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The subsurface three-dimensional (3D) model of density distribution is obtained by solving an under-determined linear equation that is established by gravity data. Here, we describe a new fast gravity inversion method to recover a 3D density model from gravity data. The subsurface will be divided into a large number of rectangular blocks, each with an unknown constant density. The gravity inversion method introduces a stabiliser model norm with a depth weighting function to produce smooth models. The depth weighting function is combined with the model norm to counteract the skin effect of the gravity potential field. As the numbers of density model parameters is NZ (the number of layers in the vertical subsurface domain) times greater than the observed gravity data parameters, the inverse density parameter is larger than the observed gravity data parameters. Solving the full set of gravity inversion equations is very time-consuming, and applying a new algorithm to estimate gravity inversion can significan...

Full tensor gradient (FTG) data is highly useful in hydrocarbon exploration and the detection of some geological targets with small size as its higher detailed information abundance and finer resolution. At the same time, there are some high-frequency Gaussian white noise mixed in the target signal and which has closer frequency range than the conventional gravity data. Thus, one key step before inversion is to remove as much Gaussian white noise as possible and reserve the subtle details. For this pre-processing step, several effective methods have been used, including low-pass filters, least square fitting methods based on Laplace equation and wavelet filtering methods. In this paper, we would utilize the translation invariant wavelet for the reason that it can suppress Gaussian white noise through multi-resolution analysis and at the same time can avoid pseudo-Gibbs phenomenon. The other point different from wavelet method used before is that we applied a mixed threshold constructed according to the curve of both soft threshold and hard threshold. Compared to soft and hard threshold, mixed threshold can keep more details and remove more noise respectively in terms of the energy distribution of signal and noise. Then we process wavelet coefficients with mixed threshold and do inverse transform to recover the data. The results demonstrate that translation invariant wavelet can not only remove the major part of Gaussian white noise, but also reserves high-frequency detailed information of FTG data. Obviously, translation invariant wavelet with mixed thresholding has preferable application effect in filtering FTG data.

This paper proposes a new semi-automatic inversion method for magnetic anomaly data interpretation that uses the combination of analytic signals of the anomaly at different heights to determine the depth and the structural index N of the sources. The new method utilizes analytic signals of the original anomaly at different height to effectively suppress the noise contained in the anomaly. Compared with the other high-order derivative calculation methods based on analytic signals, our method only computes first-order derivatives of the anomaly, which can be used to obtain more stable and accurate results. Tests on synthetic noise-free and noise-corrupted magnetic data indicate that the new method can estimate the depth and N efficiently. The technique is applied to a real measured magnetic anomaly in Southern Illinois caused by a known dike, and the result is in agreement with the drilling information and inversion results within acceptable calculation error.

Geophysical exploration generates a very large amount of data, which require special techniques to process meaningfully. This paper presents a modification of the inversion method of probability tomography and a practical application of the method for the joint inversion with multiple gravity gradiometry tensors. The depth-weighting matrix is introduced to increase the vertical resolution. The problems associated with choosing tensors are discussed. We combine the high-performance computing techniques, Message Passing Interface with Compute Unified Device Architecture, for hybrid programming to build a faster parallel program. In the synthetic model tests, it is found that the joint inversion with multiple tensors is superior to the classical algorithm with a single tensor for delineating the boundary of the adjacent geological bodies. The hybrid parallel program has a speedup of far more than 100, which is higher than the program using one parallel technique alone; thus, the program could be used for large-scale data. The parallel algorithm is applied to real geophysical exploration data from Vinton Dome and we obtained a good density result. Our algorithm is demonstrated to be reliable and feasible.

We modify the classical preconditioned conjugate gradient method for full tensor gravity gradiometry data. The resulting parallelized algorithm is implemented on a cluster to achieve rapid density inversions for various scenarios, overcoming the problems of computation time and memory requirements caused by too many iterations. The proposed approach is mainly based on parallel programming using the Message Passing Interface, supplemented by Open Multi-Processing. Our implementation is efficient and scalable, enabling its use with large-scale data. We consider two synthetic models and real survey data from Vinton Dome, US, and demonstrate that our solutions are reliable and feasible.

Edge detection is a useful tool in the interpretation of potential field data, and the existing edge detection filters are almost functions of first-order horizontal and vertical derivatives. We propose step-edge detection filters to improve the resolution of edge detection results, which use the functions of different-order derivatives to accomplish the edge detection task. We demonstrate the proposed filters on synthetic potential field data, and the results show that the new methods can recognize the edges of the sources more precisely and clearly. We also discuss the application effect of different step-edge detection filters. Lastly, we apply the proposed filters to real potential field data, and the recognized edges of the stratigraphic markers are more precise and clear.

Many of the existing balanced edge detection filters of potential field data only use the feature that the vertical derivative is zero above the source edges to recognise the source edges. This will produce spurious edges in the interpretation of potential field data. In order to solve this disadvantage, a new format of the edge detection filter is presented, which produces maximum values only when the horizontal derivative is a maximum and the vertical derivative is zero, so the new filters will not produce spurious edges, and will provide more accurate results. The proposed filters are tested on synthetic potential field data. The recognised edges are shown to be consistent with the true edges, and do not produce additional edges. Moreover, the proposed filter suppresses the effect of noise, and displays the edges more clearly compared to previous edge detection filters. It is also applied to real magnetic data to obtain the horizontal locations of iron ore.

Balanced edge detection filters can recognize the edges of the shallow and deep bodies simultaneously, and are commonly used in the edge detection of potential field data. In this paper, we present using the balanced edge detection filters to estimate source locations, and derive two linear equations based on the balanced edge detection filters that can estimate the locations of the source without any priori information about the nature (structural index) of the source. The proposed methods are demonstrated on synthetic gravity anomalies, and the inversion results show that the proposed methods can successfully estimate location parameters of the sources. I also apply the proposed methods to real magnetic data, and the inversion results estimated by the proposed methods are consistent with the results estimated by the other similar method.

Downward continuation is a useful tool in the processing of potential field data, which can effectively enhance weak anomalies and identify overlap anomalies, but we all know that the computation of downward continuation is unstable, and easily distorts the true feature of potential field data. Because the computation of upward continuation and horizontal derivatives is stable, we proposed using the combination of upward continuation and horizontal derivative to accomplish the downward continuation of potential field data. The proposed method is demonstrated on synthetic potential field data, and the results show that the proposed method can finish the downward continuation of the data stably and precisely, and the precision of the proposed method is higher than the traditional method. We also apply it to real potential field data, and the results show that the proposed method accomplishes the downward continuation of the real data stably.