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.
The high-stress hazards of underground engineering have stimulated the exploration of microseismic monitoring and early warning methods. To achieve a good monitoring effect, the monitoring object is usually enclosed by a microseismic array (sensor array) (e.g., slope engineering, etc.). However, some characteristics of a buried tunnel, including “linear”, “deep-buried”, and “long”, make it difficult to deploy a reasonable microseismic array, which leads to the microseismic array being non-enclosed for the monitoring object. Application of the non-enclosed microseismic array yields decreases the accuracy of the source location. To solve the problem wisely, this paper deals with the feasibility of non-enclosed microseismic arrays (axial-extended, lateral-extended, and twin-tube arrays) by introducing a quantitative method. To this end, an optimized microseismic array with the best source location accuracy for a twin-tube expressway tunnel is proposed. The obtained results reveal that the non-enclosed microseismic arrays, which are unavoidable in expressway tunnel engineering, do not introduce errors but reduce the ability to resist them. Further, the twin-tube array achieves a better source location accuracy than the axial and lateral-extended arrays. In the application of the source location based on the particle swarm optimization (PSO) algorithm to the twin-tube array, microseismic events, which cluster in the rockburst section, are wholly gathered, and the maximum error is reduced by about 30–50 m, indicating its greater feasibility with respect to the single-tube array.
Summary Denoising and onset time picking of signals are essential before extracting source information from collected seismic/microseismic data. We proposed an advanced deep dual-tasking network (DDTN) that integrates these two procedures sequentially to achieve the optimal performance. Two homo-structured encoder-decoder networks with specially designed structures and parameters are connected in series for handling the denoising and detection of microseismic signals. Based on the similarity of data types, the output of the denoising network will be imported into the detection network to obtain labels for the signal duration. The procedures of denoising and duration detection can be completed in an integrated way, where the denoised signals can improve the accuracy of onset time picking. Results show that the method has a good performance for the denoising of microseismic signals that contain various types and intensities of noise. Compared with existing methods, DDTN removes the noise with a minor waveform distortion. It is ideal for recovering the microseismic signal while maintaining a good capacity for onset time picking when the signal-to-noise ratio is low. Based on that, the second network can detect a more accurate duration of microseismic signals and thus obtain more accurate onset time. The method has great potential to be extended to the study of exploration seismology and earthquakes.
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