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Comparison Analysis of Positioning Accuracy between GGA-ELM-Based Geomagnetic Field Model and Actual Aeromagnetic Survey Data
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Chaofei CHANG1, Bing ZHANG2, Libin CHEN2, Weibao ZOU1, *, Enming LIU1, Daheng HAN1, Qidong LI1, Xin PENG3
Journal of Geodesy and Geodynamics | 2026, 46(9) : 1162 - 1170
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Journal of Geodesy and Geodynamics | 2026, 46(9): 1162-1170
Comparison Analysis of Positioning Accuracy between GGA-ELM-Based Geomagnetic Field Model and Actual Aeromagnetic Survey Data
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Chaofei CHANG1, Bing ZHANG2, Libin CHEN2, Weibao ZOU1, *, Enming LIU1, Daheng HAN1, Qidong LI1, Xin PENG3
Affiliations
  • 1 College of Geological Engineering and Surveying, Chang'an University, Xi'an 710054, China
  • 2 China State Shipbuilding Corporation Limited Systems Engineering Research Institute, Beijing 100094, China
  • 3 61363 Troops of PLA, Wuhan 430071, China
Published: 2026-09-15 doi: 10.14075/j.jgg.2025.11.380
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To address the limited positioning accuracy of global geomagnetic field models in localized regions, this paper proposes a geomagnetic positioning method based on a genetic algorithm-optimized extreme learning machine (GGA-ELM). By introducing an elite backpropagation strategy, the initial weights and thresholds of the ELM network are optimized, thereby improving training efficiency and reducing the risk of getting stuck in local optima. Based on actual aeromagnetic data from the Yuyao and Weishui regions in China, and in combination with the EMM2017, WMM2020, and IGRF13 geomagnetic field models, this study analyzes the differences in positioning accuracy between model data and observed data. Experiments demonstrate that the EMM2017 model achieves the best overall positioning accuracy at both sites (Yuyao: 6.29 m in longitude and 5.92 m in latitude; Weishui: 6.81 m in longitude and 5.89 m in latitude), significantly outperforming the WMM2020 and IGRF13 models. In the measured aeromagnetic data, the error in the latitudinal direction was slightly greater than that in the longitudinal direction (with a difference in root mean square error of approximately 0.5 m), whereas the geomagnetic field model data exhibited a characteristic where the longitudinal error was greater than the latitudinal error. The GGA-ELM method performed consistently at both locations, with positioning accuracy differences of less than 5%, verifying the robustness of the GGA-ELM method. This study provides a theoretical basis for model selection and algorithm optimization in geomagnetic navigation.

geomagnetic positioning  /  improved genetic algorithm  /  extreme learning machine  /  geomagnetic field model  /  aeromagnetic data  /  positioning accuracy
常超飞, 张兵, 陈利斌, 邹维宝, 刘恩铭, 韩大恒, 李启栋, 彭鑫. 基于GGA-ELM的地磁场模型与实测航磁数据定位精度对比分析. 大地测量与地球动力学, 2026 , 46 (9) : 1162 -1170 . DOI: 10.14075/j.jgg.2025.11.380
Chaofei CHANG, Bing ZHANG, Libin CHEN, Weibao ZOU, Enming LIU, Daheng HAN, Qidong LI, Xin PENG. Comparison Analysis of Positioning Accuracy between GGA-ELM-Based Geomagnetic Field Model and Actual Aeromagnetic Survey Data[J]. Journal of Geodesy and Geodynamics, 2026 , 46 (9) : 1162 -1170 . DOI: 10.14075/j.jgg.2025.11.380
Year 2026 volume 46 Issue 9
28
11
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doi: 10.14075/j.jgg.2025.11.380
  • Receive Date:2025-11-08
  • Online Date:2026-10-30
  • Published:2026-09-15
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  • Received:2025-11-08
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    1 College of Geological Engineering and Surveying, Chang'an University, Xi'an 710054, China
    2 China State Shipbuilding Corporation Limited Systems Engineering Research Institute, Beijing 100094, China
    3 61363 Troops of PLA, Wuhan 430071, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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