902398},
groups = {polimine},
year = {2019},
}

2018

F. Picetti, G. Testa, F. Lombardi, P. Bestagini, M. Lualdi, and S. Tubaro, “Convolutional Autoencoder for Landmine Detection on GPR Scans,” in IEEE International Conference on Telecommunications and Signal Processing (TSP), 2018.
[Bibtex]

@InProceedings{picetti2018convolutional,
author = {Picetti, Francesco and Testa, Giuseppe and Lombardi, Federico and Bestagini, Paolo and Lualdi, Maurizio and Tubaro, Stefano},
booktitle = {IEEE International Conference on Telecommunications and Signal Processing (TSP)},
title = {Convolutional Autoencoder for Landmine Detection on {GPR} Scans},
doi = {10.1109/TSP.2018.8441206},
groups = {polimine},
year = {2018},
}

2017

S. Lameri, F. Lombardi, P. Bestagini, M. Lualdi, and S. Tubaro, “Landmine Detection from GPR Data Using Convolutional Neural Networks,” in European Signal Processing Conference (EUSIPCO), 2017.
[Bibtex]

@inproceedings{lameri2017landmine,
author = {Lameri, Silvia and Lombardi, Federico and Bestagini, Paolo and Lualdi, Maurizio and Tubaro, Stefano},
booktitle = {European Signal Processing Conference (EUSIPCO)},
groups = {polimine},
title = {Landmine Detection from GPR Data Using Convolutional Neural Networks},
year = {2017}}

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