Abstract
The firework algorithm (FWA) is a novel swarm intelligence-based method recently proposed for the optimization of multi-parameter, nonlinear functions. Numerical waveform inversion experiments using a synthetic model show that the FWA performs well in both solution quality and efficiency. We apply the FWA in this study to crustal velocity structure inversion using regional seismic waveform data of central Gansu on the northeastern margin of the Qinghai-Tibet plateau. Seismograms recorded from the moment magnitude (MW) 5.4 Minxian earthquake enable obtaining an average crustal velocity model for this region. We initially carried out a series of FWA robustness tests in regional waveform inversion at the same earthquake and station positions across the study region, inverting two velocity structure models, with and without a low-velocity crustal layer; the accuracy of our average inversion results and their standard deviations reveal the advantages of the FWA for the inversion of regional seismic waveforms. We applied the FWA across our study area using three component waveform data recorded by nine broadband permanent seismic stations with epicentral distances ranging between 146 and 437 km. These inversion results show that the average thickness of the crust in this region is 46.75 km, while thicknesses of the sedimentary layer, and the upper, middle, and lower crust are 3.15, 15.69, 13.08, and 14.83 km, respectively. Results also show that the P-wave velocities of these layers and the upper mantle are 4.47, 6.07, 6.12, 6.87, and 8.18 km/s, respectively.
Highlights
Determining the structure of the Earth is one of the main aims of geophysical research
A layered one-dimensional (1D) velocity structure can be used as an initial model for two-dimensional (2D) and three-dimensional (3D) inversions, while regional seismic waveform inversion is an effective tool that can be used to determine the former
Ding et al (2015) applied both particle swarm optimization (PSO) and AR-firework algorithm (FWA) approaches to the inversion of regional waveforms using synthetic models and data; the results of this study show that both PSO and ARFWA are more efficient at avoiding premature in numerical tests, compare to the widely used evolutionary algorithms, including genetic algorithm (GA), niching GA (NGA), and differential evolution
Summary
Determining the structure of the Earth is one of the main aims of geophysical research. As seismic waveform inversion problems are usually highly nonlinear and have non-unique solutions (Maurice et al 2003; Li and Lei 2014a), linearized and gradient-based methods have traditionally been applied because of their computational efficiency. One shortcoming of these methods is that they often rely on the initial models and can converge on local minima; alternative approaches that do not depend on gradient-based searching within the model space can be used instead to improve the probability of convergence on the global minimum. An MW 5.4 earthquake took place near Minxian in Gansu Province on July 22, 2013, and was recorded by densely distributed permanent recording stations across the province These recordings include three seismic waveform components that provide valuable data for investigations of regional crustal velocity structure in central Gansu. We utilize regional seismic waveform inversion to obtain velocity structures within the crust, important data that augments our geophysical understanding of this region
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