default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDComputers & Geosciences, Volume 168
Volume 168, November 2022
- Nisar Ahmed
, Wiktor Waldemar Weibull, Dario Grana:
Constrained non-linear AVO inversion based on the adjoint-state optimization. 105214 - Thomas C. Wilkes, Tom D. Pering, Andrew J. S. McGonigle:
Semantic segmentation of explosive volcanic plumes through deep learning. 105216 - Jiabin Liang, Yongyang Sun, Maxim Lebedev, Boris Gurevich, Michel Nzikou, Stephanie Vialle, Stanislav Glubokovskikh:
Multi-mineral segmentation of micro-tomographic images using a convolutional neural network. 105217 - Binqian Yin, Qinhong Hu, Yingying Zhu, Chen Zhao, Keren Zhou:
Paw-Net: Stacking ensemble deep learning for segmenting scanning electron microscopy images of fine-grained shale samples. 105218 - Fang Gao, Yihui Li, Peng Zhang, Yuwei Zhai, Yan Zhang, Yongshuai Yang, Yuan An:
A high-resolution panchromatic-multispectral satellite image fusion method assisted with building segmentation. 105219 - Mario Mhawej, Yaser Abunnasr:
Daily Ten-ST-GEE: An open access and fully automated 10-m LST downscaling system. 105220 - Bin Wang, Liang Wu, Zhong Xie, Qinjun Qiu, Yuan Zhou, Kai Ma, Liufeng Tao:
Understanding geological reports based on knowledge graphs using a deep learning approach. 105229 - Li Zhao, Chunsheng Feng, Chen-Song Zhang, Shi Shu:
Parallel multi-stage preconditioners with adaptive setup for the black oil model. 105230
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-10-28 20:17 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
