Estimation of Deep-Seated Faults Parameters from Gravity Data Using the Cuckoo Search Algorithm


Pure and Applied Geophysics, vol.180, no.12, pp.4147-4173, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 180 Issue: 12
  • Publication Date: 2023
  • Doi Number: 10.1007/s00024-023-03368-x
  • Journal Name: Pure and Applied Geophysics
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), Compendex, Geobase, INSPEC
  • Page Numbers: pp.4147-4173
  • Keywords: Cuckoo Search Algorithm, Deep-seated faults, Gravity inversion, Parameter tuning
  • Dokuz Eylül University Affiliated: Yes


Cuckoo Search Algorithm (CSA) is a nature-inspired metaheuristic optimization algorithm. The optimization algorithm was inspired by the life story of a family of birds called the Cuckoo. In this paper, an application of the CSA, is implemented to estimate model parameters of deep-seated faults by using gravity anomalies. To increase the efficiency of the algorithm, parameter tuning studies are carried out for the optimum algorithm-based control parameters. In addition, the reliability and possible uncertainties of the obtained solutions were examined using probability density function analysis. The model parameters (z 1, z 2, α, x 0 and ρ) for noisy and noise-free synthetic models were obtained by using CSA. These obtained parameters are very compatible with the true model parameters for noise-free data, similarly well-nigh compatible results for noisy data. As a result we can say that the method works successfully when applied to synthetic data. The technique is tested on three widely studied benchmark field data including gravity anomaly [Garber Fault (USA), Gazal Fault (Egypt), Mersa Matruh Fault (Egypt)]. In previous studies, various solution techniques have been used for the inversion of these field data and the compatibility of the model parameters obtained in this study with the values obtained from previous studies was compared. The results demonstrate that the CSA is a very effective and robust approach for the optimization of gravity anomalies.