Cuckoo Search Algorithm for model parameter estimation from self-potential data

Abstract

The use of the Cuckoo Search Algorithm (CSA) is introduced for estimating the model parameters from both synthetic and field self-potential (SP) anomalies in this study. The CSA offers a simple and efficient way to optimization studies as a popular nature-inspired metaheuristic algorithm because it requires only two algorithm-based parameters (the number of population and probability of recognition of the egg), and instead of isotropic random walks, it employs Lévy flights for search. This work presents the first application of the CSA for inversion of SP anomalies in geophysics. The test studies with the synthetic data included the cases of the noise-free and noisy data. Additionally, the Bavarian (Germany), Süleymanköy (Turkey) and Malachite (Colorado, USA) anomalies, which are the well-studied benchmark field data sets in the literature, were tested with the field data. An uncertainty appraisal analysis was performed for each model parameter via the Metropolis-Hastings algorithm. Also, the estimated parameters from the CSA were compared with the previous studies used a variety of metaheuristic methods to invert the same field data sets. According to the results, the CSA can be integrated into the inversion of SP anomalies as an efficient alternative method.