Improved sparrow search algorithm for inversion of geometric parameters of earthquake source faults

With the continuous improvement of the accuracy of geodetic deformation data, the inversion of seismic source parameters puts forward a higher demand for nonlinear inversion algorithms. In this research, an improved Sparrow Search Algorithm (SSA) is proposed for the seismic source parameter inversion problem. By replacing the original population generation in the improved algorithm with Latin hypercubic sampling, the Sparrow Search Algorithm reduces the repetition of samples in the population initialization. Subsequently, the algorithm introduces adaptive weights in the discoverer generation phase of the sparrow algorithm and combines the Levy flight strategy to make the algorithm more comprehensive and improve the search accuracy during the whole iteration process. Therefore, the improved Latin hypercube-based sparrow search algorithm (ILHSSA) has better advantages in terms of iterative convergence speed and stability. In order to verify the performance of ILHSSA, the basic genetic algorithm (GA) and sparrow search algorithm (SSA) are examined and compared with ILHSSA by simulated earthquakes of two different earthquake types. The simulation experiments show that the improved algorithm ILHSSA outperforms SSA in accuracy and stability. Compared with the GA algorithm, ILHSSA can achieve the same inversion accuracy as GA, and it even surpasses GA in inversion speed and the inversion results of some parameters, demonstrating better stability. Finally, the improved algorithm is used for the 2017 Bodrum-Cos earthquake and the 2016 Amatrice earthquake in Italy. The inversion results all reflect the practicality and reliability of the improved algorithm.