Study of techniques of identifying the earthquake precursory anomalies in terms of mathematical mode

• 作 者: 严尊国 钱家栋 陈俊华 李胜乐
• 出 处: Acta Seismologica Sinica
• 发表日期: 2000年06期
• 关键词: identifying audio Figure representing stationary otherwise anomalous holds actua

This paper deals mainly with the key technique of identifying the anomalous signals without distortion, which might be the precursors associated with earthquakes, from the real time series of observations that would be usually the mixture of the anomalous signals, the normal background variations, some interference and noises. The key technique of (un-biased estimation( is to construct an empirical time series and set up the criterion for identifying the anomalous variation on the bases of time series analysis. To the end of testing the method, a man-made time series including the normal variations and random interference as well as specific anomaly, has been constructed. And the test of picking up the anomaly has been conducted with the intuitive and effective way of identifying the anomalous signal from a complicated time series. Test results confirms that the techniques under discussion are effective and applicable, and the signals extracted from the analysis, could be clear and precise, and is almost similar to the known simulated anomalous signals in the experiments.

This paper deals mainly with the key technique of identifying the anomalous signals without distortion, which might be the precursors associated with earthquakes, from the real time series of observations that would be usually the mixture of the anomalous signals, the normal background variations, some interference and noises. The key technique of (un-biased estimation (is to construct an empirical time series and set up the criterion for identifying the anomalous variation on the bases of time series analysis. To the end of testing the method, a man-made time series including the normal variations and random interference as well as specific anomaly, has been constructed. And the test of picking up the anomaly has been conducted with the intuitive and effective way of identifying the anomalous signal from a complicated time series. Test results confirms that the techniques under discussion are effective and applicable, and the signals extracted from the analysis, could be clear and pre cise, and is almost similar to the known simulated anomalous signals in the experiments.