Combining Machine Learning and Geophysical Inversion for Applied Geophysicsmachine learningdata miningmodelling and inversionlithology3D geological mappingMachine learning and geophysical inversion both represent ways that the applied geophysicist might gain knowledge from field observations and remote sensed data....
Regarding geoscience, the review has a bias towards geophysics but aims to strike a balance with geochemistry, geostatistics, and geology, however excludes remote sensing, as this would exceed the scope. In general, I aim to provide context for the recent enthusiasm surrounding deep learning with ...
Recent advances in machine learning can be attributed to the increase in computer power and the adoption of deep-learning methods21. In geophysics, machine learning has been long established22,23, however, a suite of recent applications has appeared in the literature. These include seismic ...
Finally, it introduces the latest application of machine learning in glitch data processing through the Gravity Spy project. The goal is to present recent advancements in machine learning for the recognition, modeling, and removal of glitches, providing a reference for researchers handling transient ...
Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learnin...
The currently proposed method highlights deep integration of field survey and machine learning algorithm, and emphasizes importance of field work in the whole modeling process. Useful geo-information can be deeply mined from existing data and further updates former geological understandings. Meanwhile, ...
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The amount of data may range from several billion samples in geophysics to only a few in medical applications. Further, a vectorial rep- resentation of spectra typically leads to huge-dimensional problems. This scenario gives the background for particular requirements of respective machine learning ...
Physics-informed machine learning for system reliability analysis and design with partially observed information Constructing a high-fidelity predictive model is crucial for analyzing complex systems, optimizing system design, and enhancing system reliability. Althoug... Y Xu,P Bansal,P Wang,... - 《...
- 《Geophysics》 被引量: 1发表: 2018年 Local mean imputation for handling missing value to provide more accurate facies classification Currently, the machine learning approach is the most popular solution for the classification of rock facies. However, a common issue with this method is ... FI...