Abstract
Electromagnetic tomography technology (EMT) is widely used in underground energy exploration. Limited by objective conditions, the collected projection data of electromagnetic waves are sparse and incomplete. Therefore, a study of the tomographic inversion algorithm of EMT based on incomplete projection data has an important guiding significance for the exploitation of underground energy. As a global optimization probability search algorithm, the simple genetic algorithm (SGA) has been widely used in the process of tomographic inversion. However, SGA evolves through a single population, and the values of crossover and mutation probability are always fixed, so there are risks of premature convergence and poor local search ability. To improve the performance of the SGA, a new approach of adaptive multi-population parallel genetic algorithm (AMPGA) with constraints is proposed in this paper. First, the AMPGA makes full use of multi-group adaptive co-evolution to improve the local and global search ability of SGA and restrain the risk of premature convergence. Then, the introduction of prior information as a constraint makes the results clearer and more accurate. The proposed algorithm has been verified in a numerical experiment and field tests, and the results show that the proposed algorithm can well balance global and local search capabilities, which offers a more realistic and stable tomographic result.
Highlights
With continuous energy shortages, accurate exploration of underground energy has become the mainstream of international research [1]
To make full use of the global evolutionary characteristics of simple genetic algorithm (SGA) and restrain its shortcomings of premature convergence and poor local search ability in the inversion process of Electromagnetic tomography (EMT), an adaptive multi-population parallel genetic algorithm (AMPGA) with constraints is proposed in this paper
The area where the fault is not revealed in the roadway was set as the constraint for the AMPGA, and the value of the the fault is not revealed in the roadway was set as the constraint for the AMPGA, and the value of absorption coefficient was equal to the average of the entire area
Summary
Accurate exploration of underground energy has become the mainstream of international research [1]. EMT has been widely used in the exploration of coal, oil, natural gas, and other mineral resources [6,7,8]. In the process of exploration, the ray coverage angle in the observation system is limited by field detection conditions, and the electromagnetic wave projection data obtained are always incomplete. The simple genetic algorithm (SGA) is a highly parallel, random, and adaptive global optimization probability search algorithm developed from natural selection and evolutionary mechanisms in the biological world [9]. Since it does not depend on gradients in optimization, it has
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