Cargando…
Diffusion Model for DAS-VSP Data Denoising
Distributed acoustic sensing (DAS) has emerged as a transformational technology for seismic data acquisition. However, noise remains a major impediment, necessitating advanced denoising techniques. This study pioneers the application of diffusion models, a type of generative model, for DAS vertical...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611154/ https://www.ncbi.nlm.nih.gov/pubmed/37896712 http://dx.doi.org/10.3390/s23208619 |
_version_ | 1785128424906424320 |
---|---|
author | Zhu, Donglin Fu, Lei Kazei, Vladimir Li, Weichang |
author_facet | Zhu, Donglin Fu, Lei Kazei, Vladimir Li, Weichang |
author_sort | Zhu, Donglin |
collection | PubMed |
description | Distributed acoustic sensing (DAS) has emerged as a transformational technology for seismic data acquisition. However, noise remains a major impediment, necessitating advanced denoising techniques. This study pioneers the application of diffusion models, a type of generative model, for DAS vertical seismic profile (VSP) data denoising. The diffusion network is trained on a new generated synthetic dataset that accommodates variations in the acquisition parameters. The trained model is applied to suppress noise in synthetic and field DAS-VSP data. The results demonstrate the model’s effectiveness in removing various noise types with minimal signal leakage, outperforming conventional methods. This research signifies diffusion models’ potential for DAS processing. |
format | Online Article Text |
id | pubmed-10611154 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106111542023-10-28 Diffusion Model for DAS-VSP Data Denoising Zhu, Donglin Fu, Lei Kazei, Vladimir Li, Weichang Sensors (Basel) Article Distributed acoustic sensing (DAS) has emerged as a transformational technology for seismic data acquisition. However, noise remains a major impediment, necessitating advanced denoising techniques. This study pioneers the application of diffusion models, a type of generative model, for DAS vertical seismic profile (VSP) data denoising. The diffusion network is trained on a new generated synthetic dataset that accommodates variations in the acquisition parameters. The trained model is applied to suppress noise in synthetic and field DAS-VSP data. The results demonstrate the model’s effectiveness in removing various noise types with minimal signal leakage, outperforming conventional methods. This research signifies diffusion models’ potential for DAS processing. MDPI 2023-10-21 /pmc/articles/PMC10611154/ /pubmed/37896712 http://dx.doi.org/10.3390/s23208619 Text en © 2023 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 | Article Zhu, Donglin Fu, Lei Kazei, Vladimir Li, Weichang Diffusion Model for DAS-VSP Data Denoising |
title | Diffusion Model for DAS-VSP Data Denoising |
title_full | Diffusion Model for DAS-VSP Data Denoising |
title_fullStr | Diffusion Model for DAS-VSP Data Denoising |
title_full_unstemmed | Diffusion Model for DAS-VSP Data Denoising |
title_short | Diffusion Model for DAS-VSP Data Denoising |
title_sort | diffusion model for das-vsp data denoising |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611154/ https://www.ncbi.nlm.nih.gov/pubmed/37896712 http://dx.doi.org/10.3390/s23208619 |
work_keys_str_mv | AT zhudonglin diffusionmodelfordasvspdatadenoising AT fulei diffusionmodelfordasvspdatadenoising AT kazeivladimir diffusionmodelfordasvspdatadenoising AT liweichang diffusionmodelfordasvspdatadenoising |