Lithology prediction in the subsurface by artificial neural networks on well and 3D seismic data in clastic sediments: a stochastic approach to a deterministic method

Author:

Kamenski AnaORCID,Cvetković MarkoORCID,Kolenković Močilac IvaORCID,Saftić Bruno

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,Modeling and Simulation

Reference67 articles.

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2. Alman, D.H., Ningfang, L.: Overtraining in back-propagation neural networks: a CRT color calibration example. Color Res. Appl. 27, 122–125 (2002). https://doi.org/10.1002/col.10027

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4. Bjørlykke, K.: Petroleum geoscience. Springer, Berlin (2010)

5. Bowman, M., Smyth, H.R., Good, T.R., Passey, S.R., Hirst, J.P.P., Jordan, C.J.: The Value of Outcrop Studies in Reducing Subsurface Uncertainty and Risk in Hydrocarbon Exploration and Production. Geological Society, London (2016)

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