The full magnetic gradient tensor （MGT） refers to the spatial change rate of the three field components of the geomagnetic field vector along three mutually orthogonal axes. The tensor is of use to geological mappin...The full magnetic gradient tensor （MGT） refers to the spatial change rate of the three field components of the geomagnetic field vector along three mutually orthogonal axes. The tensor is of use to geological mapping, resources exploration, magnetic navigation, and others. However, it is very difficult to measure the full magnetic tensor gradient using existing engineering technology. We present a method to use triaxial aeromagnetic gradient measurements for deriving the full MGT. The method uses the triaxial gradient data and makes full use of the variation of the magnetic anomaly modulus in three dimensions to obtain a self-consistent magnetic tensor gradient. Numerical simulations show that the full MGT data obtained with the proposed method are of high precision and satisfy the requirements of data processing. We selected triaxial aeromagnetic gradient data from the Hebei Province for calculating the full MGT. Data processing shows that using triaxial tensor gradient data allows to take advantage of the spatial rate of change of the total field in three dimensions and suppresses part of the independent noise in the aeromagnetic gradient. The calculated tensor components have improved resolution, and the transformed full tensor gradient satisfies the requirement of geological mapping and interpretation.展开更多
Airborne gravity gradient data contain additional short-wavelength information about the buried geological bodies.This study develops a fast interpretation method based on the gravity gradient data for the sources’sp...Airborne gravity gradient data contain additional short-wavelength information about the buried geological bodies.This study develops a fast interpretation method based on the gravity gradient data for the sources’spatial location and physical property parameters.This study analyzes the advantages of the source parameter inversion method based on tensor invariants.It proposes a normalized fast-imaging method based on tensor invariants to quickly estimate the spatial location parameters of sources through the local maximum value position of the imaging results.First,the tensor invariant characteristics and the imaging method’s effect in a simple model are analyzed using a theoretical model.Second,to analyze the imaging method’s application effect in complex model conditions,the method’s applicability is quantitatively analyzed using the data added with noise,superimposed anomalies of adjacent sources,and anomalies of deep and shallow geological bodies.The theoretical model’s simulation results show that the model’s imaging results in this study have satisfactory performance on the spatial position estimation of the sources.Finally,the method is applied to the gravity anomaly data corresponding to the Humble salt dome.The imaging results can effectively estimate the distribution of the salt dome’s horizontal and depths,verifying the practicability of the method.展开更多
The gravity field models GUCAS_EGM and GUCAS_EGM_DL are established from GOCE data (GOCE Level 2 Products from Nov. 1 to Dec. 31, 2009) based on the method of the invariants of the gravity gradient tensor, where GUCAS...The gravity field models GUCAS_EGM and GUCAS_EGM_DL are established from GOCE data (GOCE Level 2 Products from Nov. 1 to Dec. 31, 2009) based on the method of the invariants of the gravity gradient tensor, where GUCAS_EGM is derived after GOCE gravity gradient data are filtered with FIR, and GUCAS_EGM_DL is computed with an additional Durbin-Levison arithmetic apart from FIR. Since this method, different from current programs dealing with GOCE data, is introduced for the first time, some new problems are required to be discussed in advance; for example, how to filter GOCE gravity gradient data, how to compute the invariants of the gradient tensor, and how to deal with the pole gap and so on. In addition, by comparing our models with ones recommended by ESA, it can be seen that the variations of GUCAS_EGM and the models recommended by ESA to EGM08 are almost equivalent, and the variation of GUCAS_EGM_DL to EGM08 is obviously less than ones of the recommended models.展开更多
基金supported by the National High Technology Research and Development Program of China(863 Program)(No.2013AA063901 and No.2006AA06A201)
文摘The full magnetic gradient tensor （MGT） refers to the spatial change rate of the three field components of the geomagnetic field vector along three mutually orthogonal axes. The tensor is of use to geological mapping, resources exploration, magnetic navigation, and others. However, it is very difficult to measure the full magnetic tensor gradient using existing engineering technology. We present a method to use triaxial aeromagnetic gradient measurements for deriving the full MGT. The method uses the triaxial gradient data and makes full use of the variation of the magnetic anomaly modulus in three dimensions to obtain a self-consistent magnetic tensor gradient. Numerical simulations show that the full MGT data obtained with the proposed method are of high precision and satisfy the requirements of data processing. We selected triaxial aeromagnetic gradient data from the Hebei Province for calculating the full MGT. Data processing shows that using triaxial tensor gradient data allows to take advantage of the spatial rate of change of the total field in three dimensions and suppresses part of the independent noise in the aeromagnetic gradient. The calculated tensor components have improved resolution, and the transformed full tensor gradient satisfies the requirement of geological mapping and interpretation.
基金supported by the National Key R&D Program of China(No.2020YFE0201300)Natural Science Foundation of Jilin Province(No.20210508033RQ)Fundamental Research Funds for the Central Universities and Geological Survey Project(No.DD20190129).
文摘Airborne gravity gradient data contain additional short-wavelength information about the buried geological bodies.This study develops a fast interpretation method based on the gravity gradient data for the sources’spatial location and physical property parameters.This study analyzes the advantages of the source parameter inversion method based on tensor invariants.It proposes a normalized fast-imaging method based on tensor invariants to quickly estimate the spatial location parameters of sources through the local maximum value position of the imaging results.First,the tensor invariant characteristics and the imaging method’s effect in a simple model are analyzed using a theoretical model.Second,to analyze the imaging method’s application effect in complex model conditions,the method’s applicability is quantitatively analyzed using the data added with noise,superimposed anomalies of adjacent sources,and anomalies of deep and shallow geological bodies.The theoretical model’s simulation results show that the model’s imaging results in this study have satisfactory performance on the spatial position estimation of the sources.Finally,the method is applied to the gravity anomaly data corresponding to the Humble salt dome.The imaging results can effectively estimate the distribution of the salt dome’s horizontal and depths,verifying the practicability of the method.
基金supported by National Natural Science Foundation of China (Grant No.41074015)Program of Chinese Academy of Sciences (Grant No.XMXX280730)
文摘The gravity field models GUCAS_EGM and GUCAS_EGM_DL are established from GOCE data (GOCE Level 2 Products from Nov. 1 to Dec. 31, 2009) based on the method of the invariants of the gravity gradient tensor, where GUCAS_EGM is derived after GOCE gravity gradient data are filtered with FIR, and GUCAS_EGM_DL is computed with an additional Durbin-Levison arithmetic apart from FIR. Since this method, different from current programs dealing with GOCE data, is introduced for the first time, some new problems are required to be discussed in advance; for example, how to filter GOCE gravity gradient data, how to compute the invariants of the gradient tensor, and how to deal with the pole gap and so on. In addition, by comparing our models with ones recommended by ESA, it can be seen that the variations of GUCAS_EGM and the models recommended by ESA to EGM08 are almost equivalent, and the variation of GUCAS_EGM_DL to EGM08 is obviously less than ones of the recommended models.
