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Xiong Zhenghui, Li Xiaojun, Dai Zhijun, Chen Su. 2019: A method for identifying the baseline drift of strong-motion records based on L1-norm regularization. Acta Seismologica Sinica, 41(1): 111-123. DOI: 10.11939/jass.20180072
Citation: Xiong Zhenghui, Li Xiaojun, Dai Zhijun, Chen Su. 2019: A method for identifying the baseline drift of strong-motion records based on L1-norm regularization. Acta Seismologica Sinica, 41(1): 111-123. DOI: 10.11939/jass.20180072
shu

A method for identifying the baseline drift of strong-motion records based on L1-norm regularization

  • 1.

    Institute of Geophysics，China Earthquake Administration，Beijing 100081，China

  • 2.

    China Earthquake Risk and Insurance Laboratory，Beijing 100081，China

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  • Received Date: May 30, 2018
  • Revised Date: June 24, 2018
  • Available Online: January 16, 2019
  • Published Date: December 31, 2018
    Graphical Abstract
    • Abstract
  • To identify the accurate baseline drift in ground acceleration, velocity, and displacement time series is one of the basic and challenge problems in the research of strong ground motion. This study proposes a new baseline-correction method based on L1-norm regularization. It aims at minimizing the error of fitting velocity trace subject to let the sum of absolute values of acceleration baseline drift be small. As the baseline-offset is figured out by the convexity-optimized tool automatically in this L1-norm regularization based baseline-correction method, the subjective interferences can be well avoided such as selecting segmentation times and the start and end moments. And then representative noise models of acceleration baseline offset are added respectively to typical strong-motion records in order to test and verify the new method. The results shows that our method is universal for identifying and processing single-, double-, and multi-stage baseline drift noises. It can sensitively capture the trend （slope） change of the velocity trace while it’s no need to set segmentation times and positions of piecewise linear fitting in advance. The pre-event interval of strong-motion record has a great influence on the processing results of this method. If the pre-event interval is long enough （e.g. 20 seconds） in a record, the identification of the baseline drift noise will be much more accurate, and the recovered displacement trace will match better with the real one. Additionally, this method shows good performance to recover peak ground velocity and the whole velocity time series even if the record almost has no pre-event portion.
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    • References (45)
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