| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A convolutional recurrent neural network for strong convective rainfall nowcasting using weather radar data in Southeastern Brazil
|
Caseri, Angelica N.
|
|
|
3 |
C |
p. 8-13
|
artikel
|
| 2 |
Advanced geochemical exploration knowledge using machine learning: Prediction of unknown elemental concentrations and operational prioritization of Re-analysis campaigns
|
Zhang, Steven E.
|
|
|
3 |
C |
p. 86-100
|
artikel
|
| 3 |
A new correlation for calculating wellhead oil flow rate using artificial neural network
|
Azim, Reda Abdel
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|
|
3 |
C |
p. 1-7
|
artikel
|
| 4 |
Artificial intelligence-based anomaly detection of the Assen iron deposit in South Africa using remote sensing data from the Landsat-8 Operational Land Imager
|
Nwaila, Glen T.
|
|
|
3 |
C |
p. 71-85
|
artikel
|
| 5 |
A study on geological structure prediction based on random forest method
|
Chen, Zhen
|
|
|
3 |
C |
p. 226-236
|
artikel
|
| 6 |
A study on small magnitude seismic phase identification using 1D deep residual neural network
|
Li, Wei
|
|
|
3 |
C |
p. 115-122
|
artikel
|
| 7 |
Attenuation of seismic migration smile artifacts with deep learning
|
Yoo, Jewoo
|
|
|
3 |
C |
p. 123-131
|
artikel
|
| 8 |
Ensemble hybrid machine learning methods for gully erosion susceptibility mapping: K-fold cross validation approach
|
Roy, Jagabandhu
|
|
|
3 |
C |
p. 28-45
|
artikel
|
| 9 |
Estimation of the effectiveness of multi-criteria decision analysis and machine learning approaches for agricultural land capability in Gangarampur Subdivision, Eastern India
|
Saha, Sunil
|
|
|
3 |
C |
p. 179-191
|
artikel
|
| 10 |
Geostatistical semi-supervised learning for spatial prediction
|
Fouedjio, Francky
|
|
|
3 |
C |
p. 162-178
|
artikel
|
| 11 |
High resolution pre-stack seismic inversion using few-shot learning
|
Chen, Ting
|
|
|
3 |
C |
p. 203-208
|
artikel
|
| 12 |
Integrating the artificial intelligence and hybrid machine learning algorithms for improving the accuracy of spatial prediction of landslide hazards in Kurseong Himalayan Region
|
Saha, Anik
|
|
|
3 |
C |
p. 14-27
|
artikel
|
| 13 |
Irregularly sampled seismic data interpolation via wavelet-based convolutional block attention deep learning
|
Lou, Yihuai
|
|
|
3 |
C |
p. 192-202
|
artikel
|
| 14 |
Machine learning in petrophysics: Advantages and limitations
|
Xu, Chicheng
|
|
|
3 |
C |
p. 157-161
|
artikel
|
| 15 |
MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning
|
Alkhalifah, Tariq
|
|
|
3 |
C |
p. 101-114
|
artikel
|
| 16 |
Near-surface velocity estimation using shear-waves and deep-learning with a U-net trained on synthetic data
|
Gupta, Taneesh
|
|
|
3 |
C |
p. 209-224
|
artikel
|
| 17 |
Optimized feature selection assists lithofacies machine learning with sparse well log data combined with calculated attributes in a gradational fluvial sequence
|
Wood, David A.
|
|
|
3 |
C |
p. 132-147
|
artikel
|
| 18 |
PolarCAP – A deep learning approach for first motion polarity classification of earthquake waveforms
|
Chakraborty, Megha
|
|
|
3 |
C |
p. 46-52
|
artikel
|
| 19 |
ResGraphNet: GraphSAGE with embedded residual module for prediction of global monthly mean temperature
|
Chen, Ziwei
|
|
|
3 |
C |
p. 148-156
|
artikel
|
| 20 |
Synthetic shear sonic log generation utilizing hybrid machine learning techniques
|
Kim, Jongkook
|
|
|
3 |
C |
p. 53-70
|
artikel
|
| 21 |
Thank you reviewers!
|
|
|
|
3 |
C |
p. 225
|
artikel
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