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BormannPeter, SaulJoachim, . (2008). The New IASPEI Standard Broadband Magnitude m B
. Seismological Research Letters, 79(5), 698–705.
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Bormann Peter, Saul Joachim, . (2009). A Fast, Non-saturating Magnitude Estimator for Great Earthquakes
. 0895-0695, 80(5), 808–816.
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McNamara D E, Hutt C R, Gee L S, Benz H M, Buland R P, . (2009). A Method to Establish Seismic Noise Baselines for Automated Station Assessment
. Seismological Research Letters, 80(4), 628–637.
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Ammon Charles J, Lay Thorne, Simpson David W, . (2010). Great Earthquakes and Global Seismic Networks
. Seismological Research Letters, 81(6), 965–971.
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Francisco Bravo, Pablo Koch, Sebastian Riquelme, Mauricio Fuentes, Jaime Campos. (2019). (Vol. 90).
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. (2020). Using Component Ratios to Detect Metadata and Instrument Problems of Seismic Stations: Examples from 18 Yr of GEOSCOPE Data (Vol. 91).
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Andrea Chiang, Gene A. Ichinose, Doug S. Dreger, Sean R. Ford, Eric M. Matzel, Steve C. Myers, W. R. Walter. (2018). (Vol. 89). Bachelor's thesis, , .
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Robert E. Anthony, Adam T. Ringler, Michael DuVernois, Kent R. Anderson, David C. Wilson. (2021). Six Decades of Seismology at South Pole, Antarctica: Current Limitations and Future Opportunities to Facilitate New Geophysical Observations (Vol. 92).
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James K. Kleckner, Kyle B. Withers, Eric M. Thompson, John M. Rekoske, Emily Wolin, Morgan P. Moschetti. (2022). Automated Detection of Clipping in Broadband Earthquake Records (Vol. 93).
Abstract: Because the amount of available ground?motion data has increased over the last decades, the need for automated processing algorithms has also increased. One difficulty with automated processing is to screen clipped records. Clipping occurs when the ground?motion amplitude exceeds the dynamic range of the linear response of the instrument. Clipped records in which the amplitude exceeds the dynamic range are relatively easy to identify visually yet challenging for automated algorithms. In this article, we seek to identify a reliable and fully automated clipping detection algorithm tailored to near?real?time earthquake response needs. We consider multiple alternative algorithms, including (1) an algorithm based on the percentage difference in adjacent data points, (2) the standard deviation of the data within a moving window, (3) the shape of the histogram of the recorded amplitudes, (4) the second derivative of the data, and (5) the amplitude of the data. To quantitatively compare these algorithms, we construct development and holdout datasets from earthquakes across a range of geographic regions, tectonic environments, and instrument types. We manually classify each record for the presence of clipping and use the classified records. We then develop an artificial neural network model that combines all the individual algorithms. Testing on the holdout dataset, the standard deviation and histogram approaches are the most accurate individual algorithms, with an overall accuracy of about 93%. The combined artificial neural network method yields an overall accuracy of 95%, and the choice of classification threshold can balance precision and recall.
Programme: 133
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. (2021). Preface to the Focus Section on European Seismic Networks and Associated Services and Products (Vol. 92).
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