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.

柴达木盆地是青藏高原东北部大型断陷山间盆地，该地区的流动观测记录了2008年11月10日发生于大柴旦附近的Mw6.3级地震。和附近的基岩上的记录相比，盆地内部的记录显示出非常显著的地面运动放大效应，表现为峰值速度的增大，持续时间的延长，盆地内部呈现出长持续时间的后续震相。傅立叶频谱分析表明盆地内部显著的后续震相的频率和直达波相比较低，地面质点运动轨迹图显示后续震相为面波运动特征。为了解释地面运动的差异，构建二维模型，通过交错网格高阶有限差分方法计算了地震波在盆地内部的传播过程，结果显示，盆地内部低速层的存在造成直达波的放大以及多次反射与转换，盆地边缘结构造成的波的相干叠加产生了强烈的次生面波，其低频、大振幅、长持续时间的特征是盆地内部地面运动放大的主要原因。

Inversion is a critical and challenging task in geophysical research. Geophysical inversion can be formulated as an optimization problem to find the best parameters whose forward synthesis data most fit the observed data. The inverse problems are usually highly non-linear, multi-modal as well as ill-posed, so conventional optimization algorithms cannot handle it very efficiently. In the past decades, genetic algorithm (GA) and its many variants are widely applied to inverse problems and achieve great success. Swarm intelligence algorithms are a family of global optimizers inspired by swarm phenomena in nature, and have shown better performance than GA for diverse optimization problems. However, swarm intelligence algorithms are not utilized for geophysical inversion problems until recently and only limited number of works are reported. In this paper, we try to apply two swarm intelligence algorithms, Particle Swarm Optimization (PSO) and Fireworks Algorithm (FWA), to the regional seismic waveform inversion. To explore the advantages and disadvantages of swarm intelligence algorithms over GA, synthetic experiments are conducted by using these two swarm intelligence algorithm and several GA variants as well as Differential Evolution (DE). The experimental results show that, both swarm intelligence algorithms outperform the widely used GA, DE, and the models estimated by swarm intelligence algorithms are closer to the true solution. The promising results imply that swarm intelligence algorithms are a potentially more powerful tool for inversion problems.

A 2-D lateral heterogeneous model was constructed to simulate basin-edge effects using PSM/FDM method. Effects of basin-edge geometry and source depth were simulated. PGV of different models are given to illustrate the effects, and it suggests that the basin geometry and the depth of soft sediment play crucial roles in seismic ground motion study for sedimentary basin.