强震动记录的低频截止频率自动搜索算法

本文针对传统方法在强震动记录处理中确定滤波低频截止频率效率较低的问题，提出一种确定滤波低频截止频率的自动搜索模型，并利用统计学习方法中的损失函数确定自动搜索模型流程的结束条件。基于2008年汶川和2013年芦山两次地震主震和余震获得的强震动记录，将自动搜索算法得出的结果与传统方法确定的低频截止频率进行比较，分析自动搜索算法产生误差的原因，进而提出自动搜索算法应遵循的原则和适用条件。结果表明该算法计算效率比传统方法有很大提升，特别适合海量强震动记录的批量处理。

Automatic Search Algorithm of Low Cut-off Frequency for Filtering Strong Motion Records

Aiming at the low efficiency of traditional methods to determine the low cut-off frequency for filtering strong-motion records, this paper proposes an automatic search algorithm to determine the cut-off frequency of low-frequency filters and uses the loss function in a statistical learning method to determine the end condition of the automatic search model flow. Based on strong-motion records obtained from the main shock and aftershocks of the 2008 Wenchuan earthquake and 2013 Lushan earthquake, we compare the results of automatic search algorithms and the low cut-off frequency identified by traditional methods, analyze the errors of automatic search algorithms, and propose principles and applicable conditions for their use. The results show that the algorithm improves computational efficiency significantly compared to traditional methods; in particular, the algorithm is suitable for the batch-processing of massive strong-motion records.