نوع مقاله : مقاله تحقیقی (پژوهشی)
عنوان مقاله [English]
The objective of this study is the 3D modeling inversion of gravity anomalies in order to obtain a density model in different depth sections. We used a new method based on stochastic methods. Among the stochastic methods that can be applied for solving inverse optimization problems in geophysics are meta-heuristic algorithms which are based on artificial intelligence. The ant colony algorithm is categorized in this group of algorithms. It works on the basis of probability and trial and error and follows ants' behavior in finding the shortest distance between the nest and the food. This behavior of ants is closely similar to the inverse problems in geophysics which try to find the best solution for the unknowns in observation model. Therefore, this idea is applied for solving linear inverse problems. A MATLAB-based inversion code for the presented method was prepared. To examine the performance of this method, three different artificial models were assayed. The structure of these models was considered as a combination of 3-dimentional cubes so as to model every unknown geometrical structure. In the first example, our purpose was to investigate interferential anomalies resulting from two simple models with different density contrasts located in different depths. In the second example, to show the ability of the algorithm in an inversion using small anomalies, a model of an irregular geometry was assessed in different depths. Finally, in the third example, an interferential anomaly resulting from two models of complicated geometry, namely T and L and of different density contrasts was assessed. This method was applied for artificial models with and without noise. The results show that for an inversion by the use of the ant colony algorithm, there is no need to separate the interferential anomaly and it is possible to use it for a combination of density contrasts. Also, this algorithm is able to inverse anomalies of an order of MGAL.
These anomalies belong to very small causative bodies such as: cavities and small ore-bodies. These anomalies are the main object of enviromental or engineering geophysics.
The algorithm is semi-authomatic and search the best results without comprehensive pre-conditions. The method is well designed to consider the multiple anomalies in complex conditions. This character enables us to use it for interpretation of complex anomalies caused by geological sources where most of the semi-authomatic methods are useless.
On the other hand, the inversion algorithm can be applied for different density contrasts. Relative positive or negative anomalies could be obtained by applying this method. This means that all different anomalies regarding their density contrasts can be detected and modelled through this method. Therefore we are not forced to isolate the object anomalies for inversion and this makes its application fast and easy for the whole surface of the Bougure residual anomalies. This character is very rarely obtainable in the published inversion algorithms. This character is particularly helpful when we would like to invert the precise data in the case of engineering geophysics.
Another advantage of this method is its velocity due to its usage of probability and trial and error theory. This advantage is very important when we are facing with a large set of data and parameters to invert. The high number of parameters is a major problem in linear inversion methods, but can be treated by this method very properly.