Discriminating landslides from other events in seismic records is challenging due to unclear phases and overlapped frequency content. We analyze the seismic waveform power spectral density (PSD) and its skewness to discriminate landslides from earthquakes and background noise. By comparing PSDs of landslides with small-magnitude earthquakes and noise in the Alaskan region, we find distinct power decay trends in the 0.01–5 Hz frequency range. The method was successfully tested on the seismic waveforms of seven global landslides. Further, the statistical significance of the approach was tested on 835 landslide waveforms using probability density, skewness and crosscorrelation of waveform PSD. This novel integration of seismic waveform PSDs and their skewness analysis is found to be robust and statistically significant for automatic landslide detection in continuous seismic data, with vast potential for early warning through real-time seismic networks.

中文翻译：

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使用功率谱密度分析区分连续地震数据中的滑坡波形


由于阶段不清楚和频率内容重叠，区分滑坡与地震记录中的其他事件具有挑战性。我们分析了地震波形功率谱密度 （PSD） 及其偏度，以区分滑坡与地震和背景噪声。通过比较阿拉斯加地区小震级地震和噪声的滑坡 PSD，我们发现在 0.01-5 Hz 频率范围内有明显的功率衰减趋势。该方法在 7 次全球滑坡的地震波形上成功进行了测试。此外，使用波形 PSD 的概率密度、偏度和互相关在 835 个滑坡波形上测试了该方法的统计显着性。地震波形 PSD 及其偏度分析的这种新颖集成被发现对于连续地震数据中的自动滑坡检测具有稳健性和统计学意义，具有通过实时地震网络进行早期预警的巨大潜力。