Magnetotelluric Adaptive Inversion Using Multi‐Resolution Tetrahedral Grids: Application to the North China Craton

大地电磁法 克拉通 反演(地质) 四面体 地质学 北京 中国 地震学 地理 几何学 工程类 电阻率和电导率 考古 数学 构造学 电气工程
作者
Huang Chen,Zhengyong Ren,Jingtian Tang
出处
期刊:Journal Of Geophysical Research: Solid Earth [Wiley]
卷期号:130 (4) 被引量:8
标识
DOI:10.1029/2024jb030405
摘要

Abstract We developed a novel three‐dimensional magnetotelluric adaptive inversion algorithm optimized to interpret field datasets collected in realistic geological environments. Using a newly designed data‐driven indicator, it tends to enhance features in data‐sensitive regions and generate a set of multiscale inversion models with gradually increased resolution. Additionally, utilizing the nested tetrahedral grids, it meets different mesh resolution requirements for forward modeling and inversion, which addresses the trade‐off between modeling accuracy and computational load. Validation against synthetic data confirms the algorithm's ability to efficiently delineate subsurface structures, notably enhancing the interpretability of magnetotelluric data. We applied the proposed algorithm to reinterpret field magnetotelluric data collected in the North China Craton within complex geological settings. The resulting conductivity structures reveal consistent high conductivity anomalies in the western Ordos Basin and the North China Plain, reflecting younger geological conditions. Additionally, high resistivity characteristics are observed beneath mountains such as the Luliang and Taihang Mountains, and three common high‐conductivity anomalies from the upper mantle are identified. Notably, we found a previously identified conductor at 20–70 km depth beneath the southern Bohai Bay Basin, previously interpreted as electrical conductivity anisotropy, is now positioned at a deeper depth near the lithosphere‐asthenosphere boundary, suggesting it may represent upwelling asthenospheric material. This research highlights the proposed adaptive inversion algorithm's potential to enhance subsurface imaging in geophysical exploration, with future integrations with other geophysical methods and efficiency improvements poised to extend its applicability to more complex datasets, aiding resource exploration, geohazard assessment, and deep Earth studies.
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