An Efficient Approach of Data Adaptive Polarization Filter to Extract Teleseismic Phases from the Ocean-Bottom Seismograms

Abstract

Abstract The microseism is the strongest component of background seismic noise that masks seismic signals recorded by ocean-bottom seismographs (OBSs). Such undesired noise hampers the identification of critical seismic phases and sometimes even the entire waveform. Here, we introduce the data adaptive polarization filter (DAPF), an approach that suppresses random signals from the background seismic-noise significantly to overcome such difficulties. To automate this task, we have developed a self-contained software suite—DAPF-v1, supported by a MATLAB graphical user interface. The polarization filter is constructed from the data spectral density matrices of seismogram segments employing multitaper spectral analysis approach. We demonstrate a successful application of this technique to the OBSs deployed in the Indian Ocean. Our results confirm substantially enhanced signal-to-noise ratio after application of DAPF. The application of this technique has extensive implications for seismological studies particularly those aimed at understanding deep mantle dynamics, in which phase identification and qualitative waveform recovery are crucial yet challenging.