Vertical electrical sounding data inversion using continuous ant colony optimization algorithm: A case study from Hassi R'Mel, Algeria

Abstract

Among the existing geophysical methods, the vertical electrical sounding remains a fast and economical way to detect groundwater resources. However, the interpretation of the vertical electrical sounding data often suffers from non‐uniqueness due to the ill‐posed nature of the inverse problem. In recent years, metaheuristic algorithms have been successfully used for solving ill‐conditioned and ill‐posed problems. This work presents a scheme that uses the continuous ant colony optimization (ACOR) technique to invert vertical electrical sounding data. The ACOR is a global search algorithm that explores and finds the globally optimal solution over a search space by mimicking the behaviour of biological ants. The development of this algorithm was due to the requirement to interpret a set of vertical electrical sounding collected at the region of Hassi R'Mel (Algerian Sahara). The area has a particular geological/geoelectrical structure, which renders the interpretation of vertical electrical sounding challenging as standard inversion approaches tend to fail to recover a reliable resistivity model. The ACOR algorithm was initially tested with synthetic data from models simulating the geological/hydrogeological structure of the studied area. The results verified the robustness and stability of the ACOR algorithm even in the presence of a high level of noise. Furthermore, the tests indicated that the ACOR algorithm performed better when compared to other inversion techniques for this particular geoelectrical structure. Five vertical electrical sounding profiles using a Schlumberger array collected in the region of Hassi R'Mel were inverted using the ACOR algorithm. The models confirmed the presence of the two central aquifer systems and showed the geometry of the aquifer with the most favourable conditions for water accumulations.

2D Inversion of Magnetic Anomaly data based on Deep Learning

“…Traditional linear iterative regularization inversion is prone to getting stuck in local minima, and global optimization inversion methods have received widespread attention. Nonlinear methods such as Monte Carlo (MC) [3], simulated annealing (SA) [4], genetic algorithm (GA) [5], artificial neural network (ANN) [6], particle swarm optimization (PSO) [7], differential evolution [8], ant colony optimization (ACO) [9] have been widely used in geophysical inversion.…”