گرانی سنجی

Summary:

The inversion of gravity data is one of the most important steps in the interpretation of these data. The purpose of this work is to estimate the distribution of the unknown subsurface model by the measured data on the ground. The main problem in the inversion of the data obtained from the operation of the gravity survey is the non-uniqueness response due to the inversion of geophysical data. Linear inversion of gravity data is an underdetermined and bad-condition. It is important to determine the optimal regularization parameter for the inversion of gravity data. One of these methods is the Generalized Cross-Validation (GCV). In this research, the quadratic method has been used as an optimization method.

Inversion of gravity data is one of the most important steps in the interpretation of practical gravity data. The goal of inversion is to estimate the density distribution of an unknown subsurface model from a set of known gravity observations measured on the surface. Inversion of gravity data is an underdetermined and ill-posed problem. In addition, the non-uniqueness of the solution is the main issue of the inversion. One way to achieve a suitable model result in the inversion is to carry out the inversion with smoothness and smallness constraints. The solution can then be obtained by minimization of an objective function that consists of a misfit function and one of Tikhonov regularization functions. The regularization parameter makes a trade-off between misfit and regularization function. The determination of an optimal regularization parameter is highly important in gravity data inversion. There are different methods for automatic estimation of the regularization parameter in inversion. The GCV method is one of the most popular methods for choosing optimal regularization parameters in the inversion of gravity data.

Methodology and Approaches:

In this paper, we use the quadratic method to minimize the Tikhonov objective function. Also, in order to obtain the regularization parameter of the generalized cross-validation (GCV) method. The GCV method has been adapted for the solution of inverse problems. The basic idea for GCV is that a good solution to the inverse problem is one that is not unduly sensitive to any particular datum. In this method, the optimal regularization parameter minimizes the GCV function. We have developed an algorithm for 2-D inversion of gravity data that uses the GCV method for choosing optimal regularization parameter, and then, the inverse problem is solved by the quadratic algorithm. To evaluate the reliability of the introduced method, the gravity data of a synthetic model contaminated by 5 percent random noise have been inverted using the developed method. Finally, The introduced algorithm has been used for 2-D inversion of gravity data from San Nicolas massive sulfide deposit. The results are consistent with geological information.

In this paper, the GCV method has been developed for choosing the optimal regularization parameter in 2-D constrained inversion of gravity data using the quadratic algorithm. Data from the synthetic model have been inverted using the introduced algorithm and acceptable results have been obtained. The geometrical parameters of the synthetic model have been obtained from the inversion process with acceptable accuracy. After validation of the algorithm performance on the synthetic model, it has been applied for 2-D inversion of gravity data from San Nicolas massive sulfide deposit. The results of geological information in the area confirm the results of the 2-D inversion.

 Chromite exploration is really important in mineral exploration. Gravity method is really important in chromite exploration. Edge detection methods are used to determine lenses of chromite. In this paper, we used the curvature gravity gradient tensor (CGGT) along with the tilt angle method to detect chromite lenses. Application of the methods on synthetic and real gravity data showed that the CGGT can determine the edges of chromite lenses better than the tilt angle method.

Chromite is a strategic mineral. Therefore, the exploration of chromite mineral reserves is the main mineral exploration priorities. Chromite has a marked density contrast with the host rock, so the gravity method can be applied for exploration of the chromite ore bodies. The boreholes locations are usually determined after finding the edges of the chromite lenses by edge detection of the gravity anomalies. There are various edge detection methods. Most of the edge enhancement techniques are interpreted qualitatively. The Tilt angle method is a traditional method that can detect edges of subsurface structures quantitatively. The value of Tilt angle is zero above edges of subsurface bodies. The curvature gravity gradient tensor (CGGT) was also used to interpret the geological structure quantitatively. The value of eigenvalues of CGGT are zero above edges of subsurface bodies. In this paper, we used CGGT for edge detection of chromite lenses.

