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Framework for Automated Earthquake Event Detection Based on Denoising by Adaptive Filter.
- Source :
- IEEE Transactions on Circuits & Systems. Part I: Regular Papers; Sep2020, Vol. 67 Issue 9, p3070-3083, 14p
- Publication Year :
- 2020
-
Abstract
- Automated detection of the P-wave arrival for real-time earthquake-early-warning-system employing conventional short-time-average/long-time-average (STA/LTA) algorithm is prone to suffer from erroneous detection due to high background noise. This paper introduces an enhanced variable step-size least mean square (EVSSLMS) algorithm for considerably improving the P-wave detection. The proposed event detection scheme employs the EVSSLMS algorithm for de-noising the seismic data followed by conventional STA/LTA algorithm to detect arrival of the P-wave. The EVSSLMS algorithm outperforms the existing trigonometric least mean square (TLMS), variable step-size LMS (VSSLMS), least mean logarithmic square (LMLS) and variable $\alpha $ -LMLS algorithms in terms of convergence speed and steady-state error and achieves a significant enhancement in detection accuracy by 42%, 33%, 25% and 23% at low signal-to-noise ratio (SNR) as compared to the existing TLMS, VSSLMS, LMLS, and variable- $\alpha $ LMLS algorithms, respectively. Moreover, a high-speed and low-complexity very-large-scale-integrated (VLSI) architecture has been implemented on both field-programmable-gate array (FPGA) and application-specific-integrated circuit (ASIC) platforms for real-time applications. Comparison of architectural performance of the proposed scheme with that of architecture designed to realize variable- $\alpha $ LMLS algorithm exhibits 36% less slice-delay-product whereas, its ASIC implementation exhibits 7.27% less area-delay-product, 9.31% less energy-per-sample with 5.2% more maximum achievable frequency. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15498328
- Volume :
- 67
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Circuits & Systems. Part I: Regular Papers
- Publication Type :
- Periodical
- Accession number :
- 145399741
- Full Text :
- https://doi.org/10.1109/TCSI.2020.2984960