Geomagnetically induced current uncertainty quantification based on the earth conductivity models constructed by the Kriging method

Liu, Qing; Li, JinLong; Ding, Nan; Yi, XiaoHu; Li, Liang; Zhong, Hao

doi:10.1016/j.epsr.2022.108846

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…where N(l) represents the logarithm of the data pair divided by the vector l. For different lags l, the values of γ * (l) can be calculated according to Equation ( 6), and points [l i , γ(l i )] can be marked in the graph of the experimental variogram, which can directly reflect the relevant situation of measurement data [27].…”

Section: Experimental Variogrammentioning

confidence: 99%

Enhancing Precision in Magnetic Map Interpolation for Regions with Sparse Data

Li,

Zhang,

Pan

et al. 2024

Applied Sciences

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The high-precision magnetic anomaly reference map is a prerequisite for magnetic navigation and magnetic target detection. However, it is difficult to reflect the detailed characteristics of magnetic anomaly changes by using conventional data interpolation and reconstruction in the areas where magnetic anomaly gradients vary drastically and the distribution of magnetic survey lines is sparse. To solve the problem, an improved variogram of the Kriging interpolation method is proposed to improve the spatial resolution of magnetic anomaly feature. This method selects the spherical variogram model and uses the third power of the lag distance to fit the trend of magnetic anomalies. Meanwhile, the second power of the lag distance is introduced to solve the problem of under-fitting between the lag distance and the value of the variation function near the origin of the sparse variogram graph of measured data. Hyperparameter λ is introduced to compensate for the unbalance caused by the introduction of quadratic lag in the spherical variogram model. The results of several sets of simulated and measured data show that the interpolation accuracy of the proposed method is improved by 30–50% compared with the traditional Gaussian, spherical, and exponential models in the region where the magnetic anomaly gradient changes drastically, and the proposed model provides an effective way to build a high-precision magnetic anomaly reference map of the complex magnetic background under the condition of sparse survey lines.

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Section: Experimental Variogrammentioning

confidence: 99%

Enhancing Precision in Magnetic Map Interpolation for Regions with Sparse Data

Li,

Zhang,

Pan

et al. 2024

Applied Sciences

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“…In North China, 1,000 kV long transmission lines are prevalent in densely populated areas, which increases the potential for hazards from GICs during strong geomagnetic storms. Previous studies are mostly based on 1‐D resistivity models in North China (Liu et al., 2015; Zheng et al., 2013) and with a few simplified 3‐D models of local areas (Liu, Fu, et al., 2023; Liu, Li et al., 2023; Wang et al., 2015; Zhang et al., 2015). This paper presents and discusses the induced geoelectric field in North China during a large geomagnetic storm on 17th March 2015, estimated from interpolated geomagnetic field variations coupled with 119 MT impedance tensors from the SinoProbe project.…”

Section: Introductionmentioning

confidence: 99%

Geoelectric Field Estimations During Geomagnetic Storm in North China From SinoProbe Magnetotelluric Impedances

Shao,

Liu,

Luo

et al. 2024

Space Weather

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Evaluating the impact of geomagnetic disturbances on power grid infrastructure is critical to mitigate the risk posed by geomagnetically induced currents (GICs). In this paper, the geoelectric field and induced voltage distribution in North China were estimated from the SinoProbe magnetotelluric (MT) impedance data together with the geomagnetic observatory data of six INTERMAGNET stations recorded during the significant geomagnetic storm of 17th March 2015. The measured impedances from 119 SinoProbe MT sites were convolved with geomagnetic observatory data to account for the Earth's complex three‐dimensional electrical resistivity structure. The resultant geoelectric field was then used to model the induced voltage distribution across the regional power transmission network in North China. Due to the large inter‐site distances of the SinoProbe MT program, the derived geoelectric field is mostly homogeneous, except in the Ordos Basin that displays a polarization of the geoelectric field, and with higher magnitudes in the orogenic belts. The estimated geoelectric fields in Taihang‐Lvliang, Yanshan, and Luxi orogenic belts of North China were large (>1 V/km) during the storm, due to high‐resistivity lithosphere resulting in large voltage gradients in the Earth. However, in relation to locations of major power transmission lines, only the central part of North China experienced induced voltages exceeding 100 V.

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Geomagnetically induced current uncertainty quantification based on the earth conductivity models constructed by the Kriging method

Cited by 2 publications

References 20 publications

Enhancing Precision in Magnetic Map Interpolation for Regions with Sparse Data

Enhancing Precision in Magnetic Map Interpolation for Regions with Sparse Data

Geoelectric Field Estimations During Geomagnetic Storm in North China From SinoProbe Magnetotelluric Impedances

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