We present a reliable first‐arrival picking method using wavelet multilevel analysis (WMA) and polarization analysis for high‐noise three‐component (3C) microseismic data. The proposed method is based on the autoregressive (AR) model using the Akaike information criterion (AIC) algorithm, named PWT‐AIC (in which PWT is polarization wavelet transform). This study aims to address the problem that the AR‐AIC picker selects the global least point as the first arrival; it will cause a questionable arrival identification while being applied to the 3C microseismic recordings with a low signal‐to‐noise ratio. Initially, we employ the WMA to extract the dominant signal for high‐noise 3C microseismic data; we then use the reconstructed approximation data to perform the AR‐AIC calculation to identify the first arrival. Furthermore, we adapt the polarization information of 3C microseismic data to determine the proper calculation section for the AR‐AIC algorithm. We conduct a polarization analysis by adopting the eigenanalysis of the sliding covariance matrix. Compared with the popular short‐term average/long‐term average ratio and existing AR‐AIC pickers, the presented algorithm can significantly reduce the picking error. A test using synthetic 3C seismic data with high‐noise indicates that the onset time can be accurately identified, and the improved method has an error of between and sample intervals. Results using field microseismic recordings also confirmed that the proposed strategy can improve the accuracy of arrival‐time estimation for noisy 3C microseismic datasets. In addition, we perform a time consumption comparison of the different approaches. Although the proposed picker requires more computation time than the other pickers, the results are acceptable, given the capabilities of modern computers.