Geomagnetic Anomaly Mapping by Multi-fractal Measure Kriging Interpolation Method
Pages 170 - 173
Abstract
Geomagnetic reference map is one of the main factors that affect the accuracy of geomagnetic matching navigation positioning. In the study of interpolation of geomagnetic to construct reference map, the usual Kriging interpolation method has smooth effect, which leads to the loss of detailed features of geomagnetic anomaly. In this paper, a Multi-fractal Measure Kriging (MFMK) spatial interpolation algorithm is proposed. MFMK method is based on the multi-fractal measure of geomagnetic anomaly. The trend is interpolated by Kriging method and the details are interpolated by multi-fractal measure. Based on MFMK method, Interpolation part of the global geomagnetic anomaly grid data (EMAG2-V3) is performed. The results show that MFMK algorithm can not only characterize the spatial correlation and variability of geomagnetic anomaly, but also effectively measure its local singularity, and enrich the high-frequency details of geomagnetic anomaly. Finally, cross validation is used to evaluate the effectiveness of MFMK interpolation method.
References
[1]
Huang X. Geomagnetic mapping and validity estimation{J}. Journal of Beijing University of Aeronautics & Astronautics, 2009, 35(7):891--894.
[2]
Yun-Zhi Y U, Tian M J, Jiang L. The application of spatial interpolation algorithm in marine geomagnetic mapping{J}. Ship Science & Technology, 2013.
[3]
Yun-Zhi Y U, Jiang L, Tian M J, et al. Analysis of interpolation suitability of algorithm based on simulation of the measured geomagnetic data{J}. Ship Science & Technology, 2014.
[4]
CHEN Ding-Xin, LIU Dai-Zhi, ZENG Xiao-Niu et al. 2016. Application and improvement of spatial temporal Kriging in geomagnetic field interpolation.Chinese Journal Of Geophysics, 59(5): 1743--1752.
[5]
Abzalov M. Introduction to Geostatistics{M}// Applied Mining Geology. Springer International Publishing, 2016.
[6]
D. G. Krige.A statistical approach to some basic mine evaluation problems on the Witwateround{J}.J Chim Min Soc South-Africa.1951, 52:119--139P.
[7]
Liu Z P, Yang J, Wei W U. THE CHOICE OF GRIDDING METHODS FOR GEOPHYSICAL DATA{J}. Geophysical & Geochemical Exploration, 2010, 34(1):93--97.
[8]
Zhang C, Yao C L, Xie Y M, et al. Reduction of distortion and improvement of efficiency for gridding of scattered gravity and magnetic data{J}. Applied Geophysics, 2012, 9(4):378--390.
[9]
A. Spector, F. S. Grant. Statistical models for interpreting aeromagnetic data{J}. Geophysics.1970, 35:293--302P
[10]
B. B. Mandelbrot.An introduction to multifractal distribution functions{J}.Fluctuations and Pattern Formation, Stanley H.E., Ostrowsky N.(Eds.), Kluwer Academic, Dordrecht, The Netherlands.1988
[11]
Meyer B, Chulliat A, Saltus R. Derivation and Error Analysis of the Earth Magnetic Anomaly Grid at 2 Arc-Minute Resolution Version 3 (EMAG2v3){J}. Geochemistry Geophysics Geosystems, 2017.
[12]
Aiping X U. Spatiotemporal Interpolation and Cross Validation Based on Product-Sum Model{J}. Geomatics & Information Science of Wuhan University, 2012, 37(7):766--769.
Index Terms
- Geomagnetic Anomaly Mapping by Multi-fractal Measure Kriging Interpolation Method
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Published In
October 2018
221 pages
ISBN:9781450364768
DOI:10.1145/3291801
Copyright © 2018 ACM.
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In-Cooperation
- Shandong Univ.: Shandong University
- University of Queensland: University of Queensland
- Dalian Maritime University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 27 October 2018
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ICBDR 2018
ICBDR 2018: 2018 The 2nd International Conference on Big Data Research
October 27 - 29, 2018
Weihai, China
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