[1] Bhatt A, Helle HB. Committee neural networks for porosity and permeability prediction from well logs. Geophys Prospect. 2002; 50:645–60.
[2] Tiab D, Donaldson EC. Petrophysics—theory and practice of measuring reservoir rock and fluid transport properties. 2nd ed. Melbourne: Elsevier; 2004.
[3] Anderson JK. Limitations of seismic inversion for porosity and pore fluid: lessons from chalk reservoir characterization exploration. Denver: Society of Exploration Geophysicists, SEG Annual Meeting; 1996. p. 309–12.
[4] Schultz PS, Ronen S, Hattori M, Corbett C. Seismic-guided estimation of log properties (Part 1: a data-driven interpretation methodology). Lead Edge. 1994; 13:305–10.
[5] Adeli, H. and Panakkat, A. (2009). A probabilistic neural network for earthquake magnitude prediction. Neural networks. 22(7): p. 1018-1024.
[6] Leite, E.P. and de Souza Filho, C. R. (2009). Probabilistic neural networks applied to mineral potential mapping for platinum group elements in the Serra Leste region, Carajás Mineral Province, Brazil. Computers & Geosciences. 35(3): p. 675-687.
[7] Patel, A.K. and Chatterjee, S. (2016). Computer vision-based limestone rock-type classification using probabilistic neural network. Geoscience Frontiers. 7(1): p. 53-60.
[8] Er, O., et al. (2012) An approach based on probabilistic neural network for diagnosis of Mesothelioma’s disease. Computers & Electrical Engineering. 38(1): p. 75-81.
[9] Huang, C.-J. and Liao, W.-C. (2003). A comparative study of feature selection methods for probabilistic neural networks in cancer classification. in Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence. IEEE.
[10] Mantzaris, D., Anastassopoulos, G. and Adamopoulos, A. (2011). Genetic algorithm pruning of probabilistic neural networks in medical disease estimation. Neural Networks, 24(8): p. 831-835.
[11] Shan, Y., et al. (2002). Application of probabilistic neural network in the clinical diagnosis of cancers based on clinical chemistry data. Analytica Chimica Acta. 471(1): p. 77-86.
[12] Gorunescu, F., et al. (2005). An evolutionary computational approach to probabilistic neural network with application to hepatic cancer diagnosis. in 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05). IEEE.
[13] Chtioui, Y., Bertrand, D. and Barba, D. (1996). Reduction of the size of the learning data in a probabilistic neural network by hierarchical clustering. Application to the discrimination of seeds by artificial vision. Chemometrics and Intelligent Laboratory Systems. 35(2): p. 175-186.
[14] Franti, P., et al. (2000). Fast and memory efficient implementation of the exact PNN. IEEE Transactions on Image Processing. 9(5): p. 773-777.
[15] Mohebian, R., Riahi, MA., Afjeh, M. (2018). Detection of the gas‑bearing zone in a carbonate reservoir using multi‑class relevance vector machines (RVM): comparison of its performance with SVM and PNN. Carbonates and Evaporites. 33, p. 347–357.
[16] Mohebian, R., Riahi, MA., Kadkhodaie, A. (2019). Characterization of hydraulic flow units from seismic attributes and well data based on a new fuzzy procedure using ANFIS and FCM algorithms, example from an Iranian carbonate reservoir. Carbonates Evaporites. 34, p. 349–358.
[17] Tofighi, F., Armani, P., Chehrazi, A., Alimoradi, A. (2021). Comparison of the Function of Conventional Neural Networks for Estimating Porosity in One of the Southeastern Iranian Oil Fields. Petroleum Research. 31, 118, 16-18.
[18] Lim, J.-S., Park, H.-J. and Kim, J. (2006). A new neural network approach to reservoir permeability estimation from well logs. in SPE Asia Pacific Oil & Gas Conference and Exhibition. Society of Petroleum Engineers.
[19] Singh, V., et al. (2007). Neural networks and their applications in lithostratigraphic interpretation of seismic data for reservoir characterization. The Leading Edge. 26(10): p. 1244-1260.
