| Summary: | Low-amplitude signal detection is a key procedure in borehole microseismic and desert seismic exploration. Usually, signals are difficult to detect due to their low amplitude and noise contamination. To solve this problem, we propose a method combining shearlet energy entropy with a support vector machine (SVM) to detect low-amplitude signals. In the proposed method, the signal feature is extracted using shearlet energy entropy. The signal is more sparsely represented in the shearlet domain because of the multi-scale and multi-direction characteristic of the shearlet transform, which favours signal feature extraction. Furthermore, in calculating shearlet energy entropy, we use the correlation of shearlet coefficients to enhance the difference between signal and noise in the shearlet domain. Shearlet energy entropy makes the SVM achieve a more accurate classification result compared with other traditional features such as amplitude and energy. The results of synthetic and field data show that our method is more effective than the STA/LTA and the convolutional neural network for low-amplitude microseismic signal and desert seismic signal detection.
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