Application of deep learning algorithms in geotechnical engineering: a short critical review
W Zhang, H Li, Y Li, H Liu, Y Chen, X Ding - Artificial Intelligence Review, 2021 - Springer
With the advent of big data era, deep learning (DL) has become an essential research
subject in the field of artificial intelligence (AI). DL algorithms are characterized with powerful …
subject in the field of artificial intelligence (AI). DL algorithms are characterized with powerful …
[HTML][HTML] Impact of dataset size and convolutional neural network architecture on transfer learning for carbonate rock classification
Modern geological practices, in both industry and academia, rely largely on a legacy of
observational data at a range of scales. However, widespread ambiguities in the …
observational data at a range of scales. However, widespread ambiguities in the …
Ore image classification based on small deep learning model: Evaluation and optimization of model depth, model structure and data size
The ore image classification technology based on deep learning is an effective way to
improve the image sensor-based ore sorting classification capability. However, in practice …
improve the image sensor-based ore sorting classification capability. However, in practice …
[HTML][HTML] Data-driven predictive modelling of mineral prospectivity using machine learning and deep learning methods: A case study from southern Jiangxi Province …
T Sun, H Li, K Wu, F Chen, Z Zhu, Z Hu - Minerals, 2020 - mdpi.com
Predictive modelling of mineral prospectivity, a critical, but challenging procedure for
delineation of undiscovered prospective targets in mineral exploration, has been spurred by …
delineation of undiscovered prospective targets in mineral exploration, has been spurred by …
[HTML][HTML] Application of machine learning for lithofacies prediction and cluster analysis approach to identify rock type
Nowadays, there are significant issues in the classification of lithofacies and the
identification of rock types in particular. Zamzama gas field demonstrates the complex nature …
identification of rock types in particular. Zamzama gas field demonstrates the complex nature …
Lithology identification using principal component analysis and particle swarm optimization fuzzy decision tree
Lithology identification using geophysical log information is vital for log interpretation and
reservoir evaluation. As a result of the highly similar features for log curves that characterize …
reservoir evaluation. As a result of the highly similar features for log curves that characterize …
A review of deep leaning in image classification for mineral exploration
Efficient sorting and optimal utilization have become the common core issues for today's
mining industry. Vision-based sorting technology provides a powerful answer to this …
mining industry. Vision-based sorting technology provides a powerful answer to this …
Lithology identification using well logs: A method by integrating artificial neural networks and sedimentary patterns
Effective identification of lithology using well logs is one of the most important steps for
reservoir characterization. A lot of methods have been developed to identify lithology …
reservoir characterization. A lot of methods have been developed to identify lithology …
An unsupervised deep-learning method for porosity estimation based on poststack seismic data
We propose to invert reservoir porosity from poststack seismic data using an innovative
approach based on deep-learning methods. We develop an unsupervised approach to …
approach based on deep-learning methods. We develop an unsupervised approach to …
Quantum-enhanced deep learning-based lithology interpretation from well logs
N Liu, T Huang, J Gao, Z Xu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Lithology interpretation is important for understanding subsurface properties. Yet, the
common manual well log interpretation is usually with low efficiency and bad consistency …
common manual well log interpretation is usually with low efficiency and bad consistency …