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Dergiler / International Journal of Multidisciplinary Studies and Innovative Technologies / 2019 / Cilt: 3 - Sayı: 2

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

Betül AĞAOĞLU
Fatima Zehra UNAL
Mehmet Serdar GUZEL
Erkan BOSTANCI
İman ASKERBEYLİ
Sayfa
121–126
Yayın Tarihi
2019-12-23
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Artificial Intelligence, which works on the ability to learn in machines, has a widespread field of research.
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Temel Bulgular & Veri:One of the most researched topics of artificial intelligence is artificial neural networks.
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Sonuç & Katkı: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.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Artificial intelligence · Artificial neural networks · CNN

Abstract

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.

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Bu Makaleye Atıf Yap

AĞAOĞLU, B., UNAL, F. Z., GUZEL, M. S., BOSTANCI, E., ASKERBEYLİ, İ. (2019). A New View on The Processing of Seismic Data With Artificial Neural Networks. International Journal of Multidisciplinary Studies and Innovative Technologies, Cilt: 3 - Sayı: 2, 121–126. https://ajindex.com/articles/767858

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Kaynakça (18)

  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-
  7. 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-
  8. Tunçel, Sismik kırılma yöntemi ve mikrotremör ölçümlerinden elde edilen dinamik zemin paramerelerinin karşılaştırılması. 2008, DEÜ Fen Bilimleri Enstitüsü
  9. Zhang, Wang, and Chen, 2018, Deep learning for seismic lithology prediction, 215, -1368-1387-
  10. Yuan, et al., 2018, Seismic waveform classification and first-break picking using convolution neural networks, 15, -272-276-
  11. Abdel-Hamid, et al., 2014, Convolutional neural networks for speech recognition, 22, -1533-1545-
  12. 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-
  13. 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-
  14. 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-
  15. Na’imi, et al., 2014, Estimation of reservoir porosity and water saturation based on seismic attributes using support vector regression approach, 107, -93-101-
  16. Chen, 2017, Automatic microseismic event picking via unsupervised machine learning, 212, -88-102-
  17. 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-
  18. Alpaslan