Estimation of 2D velocity model using acoustic signals and convolutional neural networks

Marco Paul E. Apolinario; Samuel G. Huaman Bustamante; Giorgio Morales; Daniel Diaz

doi:10.1109/INTERCON.2019.8853566

Estimation of 2D velocity model using acoustic signals and convolutional neural networks

Marco Paul E. Apolinario^*
, Samuel G. Huaman Bustamante
, Giorgio Morales
, Daniel Diaz

^*Corresponding author for this work

National University of Engineering

Research output: Chapter in Book/Published conference output › Conference publication

3 Citations (Scopus)

Abstract

The parameters estimation of a system using indirect measurements over the same system is a problem that occurs in many fields of engineering, known as the inverse problem. It also happens in the field of underwater acoustic, especially in mediums that are not transparent enough. In those cases, shape identification of objects using only acoustic signals is a challenge because it is carried out with information of echoes that are produced by objects with different densities from that of the medium. In general, these echoes are difficult to understand since their information is usually noisy and redundant. In this paper, we propose a model of convolutional neural network with an Encoder-Decoder configuration to estimate both localization and shape of objects, which produce reflected signals. This model allows us to obtain a 2D velocity model. The model was trained with data generated by the finite-difference method, and it achieved a value of 98.58% in the intersection over union metric 75.88% in precision and 64.69% in sensitivity.

Original language	English
Title of host publication	Proceedings of the 2019 IEEE 26th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	9781728136462
DOIs	https://doi.org/10.1109/INTERCON.2019.8853566
Publication status	Published - 3 Oct 2019
Event	26th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019 - Lima, Peru Duration: 12 Aug 2019 → 14 Aug 2019

Publication series

Name	Proceedings of the 2019 IEEE 26th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019

Conference

Conference	26th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019
Country/Territory	Peru
City	Lima
Period	12/08/19 → 14/08/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Acoustic Wave Equation
Deep Learning
Encoder-Decoder
Finite-Difference Method

Access to Document

10.1109/INTERCON.2019.8853566

https://arxiv.org/abs/1906.04310Licence: CC BY-NC-SA 4.0

Cite this

Apolinario, M. P. E., Huaman Bustamante, S. G., Morales, G., & Diaz, D. (2019). Estimation of 2D velocity model using acoustic signals and convolutional neural networks. In Proceedings of the 2019 IEEE 26th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019 Article 8853566 (Proceedings of the 2019 IEEE 26th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019). IEEE. https://doi.org/10.1109/INTERCON.2019.8853566

Apolinario, Marco Paul E. ; Huaman Bustamante, Samuel G. ; Morales, Giorgio et al. / Estimation of 2D velocity model using acoustic signals and convolutional neural networks. Proceedings of the 2019 IEEE 26th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019. IEEE, 2019. (Proceedings of the 2019 IEEE 26th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019).

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title = "Estimation of 2D velocity model using acoustic signals and convolutional neural networks",

abstract = "The parameters estimation of a system using indirect measurements over the same system is a problem that occurs in many fields of engineering, known as the inverse problem. It also happens in the field of underwater acoustic, especially in mediums that are not transparent enough. In those cases, shape identification of objects using only acoustic signals is a challenge because it is carried out with information of echoes that are produced by objects with different densities from that of the medium. In general, these echoes are difficult to understand since their information is usually noisy and redundant. In this paper, we propose a model of convolutional neural network with an Encoder-Decoder configuration to estimate both localization and shape of objects, which produce reflected signals. This model allows us to obtain a 2D velocity model. The model was trained with data generated by the finite-difference method, and it achieved a value of 98.58\% in the intersection over union metric 75.88\% in precision and 64.69\% in sensitivity.",

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author = "Apolinario, \{Marco Paul E.\} and \{Huaman Bustamante\}, \{Samuel G.\} and Giorgio Morales and Daniel Diaz",

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doi = "10.1109/INTERCON.2019.8853566",

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Apolinario, MPE, Huaman Bustamante, SG, Morales, G & Diaz, D 2019, Estimation of 2D velocity model using acoustic signals and convolutional neural networks. in Proceedings of the 2019 IEEE 26th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019., 8853566, Proceedings of the 2019 IEEE 26th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019, IEEE, 26th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019, Lima, Peru, 12/08/19. https://doi.org/10.1109/INTERCON.2019.8853566

Estimation of 2D velocity model using acoustic signals and convolutional neural networks. / Apolinario, Marco Paul E.; Huaman Bustamante, Samuel G.; Morales, Giorgio et al.
Proceedings of the 2019 IEEE 26th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019. IEEE, 2019. 8853566 (Proceedings of the 2019 IEEE 26th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019).

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - Estimation of 2D velocity model using acoustic signals and convolutional neural networks

AU - Apolinario, Marco Paul E.

AU - Huaman Bustamante, Samuel G.

AU - Morales, Giorgio

AU - Diaz, Daniel

PY - 2019/10/3

Y1 - 2019/10/3

N2 - The parameters estimation of a system using indirect measurements over the same system is a problem that occurs in many fields of engineering, known as the inverse problem. It also happens in the field of underwater acoustic, especially in mediums that are not transparent enough. In those cases, shape identification of objects using only acoustic signals is a challenge because it is carried out with information of echoes that are produced by objects with different densities from that of the medium. In general, these echoes are difficult to understand since their information is usually noisy and redundant. In this paper, we propose a model of convolutional neural network with an Encoder-Decoder configuration to estimate both localization and shape of objects, which produce reflected signals. This model allows us to obtain a 2D velocity model. The model was trained with data generated by the finite-difference method, and it achieved a value of 98.58% in the intersection over union metric 75.88% in precision and 64.69% in sensitivity.

AB - The parameters estimation of a system using indirect measurements over the same system is a problem that occurs in many fields of engineering, known as the inverse problem. It also happens in the field of underwater acoustic, especially in mediums that are not transparent enough. In those cases, shape identification of objects using only acoustic signals is a challenge because it is carried out with information of echoes that are produced by objects with different densities from that of the medium. In general, these echoes are difficult to understand since their information is usually noisy and redundant. In this paper, we propose a model of convolutional neural network with an Encoder-Decoder configuration to estimate both localization and shape of objects, which produce reflected signals. This model allows us to obtain a 2D velocity model. The model was trained with data generated by the finite-difference method, and it achieved a value of 98.58% in the intersection over union metric 75.88% in precision and 64.69% in sensitivity.

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KW - Deep Learning

KW - Encoder-Decoder

KW - Finite-Difference Method

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ER -

Apolinario MPE, Huaman Bustamante SG, Morales G, Diaz D. Estimation of 2D velocity model using acoustic signals and convolutional neural networks. In Proceedings of the 2019 IEEE 26th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019. IEEE. 2019. 8853566. (Proceedings of the 2019 IEEE 26th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019). doi: 10.1109/INTERCON.2019.8853566

Estimation of 2D velocity model using acoustic signals and convolutional neural networks

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