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Articles

Using machine learning to interpret 3D airborne electromagnetic inversions

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References

  • Brown, W.M, Gedeon,T.D., Groves, D. I, and Barnes, R.G., 2000, Artificial neural networks: A new method for mineral prospectivity mapping. Australian Journal of Earth Sciences, 47:4, 757-770.
  • Cracknell, M. and Reading, A.M, 2013, Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information. Computers and Geosciences, 63, 22-33.
  • Granek, J. and Haber, E., 2016, Advanced geoscience targeting via focused machine learning applied to the QUEST project dataset, British Columbia. Geoscience BC Summary of Activities 2015. Geoscience BC Report 2016-1, 77-126. Granek, J., Haber, E., and Holtham, E., 2016, Resource Management through Machine Learning. In 25th Geophysical Conference and Exhibition hosted by ASEG-PESA-AIG 2016, 1-5.
  • Granek, J., 2016, Application of Machine Learning Algorithms to Mineral Prospectivity Mapping, Ph.D. Thesis, The University of British Columbia.
  • Haber, E., and Modersitzki, J., 2006. Intensity gradient based registration and fusion of multi-modal images. International Conference on Medical Image Computing and Computer-Assisted Intervention, 726-733.
  • Haber, E., 2019, A deep learning geoscience package, Publication in Process.
  • Isikdogan, F., Bovik, A., and Passalacqua, P., 2017, Surface Water Mapping by Deep Learning. IEEE Journal of Selected Topic Applied Earth Observations and Remote Sensing, 10-11, 4909-4918.
  • Long,Y et al., 2017, Convolutional Neural Networks for Water Body Extraction from Landsat Imagery, International Journal of Computational Intelligence and Applications. 55, 2486-2498.
  • Milletari, F., Naveb, N., Ahmadi,S. 2016, V-Net: Fully Convolutional Neural Network for Volumetric Medical Image Segmentation, 2016 Fourth International Conference on 3D Vision IEEE, 565-571.
  • Ronneberger, O., Fischer, P., and Brox, T., 2015, U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 234-241.
  • Sørensen, K., and Auken, E., 2004, SkyTEM–a new highresolution helicopter transient electromagnetic system. Exploration Geophysics, 35, 194-202.
  • Voss, K., Swenson, S., and Rodell K., 2015, Quantifying renewable groundwater stress with GRACE. Water Resources Research, 51, 1-22

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