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Mitigating Wireless Channel Impairments in Seismic Data Transmission Using Deep Neural Networks
The traditional cable-based geophone network is an inefficient way of seismic data transmission owing to the related cost and weight. The future of oil and gas exploration technology demands large-scale seismic acquisition, versatility, flexibility, scalability, and automation. On the one hand, a ty...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473374/ https://www.ncbi.nlm.nih.gov/pubmed/34577311 http://dx.doi.org/10.3390/s21186105 |
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author | Iqbal, Naveed Lawal, Abdulmajid Zerguine, Azzedine |
author_facet | Iqbal, Naveed Lawal, Abdulmajid Zerguine, Azzedine |
author_sort | Iqbal, Naveed |
collection | PubMed |
description | The traditional cable-based geophone network is an inefficient way of seismic data transmission owing to the related cost and weight. The future of oil and gas exploration technology demands large-scale seismic acquisition, versatility, flexibility, scalability, and automation. On the one hand, a typical seismic survey can pile up a massive amount of raw seismic data per day. On the other hand, the need for wireless seismic data transmission remains immense. Moving from pre-wired to wireless geophones faces major challenges given the enormous amount of data that needs to be transmitted from geophones to the on-site data collection center. The most important factor that has been ignored in the previous studies for the realization of wireless seismic data transmission is wireless channel effects. While transmitting the seismic data wirelessly, impairments like interference, multi-path fading, and channel noise need to be considered. Therefore, in this work, a novel amalgamation of blind channel identification and deep neural networks is proposed. As a geophone already is responsible for transmitting a tremendous amount of data under tight timing constraints, the proposed setup eschews sending any additional training signals for the purpose of mitigating the channel effects. Note that the deep neural network is trained only on synthetic seismic data without the need to use real data in the training process. Experiments show that the proposed method gives promising results when applied to the real/field data set. |
format | Online Article Text |
id | pubmed-8473374 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84733742021-09-28 Mitigating Wireless Channel Impairments in Seismic Data Transmission Using Deep Neural Networks Iqbal, Naveed Lawal, Abdulmajid Zerguine, Azzedine Sensors (Basel) Communication The traditional cable-based geophone network is an inefficient way of seismic data transmission owing to the related cost and weight. The future of oil and gas exploration technology demands large-scale seismic acquisition, versatility, flexibility, scalability, and automation. On the one hand, a typical seismic survey can pile up a massive amount of raw seismic data per day. On the other hand, the need for wireless seismic data transmission remains immense. Moving from pre-wired to wireless geophones faces major challenges given the enormous amount of data that needs to be transmitted from geophones to the on-site data collection center. The most important factor that has been ignored in the previous studies for the realization of wireless seismic data transmission is wireless channel effects. While transmitting the seismic data wirelessly, impairments like interference, multi-path fading, and channel noise need to be considered. Therefore, in this work, a novel amalgamation of blind channel identification and deep neural networks is proposed. As a geophone already is responsible for transmitting a tremendous amount of data under tight timing constraints, the proposed setup eschews sending any additional training signals for the purpose of mitigating the channel effects. Note that the deep neural network is trained only on synthetic seismic data without the need to use real data in the training process. Experiments show that the proposed method gives promising results when applied to the real/field data set. MDPI 2021-09-12 /pmc/articles/PMC8473374/ /pubmed/34577311 http://dx.doi.org/10.3390/s21186105 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Communication Iqbal, Naveed Lawal, Abdulmajid Zerguine, Azzedine Mitigating Wireless Channel Impairments in Seismic Data Transmission Using Deep Neural Networks |
title | Mitigating Wireless Channel Impairments in Seismic Data Transmission Using Deep Neural Networks |
title_full | Mitigating Wireless Channel Impairments in Seismic Data Transmission Using Deep Neural Networks |
title_fullStr | Mitigating Wireless Channel Impairments in Seismic Data Transmission Using Deep Neural Networks |
title_full_unstemmed | Mitigating Wireless Channel Impairments in Seismic Data Transmission Using Deep Neural Networks |
title_short | Mitigating Wireless Channel Impairments in Seismic Data Transmission Using Deep Neural Networks |
title_sort | mitigating wireless channel impairments in seismic data transmission using deep neural networks |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473374/ https://www.ncbi.nlm.nih.gov/pubmed/34577311 http://dx.doi.org/10.3390/s21186105 |
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