Abstract
The article presents the algorithm that allows one to carry out automatic detection of local anomalies of magnetometric data based on image analysis principles. The algorithm was tested on the available data of the magnetometric survey in areas of planned archaeological excavations.
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Natal’ya Genrikhovna Grafeeva. Born 1953. Graduated from the Saint Petersburg State University in 1976 (Mathematics and Mechanics, specialization Mathematics). Graduated from the post-graduate studies in 1979, and passed Ph.D. defense in 1980. Ph.D., Associate Professor of the Department of Information and Analytical Systems, St. Petersburg State University. Lecturer of the St. Petersburg State University; Professor since 1981. Scientific interests: databases, data mining.
Elena Georgievna Mikhailova. Born 1970. Ph.D. (Physics and Mathematics), Associate Professor of the Department of Information and Analytical Systems, Saint Petersburg State University, acting head of the department. Scientific Secretary of the Academic Council of the Department of Mathematics and Mechanics. Lecturer of the St. Petersburg State University since 1997.
Vasilisa Sergeevna Mikhailova. Born 1994. Graduated from the St. Petersburg State University in 2015. Defended a bachelor’s thesis in 2015. Currently, a junior engineer in OOO “OpenWay Service.” Scientific interests: image processing and data mining.
Andrei Viktorovich Chudin. Graduate of the Department of Quantum Magnetic phenomena, St. Petersburg State University. Research Engineer of the Department of Physics, SPSU from January 2, 2015, to December 31, 2015. Scientific interests: magnetic survey, remote sensing in archeology, nondestructive methods of archaeological artifacts spectroscopy, physical and chemical methods, natural science interdisciplinary methods, ancient ceramic production technology, magnetic resonance spectroscopy.
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Mikhailova, V.S., Grafeeva, N.G., Mikhailova, E.G. et al. Magnetometry data processing to detect archaeological sites. Pattern Recognit. Image Anal. 26, 789–799 (2016). https://doi.org/10.1134/S1054661816040106
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DOI: https://doi.org/10.1134/S1054661816040106