The national seismic networks of Switzerland comprise more than 200 stations. At the station sites, the empirical amplification functions (EAFs) are routinely computed after each earthquake using a generalized inversion method based on separation of source, path, and site effects. The seismic stations are also characterized through geophysical measurements aiming to estimate shear‐wave velocity profiles and horizontal‐to‐vertical spectral ratio of ambient vibrations (HVNR). Using this information, the correlation between the HVNR and EAF is assessed through canonical correlation. Once established, the canonical correlation is used to reconstruct the expected EAFpred at each considered station site in the dataset. The prediction is individually made for all seismic stations in the dataset, excluding every time the investigated station is from the calibration dataset; the reconstruction of the EAFpred is performed resorting to two parallel methods. The first method uses a combination of the canonical correlation parameters and Moran index, and the second one solves in a least‐squares sense an overdetermined linear equation system including the canonical couples deemed as reliable. After a first round of predictions, a systematic lower EAFpred in soft sediment sites and a higher EAFpred in hard‐rock sites is observed. A possible explanation for this behavior is found in the “normalization” to the Swiss standard rock profile in the computation of the EAF at the Swiss stations. Therefore, to reduce this effect, geological and geophysical parameters are considered in addition to the HVNR in the canonical correlation. We observe that the final solution improves when the least‐squares solution approach is used with a combination of HVNR, VS30⁠, and thickness of the ice cover at the last glacial maximum. Moreover, a blind test is performed using data not considered in the calibration dataset. The results highlight the ability of the method to provide an estimate of the site amplification over chosen frequency bins.

中文翻译：

---

基于站点响应代理的典型相关分析预测瑞士特定站点的放大函数

瑞士的国家地震网络包括 200 多个台站。在站点站点，经验放大函数 (EAF) 在每次地震后使用基于震源、路径和站点效应分离的广义反演方法进行例行计算。地震台站还通过地球物理测量来表征，旨在估计横波速度剖面和环境振动的水平与垂直谱比（HVNR）。使用此信息，通过典型相关性评估 HVNR 和 EAF 之间的相关性。一旦建立，典型相关用于重建数据集中每个考虑的站点站点的预期 EAFpred。对数据集中的所有地震台站单独进行预测，排除每次调查站来自校准数据集；EAFpred 的重建是通过两种并行方法进行的。第一种方法使用典型相关参数和 Moran 指数的组合，第二种方法在最小二乘意义上求解超定线性方程组，其中包括被认为可靠的典型对。在第一轮预测后，观察到软沉积地点的系统 EAFpred 较低，而硬岩地点的 EAFpred 较高。对这种行为的可能解释可以在瑞士台站 EAF 计算中对瑞士标准岩石剖面的“归一化”中找到。因此，为了减少这种影响，除了典型相关中的 HVNR 之外，还考虑了地质和地球物理参数。我们观察到，当最小二乘解法与 HVNR、VS30 和最后一次冰期最大值时的冰盖厚度结合使用时，最终解会有所改善。此外，使用校准数据集中未考虑的数据执行盲测。结果突出了该方法在所选频率仓上提供站点放大估计的能力。