Deep learning applications for wind farms site characterization and monitoring-Reference-Cited by-同舟云学术

Deep learning applications for wind farms site characterization and monitoring

Published:2021-09-01 Issue: Volume: Page:
ISSN:
Container-title:First International Meeting for Applied Geoscience & Energy Expanded Abstracts
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Author:

Abubakar Aria¹,Juncker Brædstrup Mette¹,Di Haibin¹,Troya Diaz Alberto²,Freeman Steve¹,Hviid Simon²,Ho Karkov Knud²,Kriplani Sachin¹,Manikani Sunil¹,Salun Gwenaëlle²,Zhao Tao¹

Affiliation:

1. Schlumberger

2. Ørsted Wind Power A/S

Publisher

Society of Exploration Geophysicists

Link

https://library.seg.org/doi/pdf/10.1190/segam2021-3583026.1

Reference7 articles.

1. Bertossa, A. D., J. R. Geear, A. B. Hart, and J. Pitel, 2019, An integrated assessment of the ground conditions for foundations design at St-Brieuc Offshore Windfarm: XVII European Conference on Soil Mechanics and Geotechnical Engineering, 704, doi: 10.32075/17ECSMGE-2019-0704.

2. Semi-supervised seismic and well log integration for reservoir property estimation

3. Seismic stratigraphy interpretation by deep convolutional neural networks: A semisupervised workflow

4. Stothetic CPTS from Intelligent Ground Models Based on the Integration of Geology, Geotectoics and Geophysics as a Tool for Conceptual Foundation Design and Soil Investigation Planning

5. Geological and geotechnical characterisation for offshore wind turbine foundations: A case study of the Sheringham Shoal wind farm

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