Gravity data have been frequently used in researching the subsurface to map the 3D geometry of the density structure, which is considered the basis for further interpretations, such as the estimation of exploration potential in mineral exploration. The gravity inversion, practically employed to map the density structure, can be achieved by different methods. The method based on Tikhonov regularization is the most commonly used among them. Usually, the subsurface is discretized into a set of cells or voxels. To recover a stable and reliable solution, constraints are introduced into the Tikhonov regularization. One constrained inversion introduces a quadratic penalty (L₂ norm) into the inversion, which imposes smooth features on the recovered model. Another gravity inversion, known as sparse inversion, imposes compactness and sharp boundaries on the recovered density structure. Specifically, the L₁ norm and L₀ norm are favored for such a purpose. This work evaluates the merits of the gravity data inversion in cooperation with different model norms and their applicability in exploration potential estimation. Because these norms promote different features in the recovered models, the reconstructed 3D density structure reveals different geometric features of the ore deposit. We use two types of synthetic data for evaluating the performances of the inversion with different norms. Numerical results demonstrate that L₀ norm-based inversion provides high-resolution recovered models and offers reliable estimates of exploration potential with minimal deviation from theoretical mass compared to inversions equipped with the other two norms. Finally, we use the gravity data collected over the iron ore deposit at the Dida mining area in Jilin province (Northeast China) for the application. It is estimated that the exploration potential of the iron ore deposits is about 3.2 million tons.

Edge enhancement is a frequently used transformation of potential field data. Its goal is to sharpen the position of the subsurface structures. Here we propose a new method to enhance the edges of the sources causing the potential anomalies called normalized Harris filter (NHF), which is based on the Harris filter and amplitude balance. Three synthetic data sets are used to evaluate the performance of the proposed approach. The presented approach provides a better estimation of the sources’ edges when compared to the other methods. The proposed method is robust to noisy data and can avoid the generation of artificial edges, thereby reducing the ambiguity of interpretation. The testing on real data set from the Yili basin in Northwestern China demonstrates that the new approach highlights several anomalies not shown in the geological map or other methods. The proposed approach also shows the advantages of gradually enhancing the edges of the deep-seated structure. The results demonstrate that the proposed approach may be a better detector in qualitatively determining the edges of sources causing potential field data.

Data interpolation is critical in the analysis of geophysical data when some data are missing or inaccessible. We interpolate irregular or missing potential field data using the relation between adjacent data points inspired by the Taylor series expansion (TSE). The TSE method first finds the derivatives of a given point near the query point using data from neighboring points and then uses the Taylor series to obtain the value at the query point. The TSE method works by extracting local features represented as derivatives from the original data for interpolation in the area of data vacancy. Compared with other interpolation methods, the TSE provides a complete description of potential field data. Specifically, the remainder in TSE can measure local fitting errors and help obtain accurate results. Implementation of the TSE method involves two critical parameters — the order of the Taylor series and the number of neighbors used in the calculation of derivatives. We find that the first parameter must be carefully chosen to balance between the accuracy and numerical stability when data contain noise. The second parameter can help us build an over-determined system for improved robustness against noise. Methods of selecting neighbors around the given point using an azimuthally uniform distribution or the nearest-distance principle are also presented. Our approach is first illustrated by a synthetic gravity data set from a single survey line, and then it is generalized to the case over a survey grid. In both numerical experiments, the TSE method demonstrates an improved interpolation accuracy in comparison with the minimum curvature method. Finally, we apply the TSE method to a ground gravity data set from the Abitibi Greenstone Belt, Canada, and an airborne gravity gradient data set from the Vinton Dome, Louisiana, USA.

Airborne and semi-airborne transient electromagnetic (TEM) surveys have high efficiency but may suffer from systematic errors due to the inexact shape, position, and orientation of the transmitter and receiver, which can deviate from the nominal design because of complex terrain, platform instability, or external forces. Without considering actual survey geometry, modeling and inversion can bias the interpretation of results. We develop a universal approach to layered earth capable of modeling arbitrarily complex transmitter and receiver geometry used in airborne and semi-airborne surveys. Our algorithm decomposes an airborne loop or grounded wire source to a set of x-, y-, or z-oriented electric dipoles. An arbitrarily oriented receiver coil is simulated by projecting three-component data to the actual direction of receiving. In airborne TEM, the transmitter loop and receiver coil are often bound together on a rigid frame and tilt during the flight. Our simulations and synthetic inversion show that such a tilt may reduce responses relative to the data obtained with the nominal geometry; an inversion without considering the tilt can underestimate near-surface conductivity. In semi-airborne TEM, the transmitter wire on the surface can be crooked, and the airborne receiver coil can also tilt. Our modeling shows that the simulated data can change significantly if the actual transmitter and receiver geometry does not exactly follow the nominal survey design; if not appropriately accounted for, such an error may distort the recovered conductivity model. Finally, the benefit of our algorithm is demonstrated by an airborne TEM field data inversion of groundwater problems with the tilt angle of the transmitter–receiver frame accurately modeled. Our work provides a tool for improving the resolution of airborne and semi-airborne TEM in near-surface conductivity characterization.

SUMMARY: Gravity gradient tensors (GGTs) are used to investigate the density of subsurface structures in the Earth's crust and can reduce ambiguity during data interpretation. However, costs and research area restrictions often prevent their application during surveys, thereby limiting their utility. To address this limitation, a matrix equation based on the Taylor series expansion that uses the gravity vector and its neighbours was formulated to obtain the GGTs. Higher-order derivatives of the gravity vector were utilized to constrain the calculation, which improved the accuracy of the transformation. Synthetic data were used to demonstrate that the proposed approach improved accuracy when the radial component of the gravity vector was transformed into GGTs. This approach was also applied to gravity data from the Otway Basin in Australia. Compared with the measured GGT, the results obtained using the proposed approach had a relative error of 0.46.