Large Airborne Full Tensor Gradient Data Inversion Based on a Non-Monotone Gradient Method

作者：Sun, Yong^*; Meng, Zhaohai; Li, Fengting

来源：Pure and Applied Geophysics, 2018, 175(3): 1065-1084.

DOI：10.1007/s00024-017-1723-7

• FTG data inversion
• non-monotone gradient-descent method
• large scale of data

Following the development of gravity gradiometer instrument technology, the full tensor gravity (FTG) data can be acquired on airborne and marine platforms. Large-scale geophysical data can be obtained using these methods, making such data sets a number of the "big data" category. Therefore, a fast and effective inversion method is developed to solve the large-scale FTG data inversion problem. Many algorithms are available to accelerate the FTG data inversion, such as conjugate gradient method. However, the conventional conjugate gradient method takes a long time to complete data processing. Thus, a fast and effective iterative algorithm is necessary to improve the utilization of FTG data. Generally, inversion processing is formulated by incorporating regularizing constraints, followed by the introduction of a non-monotone gradient-descent method to accelerate the convergence rate of FTG data inversion. Compared with the conventional gradient method, the steepest descent gradient algorithm, and the conjugate gradient algorithm, there are clear advantages of the non-monotone iterative gradient-descent algorithm. Simulated and field FTG data were applied to show the application value of this new fast inversion method.

• 出版日期2018-3
• 单位吉林大学