In order to obtain CGGT, at first, horizontal vector gradients of gravity gradient tensors are computed from the vertical component of gravity data with a Fourier transform technique. Then the eigenvalues of CGGT are obtained. The large eigenvalue determines the edges of negative density bodies while the small eigenvalue only can be used to outline edges of positive density bodies. The chromite has positive density contrast with the host rock and produce positive gravity anomaly. Therefore, we choose the small eigenvalue to outline edges of the chromite lenses. Finally, the tilt angle is also applied to compare with the CGGT.

The robustness of the codes used for the edge enhancement is tested with gravity field anomaly map caused by four prisms of synthetic bodies. The results indicated that the proposed method can enhance the edges of the synthetic bodies with zero contour of the small eigenvalue of the CGGT. Then, the proposed method has been applied on the real gravity data from chromite deposits In Camaguey province, Cuba. The results showed that the zero contour of the small eigenvalue of the CGGT can outline the edges of synthetic bodies and chromite lenses better than the zero contour of the tilt angle method. Therefore, we can use the small eigenvalue of the CGGT to detect edges of chromite lenses precisely.

The goal of this study is to process and interpret gravity data acquired from Sabzevar chromite exploration area by using geophysical filters such as trend removal, tilt angle, hyperbolic tilt angle. it is also aimed to compare the result of mentioned filters with one of the most effectivemethods in 3-D inverse modeling of geophysical data, named Li-Oldenburg algorithm. The inversion procedure resembles optimization process and the purpose is to find a model that minimizes the model objective function and produces model’s data that could be validated by the original observations. For comparing each filter with the result of 3D modeling, it is tried to choose the best view of final model. The comparison’s results indicate that the outcome of 3D inverse modeling is in good agreement with those of applied filters. The research’s outcome represents that there are two faults with surface outcrops in the south and center of the area, and also a variable lithology with north-south direction in the west.There can also be seen several outspread deposits of chromite with bottom’s depth of 25 meters, and a main anomaly at the west of the area which laterally extend up to 45 meters depth.

The gravimetric method is one of the most effective ways for the detection of subsurface structures such as tunnels, faulting, mineral deposits with the required density difference, especially in areas with rough topography and tectonized formations. It is possible to provide a more suitable model for better interpretation of the gravity anomaly. Inverse modeling of gravity data is the most effective way of interpretation, and in this regard, several methods have been proposed. In this paper, the 3D inverse method of Camacho is used for modeling gravity field data in a mineralized region. The case study includes an adit mining tunnel that accesses to a coal mineral and a drift tunnel which is partly drilled into the coal layer. The result of modeling has shown the depth and direction of the mineralized tunnels, Surface mining and coal layers. The modeling data was in good agreement with that of the observations and field measurements.

The gravity anomalies include gravity effects of underground structures and ore bodies، which have different densities and located in different depths. Anomaly separation methods such as trend analysis، filtering and analytical continuations، discriminate and separate the residual data from the regional components and reveal the position of the underground structures and ore bodies in the study area. Although there were some improvements in anomaly separation methods in the past decades، but there are in doubts in the determination of the anomaly boundaries. On the other hand، the removed regional anomalies still contain the effects of the residual values، which are anomaly values in reality. Modeling of raw data on probability plots is a method for determination of regional and residual trends (different sub- populations) and the exact threshold value for each effect or component (or sub-population). In this method، the data shows the boundaries of different components (regional and residual). The basic theory of probability plots modeling have been compared with the common gravity anomaly separation methods in this paper.

In this study, modeling of probability diagram method was used to calculate the residual component of gravity data at southwest of Zagros, Iran. The regional and residual components were separated based on the data analysis and evaluation of the various trends modeled on probability diagram. The first, second and optimum approximation of residual components were achieved based on different threshold values resulted from the modeled probability diagram. The regional and residual effects of the first and second approximations were not completely separated from each other in some parts of the study area. The 3D view of residual anomaly map of third trend surface, geological map together with the field investigations show that the optimum approximation map could remove the regional effects due to the deep geological structures from bBouguer anomaly values. Although not considered by the conventional anomaly separation methods, the proposed method takes into consideration the variation of the interested variable in the data set.