Optimal Resolution of Earth’s Shallow Conductivity Structure for Predicting Tidal-Induced Magnetic Fields at Geomagnetic Satellite Altitudes
Earth, Planets and Space(2025)
Central South University
Abstract
Geomagnetic satellites provide a unique opportunity to measure tidal-induced magnetic fields with exceptional accuracy and extensive spatial coverage. Recent studies have demonstrated that these satellite-detected signals are sensitive to the lithosphere-asthenosphere boundary, providing valuable insights into its global average depth. However, the reliability of the imaged lithosphere-asthenosphere boundary may be questionable due to the lack of associated statistical uncertainty analysis. This limitation arises from the high computational costs involved in incorporating Earth’s shallow conductivity structure into three-dimensional simulations. Therefore, it is essential to find a balancing point between the accuracy of the predicted satellite tidal-induced signals and the computational burden of these three-dimensional simulations, consistent with the precision of the magnetometers onboard. To address this issue, in this study, we investigated the optimal resolution of Earth’s shallow electrical structure for accurately simulating three-dimensional satellite tidal-induced signals with minimal computational cost. With the accuracy requirement 0.5 nT, this study found that Earth’s shallow conductivity model lateral resolution of 1 degree is ideal for M_2 tidal-induced magnetic field simulation at an altitude of 200 km, while a resolution of 3 degrees is more suitable at 450 km. For O_1 tide, it becomes 1 degree and 2 degrees. These findings are not only crucial for stochastic inversion of satellite signals to provide reliable deep Earth conductivity structure, but also are helpful for geophysicists in the geo-electromagnetic community to further analyze the satellite tidal-induced magnetic data sets.
MoreTranslated text
Key words
Electromagnetic induction,Tide,Geomagnetic satellite,Optimal resolution
PDF
View via Publisher
AI Read Science
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined