Three-directional analytic signal analysis and interpretation of magnetic gradient tensor

Compared to conventional magnetic data, magnetic gradient tensor data contain more high-frequency signal components, which can better describe the features of geological bodies. The directional analytic signal of the magnetic gradient tensor is not easily interfered from the tilting magnetization, b...

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Veröffentlicht in:Applied geophysics Jg. 17; H. 2; S. 285 - 296
Hauptverfasser: Guo, Can-wen, Xing, Zhe, Wang, Lin-fei, Ma, Yong, Huan, Heng-fei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Beijing Chinese Geophysical Society 01.06.2020
Springer Nature B.V
National marine data and information service, Tianjin 300171, China%China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China%Shenyang Center, China Geological Survey, Shenyang 110000, China
Schlagworte:
Algorithms
Anomalies
Attenuation
Data
Decay
Decay rate
Earth and Environmental Science
Earth Sciences
Euler-Lagrange equation
Feasibility studies
Geology
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Gravity and Magnetic Exploration Methods
Magnetic anomalies
Magnetic data
Mathematical analysis
Model testing
Recognition
Signal analysis
Signal strength
Tensors
magnetic gradient tensor
analytic signal
edge detection
ISSN:1672-7975, 1993-0658
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Zusammenfassung:Compared to conventional magnetic data, magnetic gradient tensor data contain more high-frequency signal components, which can better describe the features of geological bodies. The directional analytic signal of the magnetic gradient tensor is not easily interfered from the tilting magnetization, but it can infer the range of the field source more accurately. However, the analytic signal strength decays faster with depth, making it difficult to identify deep field sources. Balanced-boundary recognition can effectively overcome this disadvantage. We present here a balanced-boundary identification technique based on the normalization of three-directional analytic signals from aeromagnetic gradient tensor data. This method can effectively prevent the fast attenuation of analytic signals. We also derive an Euler inversion algorithm of three-directional analytic signal derivative. By combining magnetic-anomaly model testing with the traditional magnetic anomaly interpretation method, we show that the boundary-recognition technology based on a magnetic gradient tensor analytic signal has a greater advantage in identifying the boundaries of the geological body and can better reflect shallow anomalies. The characteristics of the Euler equation based on the magnetic anomaly direction to resolve the signal derivative have better convergence, and the obtained solution is more concentrated, which can obtain the depth and horizontal range information of the geological body more accurately. Applying the above method to the measured magnetic-anomaly gradient data from Baoding area, more accurate field source information is obtained, which shows the feasibility of applying this method to geological interpretations.
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ISSN:1672-7975
1993-0658
DOI:10.1007/s11770-020-0803-0