Integrated Correction of Nonlinear Dynamic Drift in Terrestrial Mobile Gravity Surveys: A Comparative Study Based on the Northeastern China Gravity Monitoring Network

Αποθηκεύτηκε σε:
Λεπτομέρειες βιβλιογραφικής εγγραφής
Εκδόθηκε σε:Remote Sensing vol. 17, no. 12 (2025), p. 2025-2044
Κύριος συγγραφέας: Chen, Zhaohui
Άλλοι συγγραφείς: Liu, Jinzhao
Έκδοση:
MDPI AG
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024 7 |a 10.3390/rs17122025  |2 doi 
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100 1 |a Chen, Zhaohui 
245 1 |a Integrated Correction of Nonlinear Dynamic Drift in Terrestrial Mobile Gravity Surveys: A Comparative Study Based on the Northeastern China Gravity Monitoring Network 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a The Northeastern China Gravity Monitoring Network (NCGMN; 40–50°N), a pioneering time-variable gravity monitoring system in high-latitude cold-temperate environments, serves as a critical infrastructure for geodynamic investigations of the Songliao Basin, Changbai Mountain volcanic zone, and northern Tan-Lu Fault Zone. To address the data reliability challenges posed by nonlinear dynamic drifts in spring-type relative gravimeters during mobile surveys, this study quantifies—for the first time—the non-smooth normal distribution characteristics of such drifts using the inaugural 2015 dataset from two CG-5 instruments. Results demonstrate a 7–15% reduction in mean dynamic drift rates compared to static conditions, with spatiotemporal variability governed by multi-physics field coupling (terrain undulation, thermal fluctuation, and barometric perturbation). A comprehensive correction framework—integrating a gravimetric line drift rate computation, multi-model validation, and absolute datum cross-validation—reveals gravity value discrepancies up to ±10 μGal across models. The innovative hybrid scheme combines local drift preprocessing (initial-point modeling, line fitting, variance-sum optimization) with global adjustment optimization, achieving the significant suppression of nonlinear drift errors. The variance-sum optimal and Bayesian adjustment hybrid synergizes local variance minimization and global temporal correlation priors, delivering the following: (1) 34% and 29% reductions in segment self-difference standard deviations versus classical and Bayesian adjustments; (2) 24% and 14% decreases in segment residual standard deviations; (3) 12% and 6% improvements in absolute datum cross-validation precision. This study establishes a foundation for the reliable extraction of μGal-level gravity signals, advancing high-precision gravity monitoring of seismicity, volcanic unrest, and fault zone deformation in complex terrains. By harmonizing local-scale accuracy with network-wide consistency, the framework sets a new benchmark for time-variable gravity studies in challenging environments. 
651 4 |a China 
653 |a Datum (elevation) 
653 |a Gravity 
653 |a Variance 
653 |a Atmospheric pressure 
653 |a Normal distribution 
653 |a Standard deviation 
653 |a Data processing 
653 |a Monitoring 
653 |a Drift rate 
653 |a Comparative studies 
653 |a Bayesian analysis 
653 |a Seismicity 
653 |a Temperate environments 
653 |a Volcanic activity 
653 |a Optimization 
653 |a Earthquakes 
653 |a Surveys 
653 |a Dynamical systems 
653 |a Drift 
653 |a Segments 
653 |a Nonlinear dynamics 
653 |a Mathematical models 
653 |a Critical infrastructure 
700 1 |a Liu, Jinzhao 
773 0 |t Remote Sensing  |g vol. 17, no. 12 (2025), p. 2025-2044 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3223940340/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3223940340/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3223940340/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch