We present a wavenumber-domain iterative approach for rapid 3-D imaging of gravity anomalies and gradients data, which is based on the 3-D mesh model with a flat observational surface. The approach deconvolves the spectra of gravity anomalies or gradients by a 2-D deconvolution filter describing the spectrum of the imaging operator and then transforms the resultant spectra into the space domain to derive the density distribution. This 2-D deconvolution filtering is operated layer by layer from top to bottom in the subsurface and finally, all the results are merged to generate the 3-D density distribution. We improve previous 2-D deconvolution filters by involving a depth-scaling factor and utilize a priori constraint and the iteration algorithm for imaging, enable the presented approach to produce a density model with a considerable resolution and accuracy. The wavenumber-domain algorithm makes the imaging faster than the conventional space-domain inversions. Tests on the synthetic data and the real data from a metallic deposit area in Northwest China verified the feasibility and high efficiency of the presented approach.INDEX TERMS 3-D imaging, gravity anomaly, gradients, iterative, wavenumber domain.

Three-dimensional magnetic inversion, based on the least-square and regularization algorithm in the space domain, is an important tool for quantitative interpretation of magnetic data. However, the common 3D inversion approaches usually require great numbers of forward and inversion calculations and cause low efficiency for inverting large-scale data. Three-dimensional imaging is an alternate rapid tool for qualitative and quantitative interpretation of magnetic data. In this paper, we present a wavenumber-domain iterative approach for 3D imaging of magnetic anomalies and gradients, which could increase imaging efficiency and is suitable for rapidly imaging large-scale data. The wavenumber-domain formulas for forward modeling and imaging of total magnetic anomaly, three magnetic components, magnetic gradients and magnetic full-tensor gradients are deduced and provided. A depth-scale factor and the constraints of magnetic interface are included into the imaging formulas to enhance depth resolution. An iterative algorithm is adopted for the imaging to reduce the fitting error and improve the imaging accuracy. Tests on synthetic and real data from the Sichuan basin, China, verified the feasibility of the presented approaches.

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Fault interpretation tasks become more and more difficult as the complexity of seismic exploration increases, especially for ultra-deep seismic data. Recently, numerous researchers have utilized automatic interpretation techniques based on deep learning to improve the efficiency and accuracy of fault prediction. As a data-driven approach, the performance of deep learning networks depends heavily on the quantity and quality of the training datasets. In this paper, we develop a new technique called structural data augmentation. Concretely, we utilize different geological structure theories to incorporate virtual folds and faults in the field seismic data to improve the diversity and generalization ability of the training datasets. To cope with the multi-stage and multi-scale complex structures developed in ultra-deep strata, the proposed augmentation workflow increases data diversity by generating various virtual structures containing multi-scale folds, listric faults, oblique-slip displacement fields, and multi-directional fault drags. Tests on the field seismic data show that our method not only outperforms conventional seismic attributes but also has advantages over other machine learning methods.

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