[20] Herrera, V.M., Russell, B. and Flores, A. (2006). Neural networks in reservoir characterization. The Leading Edge. 25(4): p. 402-411.
[21] Hampson, D.P., Schuelke, J.S. and Quirein, J.A. (2001). Use of multiattribute transforms to predict log properties from seismic data. Geophysics. 66(1): p. 220-236.
[22] Chettri, S.R. and Cromp, R.F. (1993). Probabilistic neural network architecture for high-speed classification of remotely sensed imagery. Telematics and Informatics. 10(3): p. 187-198.
[23] Mojeddifar, S., Chegeni, M.H. and Ahangarani, M.L. (2018). Gas-bearing reservoir characterization using an adjusted Parzen probabilistic network. Journal of Petroleum Science and Engineering. 169: p. 445-453.
[24] Kusy, M. and Kluska, J. (2013). Probabilistic neural network structure reduction for medical data classification. in International Conference on Artificial Intelligence and Soft Computing. Springer.
[25] Specht, D.F. and Romsdahl. H. (1994). Experience with adaptive probabilistic neural networks and adaptive general regression neural networks. in Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94). IEEE.
[26] Farrokhrooz, M. and Karimi, M. (2007). A performance comparison between Conventional PNN and Multi-spread PNN in ship noise classification. in OCEANS 2006-Asia Pacific. IEEE.
[27] Specht, D.F. (1990). Probabilistic neural networks. Neural networks. 3(1): p. 109-118.
[28] El Emary, I.M. and Ramakrishnan, S. (2008). On the application of various probabilistic neural networks in solving different pattern classification problems. World Applied Sciences Journal. 4(6): p. 772-780.
[29] Mao, K.Z., Tan, K.-C. and Ser, W. (2000). Probabilistic neural-network structure determination for pattern classification. IEEE Transactions on neural networks. 11(4): p. 1009-1016.
[30] Parzen, E. (1962). "On estimation of a probability density function and mode." The annals of mathematical statistics 33(3): 1065-1076.
[31] Cacoullos, T. (1966). "Estimation of a multivariate density." Annals of the Institute of Statistical Mathematics 18(1): 179-189.
[32] Kusy, M. and Zajdel, R. (2014). Probabilistic neural network training procedure based on Q (0)-learning algorithm in medical data classification. Applied Intelligence. 41(3): p. 837-854.
[33] Georgiou, V.L., Alevizos, P.D. and Vrahatis, M.N. (2008). Novel approaches to probabilistic neural networks through bagging and evolutionary estimating of prior probabilities. Neural Processing Letters. 27(2): p. 153-162.
[34] Sørensen JC, Gregersen U, Breiner M, Michelsen O. High-frequency sequence stratigraphy of Upper Cenozoic deposits in the central and southeastern North Sea areas. Mar Pet Geol. 1997; 14:99–123.
[35] Steeghs P, Overeem I, Tigrek S. Seismic volume attribute analysis of the Cenozoic succession in the L08 block (Southern North Sea). Glob Planet Change. 2000; 27:245–62.
[36] Tigrek S. 3D seismic interpretation and attribute analysis of the L08 block, Southern North Sea Basin. M.S. thesis, Delft University of Technology; 1988.
[37] Gregersen U. Sequence stratigraphic analysis of Upper Cenozoic deposits in the North Sea based on conventional and 3-D seismic data and well-logs. Ph.D. thesis, University of Aarhus; 1997.
[38] Aminzadeh, F. and De Groot, P. (2006). Neural networks and other soft computing techniques with applications in the oil industry: Eage Publications.
[39] OpendTectdGB Plugins User Documentation version 4.2. dGB Earth Sciences. Copyright 2002–2011. http://opendtect.org/rel/doc/ User/dgb/index.htm. Accessed 25 Aug 2012.
[40] Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization (PSO). in Proc. IEEE International Conference on Neural Networks, Perth, Australia.
[41] Congalton, R.G. and Green, K. (2008). Assessing the accuracy of remotely sensed data: principles and practices: CRC press.
)
