Residual separation of magnetic fields using a Cellular Neural Network approach

• Published in: Pure and applied geophysics Vol. 158; no. 9-10; pp. 1797 - 1818
• Main Authors: ALBORA, A. M, ÖZMEN, A, UCAN, O. N
• Format: Journal Article
• Language: English
• Published: Basel Springer 01.09.2001; Springer Nature B.V
• Subjects: Algorithms; Cellular; Cellular communication; Chromite; Earth sciences; Earth, ocean, space; Exact sciences and technology; Geophysics; Geophysics: general, magnetic, electric and thermic methods and properties; Internal geophysics; Magnetic fields; Mathematical analysis; Metal geology; Metallic and non-metallic deposits; Neural networks; Separation; Middle East; Turkey; spectra; magnetic field; oxides; maps; regional anomalies; neural networks; residual anomalies; spinel; depth; Asia; chromite; magnetic susceptibility; magnetic anomalies; distortion

--In this paper, a Cellular Neural Network (CNN) has been applied to a magnetic regional/residual anomaly separation problem. CNN is an analog parallel computing paradigm defined in space and characterized by the locality of connections between processing neurons. The behavior of the CNN is defined...