IMAGE 2025

Speaker: Ilkin Karimli
Session ID: BH P4
Session Title: Machine Learning Applied to Borehole Data 2
Date: 27 August (3:40 p.m. - 5:20 p.m.)
Type: Poster presentation
Authors: Ilkin Karimli, Ruslan Malikov, Orkhan Mammadov, Izat Shahsenov, WAVERITY
Topic: This paper introduces an innovative methodology for improving seismic image quality by integrating deep learning algorithms with traditional signal processing and modeling techniques. The proposed algorithm operates independently of well data, making it adaptable to large seismic assets. By effectively reducing noise, distinguishing geological structures from disturbances, and applying nonlinear frequency spectrum enhancements, it reveals critical subsurface features. The effectiveness of the algorithm is demonstrated through its application to real seismic data from the South Caspian Basin, with a detailed analysis of the results. The findings highlight the potential of this approach to significantly enhance the accuracy and reliability of seismic exploration in geophysics.

Speaker: Kenan Aliyev
Session ID: SP 7
Session Title: Imaging Signal Enhancement
Date: 27 August (2:10 p.m. - 2:35 p.m.)
Type: Oral presentation
Authors: Orkhan Mammadov, Tarek Eliva, Hasan Asgarov, Ruslan Malikov, Izat Shahsenov WAVERITY, Najiya Kuramshina, Kanan Aliyev, GL
Topic: This paper introduces an innovative methodology for improving seismic image quality by integrating deep learning algorithms with traditional signal processing and modeling techniques. The proposed algorithm operates independently of well data, making it adaptable to large seismic assets. By effectively reducing noise, distinguishing geological structures from disturbances, and applying nonlinear frequency spectrum enhancements, it reveals critical subsurface features. The effectiveness of the algorithm is demonstrated through its application to real seismic data from the South Caspian Basin, with a detailed analysis of the results. The findings highlight the potential of this approach to significantly enhance the accuracy and reliability of seismic exploration in geophysics.

Speaker: Orkhan Mammadov
Session ID: SP P9
Session Title: Machine Learning Applications 1
Date: 27 August (1:20 p.m. - 3 p.m.)
Type: Poster presentation
Authors: Tarek Eliva, Orkhan Mammadov, Ruslan Malikov, Ilkin Karimli, Izat Shahsenov, WAVERITY, Najiya Kuramshina, Kanan Aliyev, GL
Topic: This paper introduces an innovative methodology for improving seismic image quality by integrating deep learning algorithms with traditional signal processing and modeling techniques. The proposed algorithm operates independently of well data, making it adaptable to large seismic assets. By effectively reducing noise, distinguishing geological structures from disturbances, and applying nonlinear frequency spectrum enhancements, it reveals critical subsurface features. The effectiveness of the algorithm is demonstrated through its application to real seismic data from the South Caspian Basin, with a detailed analysis of the results. The findings highlight the potential of this approach to significantly enhance the accuracy and reliability of seismic exploration in geophysics.

Speaker: Tatyana Yusubova
Session ID: TL 3
Session Title: Analysis using Machine Learning 2
Date: 28 August (11:35 a.m. - noon)
Type: Oral presentation
Authors: Tatyana Yusubova, Konstantin Matrosov, Ruslan Malikov, and Izat Shahsenov, WAVERITY
Topic: This paper introduces an innovative methodology for improving seismic image quality by integrating deep learning algorithms with traditional signal processing and modeling techniques. The proposed algorithm operates independently of well data, making it adaptable to large seismic assets. By effectively reducing noise, distinguishing geological structures from disturbances, and applying nonlinear frequency spectrum enhancements, it reveals critical subsurface features. The effectiveness of the algorithm is demonstrated through its application to real seismic data from the South Caspian Basin, with a detailed analysis of the results. The findings highlight the potential of this approach to significantly enhance the accuracy and reliability of seismic exploration in geophysics.

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