DOI: 10.3390/min16080847 ISSN: 2075-163X

Characteristics Investigation and Sequential Joint Inversion of Potential Field Vector Components

Songlin Pan, Jun Wang, Yuan Fang, Jianyu Li

Recent advances in measurement techniques have greatly improved the accessibility of potential field vector data which contain richer directional information than conventional scalar data for high-precision localization of subsurface ore bodies. Nevertheless, the inversion characteristics of individual vector components remain poorly understood, and the effective integration of multi-vector components is still limited. To fulfill these gaps, this study first conducts a series of model experiments to systematically analyze the characteristics exhibited by each vector component. Subsequently, an iterative joint inversion scheme for comprehensive utilization of different vector components is proposed using the sequential strategy. Based on synthetic and real-data tests, it reveals that the horizontal X- and Y-components are most sensitive to sources extending along their respective axes, whereas the vertical Z-component, although beneficial for improving vertical resolution, exhibits a relatively weak anomaly amplitude. Most importantly, the proposed joint inversion method successfully integrates the complementary features of individual components, leading to marked improvements in inversion accuracy and source resolution, which provides a reliable tool for identifying concealed ore bodies, refining ore-body delineation, and prioritizing mineral exploration targets.

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