Abstract
The detection of seismic signals is vital in seismic data processing and analysis. Many algorithms have been proposed to resolve this issue, such as the ratio of short-term and long-term power averages (STA/LTA), F detector, Generalize F, and etc. However, the detection performance will be affected by the noise signals severely. In this paper, we propose a novel seismic signal detection method based on the historical waveform features to improve the seismic signals detection performance and reduce the affection from the noise signals. We use the historical events location information in a specific area and waveform features information to build the joint probability model. For the new signal from this area, we can determine whether it is the seismic signal according to the value of the joint probability. The waveform features used to construct the model include the average spectral energy on a specific frequency band, the energy of the component obtained by decomposing the signal through empirical mode decomposition (EMD), and the peak and the ratio of STA/LTA trace. We use the Gaussian process (GP) to build each feature model and finally get a multi-features joint probability model. The historical events location information is used as the kernel of the GP, and the historical waveform features are used to train the hyperparameters of GP. The beamforming data of the seismic array KSRS of International Monitoring System are used to train and test the model. The testing results show the effectiveness of the proposed method.
Highlights
Seismic arrays have been widely used in the area of seismic monitoring and nuclear test monitoring
We propose a novel method based on location information and waveform features to detect the seismic signal
Compared with the short-term and long-term power averages (STA/LTA), the method proposed in this paper uses the historical seismic events information in a specific area to achieve the seismic signal detection
Summary
Seismic arrays have been widely used in the area of seismic monitoring and nuclear test monitoring. The waveform features are the spectral energy average about the specific frequency band of the signal, the energy of the signal component decomposing by empirical mode decomposition, the peak and the ratio of STA/LTA trace. We choose some helpful features in the seismic signal detection, such as the peak and ratio of the STA/LTA trace, the average value of energy in a specific frequency band, and the energy value after empirical mode decomposition (EMD), which are independent of each other and introduced in detail later. The Gaussian process with event location information in a specific region as a kernel function is used to build the signal features model. The implementation of seismic signal detection method proposed in this paper is elaborated, including data preparation, feature extraction, model training, and etc.
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