applied sciences Article Microseismic Signal Denoising and Separation Based on Fully Convolutional Encoder–Decoder Network Hang Zhang 1,2,3 , Chunchi Ma 1,2, * , Veronica Pazzi 3 , Yulin Zou 4 and Nicola Casagli 3 1 State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China; zhanghang@stu.cdut.edu.cn 2 College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China 3 Department of Earth Sciences, University of Florence, 50125 Florence, Italy; veronica.pazzi@unifi.it (V.P.); nicola.casagli@unifi.it (N.C.) 4 Sichuan Yanjiang Panzhihua-Ningnan Highway Co., LDT, Xichang 615000, China; zouyulin000@gmail.com * Correspondence: machunchi17@cdut.edu.cn Received: 21 July 2020; Accepted: 21 September 2020; Published: 22 September 2020   Abstract: Denoising methods are a highly desired component of signal processing, and they can separate the signal of interest from noise to improve the subsequent signal analyses. In this paper, an advanced denoising method based on a fully convolutional encoder–decoder neural network is proposed. The method simultaneously learns the sparse features in the time–frequency domain, and the mask-related mapping function for signal separation. The results show that the proposed method has an impressive performance on denoising microseismic signals containing various types and intensities of noise. Furthermore, the method works well even when a similar frequency band is shared between the microseismic signals and the noises. The proposed method, compared to the existing methods, significantly improves the signal–noise ratio thanks to minor changes of the microseismic signal (less distortion in the waveform). Additionally, the proposed methods preserve the shape and amplitude characteristics so that it allows better recovery of the real waveform. This method is exceedingly useful for the automatic processing of the microseismic signal. Further, it has excellent potential to be extended to the study of exploration seismology and earthquakes. Keywords: microseismic monitoring; deep learning; microseismic signal analysis; time–frequency domain; convolutional neural network 1. Introduction During monitoring and data acquisition processes, microseismic signals are often corrupted by various types of noise due to the uncontrollable sources, conditions, and complicated environmental situations. Possible noise could be electrical, construction, mechanical, or traffic noises. Spectral filtering is commonly used for improving the signal-to-noise ratio (SNR) of the microseismic signal. However, it is ineffective to suppress the noise that has a similar frequency band with a microseismic signal. Moreover, it can distort the signal [1] and/or generate artifacts before impulsive arrivals [2]. In order to alleviate this limitation, many methods have been proposed to suppress the noise in seismic/microseismic data, including Short Time Fourier Transform (STFT) [3], the Continuous Wavelet Transform (CWT) [4,5], S-transform [6], the Radon Transform [7–9], the Wave-Packet Transform (WPT) [10,11], Empirical Mode Decomposition (EMD) [12–14], Fuzzy methods [15], singular spectrum analysis [16], sparse transform-based denoising [17], mathematical morphology-based denoising approach [18], and the non-local means (NLM) algorithm [19]. Further, some hybrid methods were proposed, which combine the advantages of two or more denoising methods [20]. Signal denoising performance can be improved through two ways: a more effective sparse representation of the data Appl. Sci. 2020, 10, 6621; doi:10.3390/app10186621 www.mdpi.com/journal/applsci