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. Paper presented at 2nd International Meeting for Applied Geoscience and Energy, United States.

Research output: Contribution to conference › Paper › peer-review

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', Paper presented at 2nd International Meeting for Applied Geoscience and Energy, United States, 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. Paper presented at 2nd International Meeting for Applied Geoscience and Energy, United States. 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. Paper presented at 2nd International Meeting for Applied Geoscience and Energy, United States. 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. Paper presented at 2nd International Meeting for Applied Geoscience and Energy, United States.5 p.

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