The observed potential field data are usually corrupted by noises from different sources, and these contained noises pose great adverse impacts on further data processing. In the literature, many noise reduction methods have been introduced to solve this problem, most of which are based on the idea of high-pass filter. However, there are some limitations for most of these conventional methods, such as the elimination of some effective high-frequency components caused by the shallower small-scale sources, resulting in reduced denoising precision, and the difficulties in determining the filter parameters manually. Faced with these problems, a new method based on RevU-Net architecture is firstly proposed to handle above issues. The method expands the feature information through the up-sampling layer at the expansion path, and uses the skip-connection technology to fuse multi-scale features. Also, the network updates itself towards the purpose of multi-scale signal capture and separation, which provides an intelligent approach and enable it to distinguish the small-scale components and noise without human intervention. The proposed method is tested on several synthetic examples. The results illustrate that this method yields satisfactory results while preserving the features of the shallower small-scale sources without the any manually setting parameters. So, it overcomes the limitations of the conventional methods stated above.

The joint inversion methods of gravity and magnetic data have been presented in numerous literatures and implemented in many real applications. However, almost all of these existing methods still face the problem of low computational efficiency, although some targeted improvements have been put forwarded. To further address this issue, this study proposes an efficient algorithm for the joint inversion of gravity and magnetic data. The new algorithm is developed from the idea of structural coupling by using the cross-gradient function and employs a sequential strategy. A new formula for efficient minimization of the structural similarity term is derived innovatively, in which the minimization of the structural coupling term is obtained by a technical combination of an alternating strategy and the randomized singular value decomposition (RSVD) algorithm. Using the proposed new formula, the dimension of the original inverse problem can be reduced greatly. Furthermore, the RSVD algorithm is utilized to perform the independent inversion and determine an optimal regularization parameter. Several comparative tests on a synthetic example show that the proposed efficient method poses great advantage in efficiency compared with the conventional sequential cross-gradient joint inversion (CSCGJI) method. The proposed algorithm is also successfully applied to the real data from a metallic deposit area in Xinjiang province, China.

The 3-D marine gravity field plays an important role in many practical applications, such as matching navigation, object monitoring, and resource exploitation. However, the accuracy of the gravity field generated by most existing methods is relatively low. To solve this problem, this study innovatively puts forward a new method for establishing a high-accuracy 3-D marine gravity field by using a joint inversion algorithm. The presented method incorporates gravity fields observed at different altitudes, especially those observed near the seafloor, because they contain more prominent short-wavelength signals of the sources. The final joint inversion formulas are developed from the standard form of independent inversion of single gravity datasets, so it is conducive to include different weighting matrices. To achieve the efficiency of the presented method, the nonlinear objective function is minimized iteratively using a fast randomized singular value decomposition (RSVD) technique. The employing of RSVD also facilitates the determination of an optimal regularization parameter based on the generalized cross-validation criterion. The presented method is tested on both synthetic gravity fields simulated by a theoretical density model and real gravity fields collected in the South China Sea. Numerical results demonstrate that the presented method can provide more accurate 3-D gravity fields than the commonly used iterative downward continuation method in the wavenumber domain (DCW) and the conventional equivalent source (CES) method.