A New View on The Processing of Seismic Data With Artificial Neural Networks

Yıl 2019, Cilt: 3 Sayı: 2, 121 - 126, 23.12.2019

Betül Ağaoğlu , Fatima Zehra Unal , Mehmet Serdar Guzel , Erkan Bostancı , İman Askerbeyli

Öz

Artificial Intelligence, which works on the ability to learn in
machines, has a widespread field of research. One of the most researched topics
of artificial intelligence is artificial neural networks. Artificial neural
networks are effective today with the solution of complex problems, calculation
and processing of information. Seismic method, which is one of the basic
applications of geophysical field, is widely used especially for the detection
of oil by using seismic waves. With the literature review, it is seen that the
types of artificial neural network architectures are used. It has been
determined that different methods are used in the processing of seismic data.
Using the convolutional neural network (CNN), one of the artificial neural
network architectures, it is aimed to achieve success in oil detection by
seismic waves.

Anahtar Kelimeler

Artificial intelligence, Artificial neural networks, CNN, seismic data

Kaynakça

• 1. Basheer and Hajmeer, 2000, Artificial neural networks: fundamentals, computing, design, and application, 43, -3-31-.
• 2. Yao, 1999, Evolving artificial neural networks, 87, -1423-1447-.
• 3. Haykin and Network, 2004, A comprehensive foundation, 2, -41-.
• 4. Rojas, Neural networks: a systematic introduction. 2013: Springer Science & Business Media.
• 5. Caner and Akarslan, 2009, Mermer Kesme İşleminde Spesifik Enerji Faktörünün ANFIS ve YSA Yöntemleri ile Tahmini, 15, -221-226-.
• 6. Harrington, 1993, Transfer functions in artificial neural networks,
• 7. Debes, Koenig, and Gross, 2005, Transfer Functions in Artificial Neural Networks A Simulation-Based Tutorial,
• 8. Keçeli, 2010, Sismik yöntem ile zemin taşıma kapasitesi ve oturmasının saptanması, 9, -23-41-.
• 9. Kanbur, Silahtar, and Özsoy, 2011, Sığ Sismik Yansıma, MASW ve ReMi Yöntemleri ile Sığ Yapıların İncelenmesi: Isparta Yerleşim Merkezi Kuzeyi Pliyo-Kuvaterner Çökel Yapı Örneği, 15, -224-232-.
• 10. Tunçel, Sismik kırılma yöntemi ve mikrotremör ölçümlerinden elde edilen dinamik zemin paramerelerinin karşılaştırılması. 2008, DEÜ Fen Bilimleri Enstitüsü.
• 11. Zhang, Wang, and Chen, 2018, Deep learning for seismic lithology prediction, 215, -1368-1387-.
• 12. Yuan, et al., 2018, Seismic waveform classification and first-break picking using convolution neural networks, 15, -272-276-.
• 13. Abdel-Hamid, et al., 2014, Convolutional neural networks for speech recognition, 22, -1533-1545-.
• 14. Huang, et al., 2018, Micro-seismic event detection and location in underground mines by using Convolutional Neural Networks (CNN) and deep learning, 81, -265-276-.
• 15. Iturrarán-Viveros and Parra, 2014, Artificial neural networks applied to estimate permeability, porosity and intrinsic attenuation using seismic attributes and well-log data, 107, -45-54-.
• 16. Raeesi, et al., 2012, Classification and identification of hydrocarbon reservoir lithofacies and their heterogeneity using seismic attributes, logs data and artificial neural networks, 82, -151-165-.
• 17. Na’imi, et al., 2014, Estimation of reservoir porosity and water saturation based on seismic attributes using support vector regression approach, 107, -93-101-.
• 18. Chen, 2017, Automatic microseismic event picking via unsupervised machine learning, 212, -88-102-.
• 19. Fattahi and Karimpouli, 2016, Prediction of porosity and water saturation using pre-stack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods, 20, -1075-1094-.
• 20. Alpaslan and Derya, 2012, PETROL ARAMA ÇALIŞMALARINDA KULLANILAN JEOFİZİK YÖNTEMLERE GENEL BİR BAKIŞ, 2, -157-170-.
• 21. Çelık, Atalay, and Bayer. Earthquake prediction using seismic bumps with artificial neural networks and support vector machines. in 2014 22nd Signal Processing and Communications Applications Conference (SIU). 2014. IEEE.
• 22. Kaur, Wadhwa, and Park. Detection and identification of seismic P-Waves using Artificial Neural Networks. in The 2013 International Joint Conference on Neural Networks (IJCNN). 2013. IEEE.
• 23. Diersen, et al., 2011, Classification of seismic windows using artificial neural networks, 4, -1572-1581-.
• 24. Dai and MacBeth. Arrival type identification in local earthquake data using an artificial neural network. in Proc. 1996.
• 25. Karim, et al., 2019, A new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processing, 39, -148-159-.
• 26. Zamani, 2012, Response prediction of earthquake motion using artificial neural networks, 1,
• 27. Benbrahim, et al. Discrimination of Seismic Signals Using Artificial Neural Networks. in WEC (2). 2005. Citeseer.
• 28. Maggi, et al., 2009, An automated time-window selection algorithm for seismic tomography, 178, -257-281-.
• 29. Murphy and Cercone. Neural network techniques applied to seismic event classification. in 1993 (25th) Southeastern Symposium on System Theory. 1993. IEEE.
• 30. Karim, et al., 2018, A new generalized deep learning framework combining sparse autoencoder and Taguchi method for novel data classification and processing, 2018,