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Academic Articles & Research Papers
Showing 6 of 166 academic articles
• Choice Reviews Online
(1992)
Partial list of topics 1. Planetary geology 2. Geophysics 3. Planetary interiors 4. Planetary atmospheres 5. Pre-main sequence stars 6. Solar physics 7. Solar activity 8. Galaxies (types, structure, chemical evolution) 9. Cosmology and grav...
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DOI: https://doi.org/10.5860/choice.29-3632
Martin V. Zombeck • Cambridge University Press eBooks
(2006)
Fully updated and including data from space-based observations, this Third Edition is a comprehensive compilation of the facts and figures relevant to astronomy and astrophysics. As well as a vast number of tables, graphs, diagrams and form...
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DOI: https://doi.org/10.1017/cbo9780511536359
Feng Xiong, Jintao Wang, Guohua Zhang, Xueming Shi et al. • Advances in Civil Engineering
(2024)
In the construction of the civil engineering infrastructure, the noise and vibration are affected by the geological conditions, adopting specific construction techniques based on the geological conditions is of great significance in suppres...
DOAJ
Open Access (DOAJ)
DOI: 10.1155/2024/3599911
Mirko van der Baan, Christian Jutten B • Geophysics
(2000)
Abstract Neural networks are increasingly popular in geophysics. Because they are universal approximators, these tools can approximate any continuous function with an arbitrary precision. Hence, they may yield important contributions to fin...
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DOI: https://doi.org/10.1190/1.1444797
Siwei Yu, Jianwei Ma • Reviews of Geophysics
(2021)
Abstract Recently deep learning (DL), as a new data‐driven technique compared to conventional approaches, has attracted increasing attention in geophysical community, resulting in many opportunities and challenges. DL was proven to have the...
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cc-by
DOI: https://doi.org/10.1029/2021rg000742
Siwei Yu, Jianwei Ma, Wenlong Wang • Geophysics
(2019)
ABSTRACT Compared with traditional seismic noise attenuation algorithms that depend on signal models and their corresponding prior assumptions, removing noise with a deep neural network is trained based on a large training set in which the ...
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Open Access
DOI: https://doi.org/10.1190/geo2018-0668.1