Convolution neural network application for first‐break picking for land seismic data-Reference-Cited by-同舟云学术

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Convolution neural network application for first‐break picking for land seismic data

Published:2022-06-28 Issue:7 Volume:70 Page:1093-1115
ISSN:0016-8025
Container-title:Geophysical Prospecting
language:en
Short-container-title:Geophysical Prospecting

Author:

Loginov Georgy N.¹²^ORCID,Duchkov Anton A.¹²,Litvichenko Dmitry A.³,Alyamkin Sergey A.⁴

Affiliation:

1. Institute of Petroleum Geology and Geophysics SB RAS Novosibirsk 630090 Russia

2. Novosibirsk State University Novosibirsk 630090, 2 Pirogova Street Russia

3. Gazpromneft‐NTC Tyumen 625048, 14, 50 Let Oktyabrya Russia

4. Expasoft LLC Novosibirsk 630090, Nikolaeva 12, 900 Russia

Publisher

Wiley

Subject

Geochemistry and Petrology,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2478.13192

Reference63 articles.

1. A review and appraisal of arrival-time picking methods for downhole microseismic data

2. Akram J. Ovcharenko O.andPeter D.(2017)A robust neural network‐based approach for microseismic event detection.SEG Technical Program Expanded Abstracts 2017. SEG p.6093.

3. Automatic earthquake recognition and timing from single traces

4. A comparative analysis of machine learning models for first‐break arrival picking;Ayub M.;Machine Learning,2021

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2. Testing the algorithm for determining the velocity model of the upper part of the medium based on the first times arrivals;Russian Journal of Geophysical Technologies;2024-08-31

3. Shear wave velocity prediction for fractured limestone reservoirs based on artificial neural network;Geophysical Prospecting;2024-06-19

4. Deep Learning in Geophysics: Current Status, Challenges, and Future Directions;Journal of the Korean Society of Mineral and Energy Resources Engineers;2024-02-28

5. First-break prediction in 3-D land seismic data using the dynamic time warping algorithm;Geophysical Journal International;2024-02-02

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