浅地表低速异常体(如空洞)的精确探测一直是地球物理领域具有重要价值与挑战性的研究课题，对城市灾害评估与复杂条件下煤田地震勘探的浅地表精细建模具有重要意义。面波全波形反演适合浅地表高精度建模，然而在实际应用中仍存在诸多亟待解决的难题。针对面波全波形反演方法中多参数串扰、实际数据预处理、震源子波估计等关键问题，开发了一套完整的瑞雷波多参数全波形反演方法流程，以实现对浅地表空洞的精确探测。该方法中模型的横波速度、纵波速度和密度随反演进程均被同步更新，减弱后两个参数偏离真实值对横波速度反演精度的消极影响。采用伴随状态法构建的拟海森算子对梯度进行预处理以压制地表伪影、增强波场照明，提高对小尺度异常的表征能力。通过褶积因子消除波场正演与实际数据采集的维数差，实现3D波场到2D波场的转换。采用校正滤波方法估计震源子波，并在迭代进程中进行动态估计，以减弱特定参数模型不准确的影响。同时，该方法采用多尺度反演策略，减轻由低速异常引起的目标函数非凸性，提高反演稳定性。合成数据和实际案例测试结果表明，瑞雷波多参数全波形反演方法得到的横波、纵波速度模型、密度模型具有基本一致性，其中横波速度模型准确度最高。实测数据反演的横波速度模型显示了一个4 m×3 m的人工空洞，与实际位置和尺寸相符，证明了该方法在浅地表空洞探测方面的可行性和有效性。

The accurate detection of near-surface low-velocity anomalies (such as cavities), which has always been a valuable and challenging research topic in the field of geophysics, holds great significance for the fine-scale near-surface modeling in urban disaster assessment and the seismic exploration of coalfields under complex conditions. The full-waveform inversion (FWI) of surface waves is suitable for high-precision near-surface modeling. However, there still exist many urgent problems with the modeling in practical applications. To solve the key issues of the FWI of surface waves, such as multi-parameter crosstalk, actual data preprocessing, and source wavelet estimation, this study developed a complete method of multi-parameter FWI of Rayleigh waves to achieve the accurate detection of near-surface cavities. In this method, (1) the S- and P-wave velocities and density of the models were updated synchronously in the process of inversion, thus reducing the negative effects of the deviations of the P-wave velocity and density from their actual values on the accuracy of inverted S-wave velocities; (2) the quasi-Hessian operator constructed using the adjoint state method was employed to conduct gradient preprocessing in order to suppress surface artifacts, enhance wavefield illumination, and improve the characterization ability for small-scale anomalies; (3) to transform the 3D wave field into a 2D wave field, the convolutional factor was used to eliminate the dimension difference between the wavefield forward modeling and the actual data acquisition; (4) to reduce the influence of specific inaccurate parameter models, the corrected filtering method was used to dynamically estimate the source wavelets during the iterative process; (5) to improve the stability of the inversion, a multi-scale inversion strategy was adopted to alleviate the non-convexity of the objective function caused by low-velocity anomalies. The synthetic data and the test results of actual cases show that the models of the S- and P-wave velocities and the density developed using the multi-parameter FWI method of Rayleigh waves were roughly consistent, with the S-wave velocity model being the most accurate. The S-wave velocity model obtained through the inversion of measured data revealed a 4 m × 3 m artificial cavity, whose location and size are consistent with the actual situation. This result demonstrates the method proposed in this study features feasibility and effectiveness in the detection of near-surface cavities.