Adaptive Filtering for Event Recognition from Noisy Signal: an Application to Earthquake Detection

Seismic event classification and detection have been important research topics because of their significance and wide applications on hazard assessment and global security. In the real world, seismic data acquisition are always impacted by unavoidable nature factors, which will introduce low-frequency noise to the seismic events of interests. Pre-processing of seismic signal using denoising techniques can be critical to the detection of the seismic events. In our work, we develop an end-to-end framework which can automatically learn the hyper-parameter in the denoising algorithm so that we do not need to manually set the hyper-parameter. Specifically, our network structure consists of two modules, an adaptive filtering module for signal denoising, and a classification module for signal classification. We further develop two mechanisms of the adaptive filtering module, namely, sample-specific mechanism and dataset-specific mechanism. We validate the performance of our detection method using a series of field seismic datasets. The classification results show that our framework can not only remove signal noise effectively but also improve the classification accuracy.