Automatic waveform-based source-location imaging using deep learning extracted microseismic signals

We have used an automatic unsupervised technique to extract waveform signals from continuous microseismic data. First, the time-frequency representation (scalogram) is obtained for the input microseismic trace. Second, the convolutional autoencoder (CAE) is used to extract the significant scalogram features related to the waveform signals and discard the rest. Third, the extracted features from the CAE encoder are considered as the input for the k-means clustering algorithm, in which the input samples are classified into waveform and nonwaveform components. The proposed algorithm is evaluated using several synthetic and field examples. We find that the proposed algorithm successfully extracts the waveform signals even in a noisy environment with a signal-to-noise-ratio as low as -10 dB. We compared the proposed algorithm to benchmark algorithms, for example, simple k-means and short-term and long-term average ratio methods, and find that the proposed algorithm performs best. We find that the detected waveform signals can enhance the resolution of microseismic imaging using a waveform-based reverse time migration method.