Exploration
Mojtab Babaei; Hamid Aghajani
Abstract
Accurate modeling of gravity anomalies caused by geological structures remains a central challenge in exploration geophysics. In mineral exploration, geophysical targets can be approximated using simple geometric shapes, and inversion techniques are employed to estimate the parameters of buried anomalous ...
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Accurate modeling of gravity anomalies caused by geological structures remains a central challenge in exploration geophysics. In mineral exploration, geophysical targets can be approximated using simple geometric shapes, and inversion techniques are employed to estimate the parameters of buried anomalous bodies. In this study, gravity data from the Cham Gilan bitumen deposit were interpreted using Improved Particle Swarm Optimization (IGPSO) and Levenberg–Marquardt (LM) algorithms. According to borehole information, the mineralized zone was represented by a dipping slab. To model gravity anomalies with these approaches, a two dimensional inclined slab of finite extent was first considered as a synthetic model. Using the metaheuristic IGPSO and deterministic LM algorithms, five parameters of the 2D slab (dip angle, upper depth, lower depth, thickness, and density contrast) were estimated. Synthetic gravity data were generated under two conditions: noiseless and with 5% Gaussian noise. Each inversion algorithm was executed 30 times (Monte Carlo runs) to evaluate accuracy, stability, and computational efficiency. Dispersion analysis indicated that LM, due to its deterministic nature, produced near zero standard deviation for noiseless data, while IGPSO exhibited notable stability despite its stochastic characteristics. The average computation time for LM and IGPSO was about 0.04 s and 1.44 s, respectively. Both methods were further employed to interpret the gravity data of the Cham Gilan bitumen zone, and the inversion results showed strong agreement with geological observations and core drilling evidence.
Exploration
Reza Shahnavehsi; Farnusch Hajizadeh
Abstract
The present work is mainly about a method for illustrating the relation between the raw data in the same time; clustering is a key procedure to solve the problem of data division; also illustrating the connection among the elements of the research area simultaneously is important. Therefore, we propose ...
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The present work is mainly about a method for illustrating the relation between the raw data in the same time; clustering is a key procedure to solve the problem of data division; also illustrating the connection among the elements of the research area simultaneously is important. Therefore, we propose a novel kind of clustering for data mining in the gravity field to reach the presenting connection among all elements in the same time. For this research work, 867 gravity surveying points were collected in the southern part of Iran (near diapir of Larestan) with a range of absolute gravity from 978579.672 to 978981.186. In this paper, clustering by self-organizing- maps, by utilizing scatter plot matrix is utilized for detecting the relation between the easting, northing, elevation, and absolute gravity simultaneously. In the proposed method, the relations between arrays, two by two, are defined, and like matrix, each raw and column has different i and j values, which represent elements of the studied area, instead of number; for example, array A23 is data division between i = 2 or raw two (in our case northing) and j = 3 or column, three (in our case elevation). In this algorithm, firstly, by using self-organizing maps, clustering is done, and this processing is generated to all arrays by scatter plot matrix, and in all arrays, three clusters are proposed; the result of this clustering shows that in arrays A12, A13, A14, A21, A23, A24, A31, A32, A41, A42, clustering is performed perfectly, and the relationship between the parameters of the studied area near Larestan salt, diaper, can be useful in notifying the properties of this salt diapir.
