The main purpose of the present study is to enhance high-level noisy data by a wavelet-based iterative filtering algorithm for identification of natural frequencies during ambient wind vibrational tests on a petrochemical process tower.Most of denoising methods fail to filter such noise properly.Both the signal-to-noise ratio and the peak signal-to-noise ratio are small.Multiresolution-based one-step and variational-based filtering methods fail to denoise properly with thresholds obtained by theoretical or empirical method.Due to the fact that it is impossible to completely denoise such high-level noisy data,the enhancing approach is used to improve the data quality,which is the main novelty from the application point of view here.For this iterative method,a simple computational approach is proposed to estimate the dynamic threshold values.Hence,different thresholds can be obtained for different recorded signals in one ambient test.This is in contrast to commonly used approaches recommending one global threshold estimated mainly by an empirical method.After the enhancements,modal frequencies are directly detected by the cross wavelet transform (XWT),the spectral power density and autocorrelation of wavelet coefficients.Estimated frequencies are then compared with those of an undamaged-model,simulated by the finite element method.