2018龙目岛地震余震识别滤波算法的实现

A. Ardianto, Y. Husni, A. Nugraha, M. Muzli, Z. Zulfakriza, H. Afif, D. Sahara, S. Widiyantoro, A. Priyono, N. Puspito, P. Supendi, A. Riyanto, S. Wei, B. S. Prabowo
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引用次数: 1

摘要

随着分析数据量的增加,越来越需要识别一致、高效和准确的地震事件的能力。本文实现了一种滤波选择算法,用于自动识别余震事件和确定到达时间,特别是对P波相位。这里在确定到达时间的不确定性方面做了修改,并且在确定所用的到达时间方面有额外的标准。附加条件是,在一定的时间跨度内,至少有5个站点由过滤器选择器到达的时间确定。这样做是为了尽量减少由于局部噪声和其他实际原因造成的识别误差,即最小台站数量以确定位置和其他地震分析。为了测试过滤器选择算法,龙目岛地震的余震数据发生在2018年7月29日(6.4级),8月5日(7级)和8月19日(6.3级和6.9级)。2018年8月4日至2018年9月4日,龙目岛当地地震台站使用了30天的余震数据。通过比较探测到的地震事件数量以及自动确定P波到达时间与人工确定P波到达时间的准确性,对滤波选择算法的结果进行了评价。此外,还将较短周期的宽频带地震仪的测量结果进行了比较,以了解工具类型对其性能结果的影响程度。与人工到达时间的比较结果表明,85%以上的自动到达时间的结果误差在0.2秒以下。因此,可以说该滤波选择算法对于识别事件和确定P波到达时间是非常有效的。本文还表明,该算法可用于宽频带短周期地震仪传感器,即使不需要事先对仪器进行校正。
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Implementation of Filter Picker Algorithm For Aftershock Identification of Lombok Earthquake 2018
The ability to identify earthquake events that are consistent, efficient and accurate is increasingly needed along with the increase in the amount of data analyzed. In this paper a filter picker algorithm is implemented to identify aftershock  events and determination of arrival time automatically, especially for the P wave phase. Here modifications are made in determining the uncertainty of arrival time and there are additional criteria in determining the time of arrival used. The additional criteria are that in a certain time span, there are at least 5 stations determined by the time the filter picker arrives. This is done to minimize identification errors due to local noise and other practical reasons, namely the minimum number of stations to determine the location and other seismological analysis. To test the filter picker algorithm, aftershock data from the Lombok earthquake occurred on July 29 (M 6.4), August 5 (M 7), and August 19 (M 6.3 and M 6.9)  2018. The aftershock data were used for 30 days, from August 4, 2018 to September 4, 2018 using local seismic station in Lombok Island. The results of the filter picker algorithm were evaluated by comparing the number of earthquake events detected and the accuracy of determining the P wave arrival time automatically to the results of manually arriving time. In addition, a comparison of the results obtained from a broadband type seismometer with a short period is used to find out how much influence the type of tool has on its performance results. The results of the comparison with the manual arrival time show that more than 85 percent of the results of the automatic arrival time have a difference below 0.2 seconds. Therefore, it can be said that the filter picker algorithm is quite effective for identifying events and determining the arrival time of P waves. In this paper it is also shown that this algorithm can be used for broad band and short period seismometer sensor, even without the prior correction of instruments.
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