Localizing the microseismic event plays a key role in microseismic monitoring. However, microseismic data usually suffer from a low signal-to-noise ratio (S/N), which could affect the resolution of the microseismic source location. We have developed an unsupervised deep learning approach based on variational autoencoder (VAE) and squeeze-and-excitation (SE) networks for enhancing microseismic signals, as well as suppressing noise. First, the microseismic data are divided into several overlapped patches. Second, the VAE encodes the data, extracting the significant features related to the useful signals. Finally, the extracted latent features are decoded to uncover the useful signals and discard the others. The SE network is used to guide the VAE to preserve the useful information related to the clean signal by scaling the extracted features from the encoder part and concatenating them with the features of the decoder part. Our algorithm is evaluated using several synthetic and field examples. As a result, a robust denoising performance is shown despite the existence of a high level of random and coherent noise, for example, with an S/N as low as −32.45 dB. Then, the denoised signal can be used as input data to image the source location using a reverse time migration method, leading to better location accuracy. Our algorithm performs the best when compared to benchmark methods such as f-x deconvolution and the damped multichannel singular spectrum analysis methods.