With monitoring of the acoustic emission phenomenon caused by rock deformation and failure, microseismic monitoring has been widely used in the development of unconventional oil and gas fields. Due to the complex environment and diversity types of the noise, the signal energy of surface microseismic monitoring is weak and the signal-to-noise ratio (S/N) of raw data is very low. In the process of data processing, many human resources are needed to discriminate the first-break picking because of the low S/N, and this directly affects the error of microseismic event location. We have adopted the regularization to Stein unbiased risk estimation (R-SURE) algorithm based on the continuous wavelet transform to separate the signal from the noise in different decomposition levels. The regularization factor is the adaptive change of the different geology and fracturing engineering, which is related to shale brittleness, fracturing pressure, and displacement. As a result, the threshold from the R-SURE algorithm is multiresolution in different levels, and the S/N could be improved effectively. In addition, we established the threshold discriminant for picking up the first-break wave of low-S/N data combined with the Akaike information criterion and characteristic function, which compared the maximum absolute value in the time window. The method has good robustness and low computational complexity. The first arrival is automatically and accurately judged, which improves the accuracy of the event location. We successfully applied these methods to the surface microseismic monitoring of shale gas fracturing in several wells in southwest China. The S/N of the raw data has been improved, the effective stimulated reservoir volume and the performance of the gas production are predicted with the results, which provides important technical support for shale gas development in the area.