Standard

Accelerated deep learning-based estimation of wavefront dips and curvatures and their application to 3D prestack data enhancement. / Gadylshin, Kirill; Silvestrov, Ilya; Bakulin, Andrey.

2022. Работа представлена на 2nd International Meeting for Applied Geoscience and Energy, Соединенные Штаты Америки.

Результаты исследований: Материалы конференций › материалы › Рецензирование

Harvard

Gadylshin, K, Silvestrov, I & Bakulin, A 2022, 'Accelerated deep learning-based estimation of wavefront dips and curvatures and their application to 3D prestack data enhancement', Работа представлена на 2nd International Meeting for Applied Geoscience and Energy, Соединенные Штаты Америки, 28.08.2022 - 01.09.2022. https://doi.org/10.1190/image2022-3735882.1

APA

Gadylshin, K., Silvestrov, I., & Bakulin, A. (2022). Accelerated deep learning-based estimation of wavefront dips and curvatures and their application to 3D prestack data enhancement. Работа представлена на 2nd International Meeting for Applied Geoscience and Energy, Соединенные Штаты Америки. https://doi.org/10.1190/image2022-3735882.1

Vancouver

Gadylshin K, Silvestrov I, Bakulin A. Accelerated deep learning-based estimation of wavefront dips and curvatures and their application to 3D prestack data enhancement. 2022. Работа представлена на 2nd International Meeting for Applied Geoscience and Energy, Соединенные Штаты Америки. doi: 10.1190/image2022-3735882.1

Author

Gadylshin, Kirill ; Silvestrov, Ilya ; Bakulin, Andrey. / Accelerated deep learning-based estimation of wavefront dips and curvatures and their application to 3D prestack data enhancement. Работа представлена на 2nd International Meeting for Applied Geoscience and Energy, Соединенные Штаты Америки.5 стр.

BibTeX

@conference{bafe6739f4dd474f8ceb42eaec65230f,
title = "Accelerated deep learning-based estimation of wavefront dips and curvatures and their application to 3D prestack data enhancement",
abstract = "We present a novel workflow for the accelerated signal enhancement of massive 3D prestack seismic data utilizing a Local Wavefront Attributes Deep Neural Network. It is based on automatic local wavefront attributes estimation using a specially trained convolutional deep neural network. The general workflow is adaptive to a particular 3D prestack seismic volume. It requires performing a conventional semblance-based estimation of wavefront dips and curvatures for only about 1% of the whole amount of data. The verification of the proposed approach is done on challenging real datasets, both marine and land. Deep learning allows achieving a significant speed-up compared to the conventional method while preserving an acceptable quality of the results.",
author = "Kirill Gadylshin and Ilya Silvestrov and Andrey Bakulin",
note = "One of the authors (Kirill Gadylshin) was supported by RSF grant No. 22-21-00738.; 2nd International Meeting for Applied Geoscience and Energy, IMAGE 2022 ; Conference date: 28-08-2022 Through 01-09-2022",
year = "2022",
month = aug,
day = "15",
doi = "10.1190/image2022-3735882.1",
language = "English",

}

RIS

TY - CONF

T1 - Accelerated deep learning-based estimation of wavefront dips and curvatures and their application to 3D prestack data enhancement

AU - Gadylshin, Kirill

AU - Silvestrov, Ilya

AU - Bakulin, Andrey

N1 - Conference code: 2

PY - 2022/8/15

Y1 - 2022/8/15

N2 - We present a novel workflow for the accelerated signal enhancement of massive 3D prestack seismic data utilizing a Local Wavefront Attributes Deep Neural Network. It is based on automatic local wavefront attributes estimation using a specially trained convolutional deep neural network. The general workflow is adaptive to a particular 3D prestack seismic volume. It requires performing a conventional semblance-based estimation of wavefront dips and curvatures for only about 1% of the whole amount of data. The verification of the proposed approach is done on challenging real datasets, both marine and land. Deep learning allows achieving a significant speed-up compared to the conventional method while preserving an acceptable quality of the results.

AB - We present a novel workflow for the accelerated signal enhancement of massive 3D prestack seismic data utilizing a Local Wavefront Attributes Deep Neural Network. It is based on automatic local wavefront attributes estimation using a specially trained convolutional deep neural network. The general workflow is adaptive to a particular 3D prestack seismic volume. It requires performing a conventional semblance-based estimation of wavefront dips and curvatures for only about 1% of the whole amount of data. The verification of the proposed approach is done on challenging real datasets, both marine and land. Deep learning allows achieving a significant speed-up compared to the conventional method while preserving an acceptable quality of the results.

UR - https://www.scopus.com/inward/record.url?eid=2-s2.0-85146652988&partnerID=40&md5=684fc6384a75acd47e8bb5aa79d602f8

UR - https://www.mendeley.com/catalogue/b232aac1-7865-3937-ae6d-0f582fc055e8/

U2 - 10.1190/image2022-3735882.1

DO - 10.1190/image2022-3735882.1

M3 - Paper

T2 - 2nd International Meeting for Applied Geoscience and Energy

Y2 - 28 August 2022 through 1 September 2022

ER -

ID: 45608098