Adaptive Denoising Approach for High-Rate GNSS Seismic Waveform Preservation: Application to the 2010 EI Mayor-Cucapah Earthquake and 2012 Brawley Seismic Swarm

Yanyan Li,Lei Yi,Caijun Xu,Haitao Yin,Chuanfa Chen,Xiaoxing He

doi:10.1109/access.2019.2956780

Abstract

The high noise levels of high-rate Global Navigation Satellite System (GNSS) solutions limit their seismological applications, including capturing earthquake-induced coseismic displacements. In this study, we developed a new adaptive denoising approach for high-rate GNSS observations to improve the precision of seismic displacements and preserve seismic waveforms in GNSS coseismic signals. The performance of the proposed method was evaluated using high-rate (5 Hz) GNSS data acquired from the moderate EI Mayor-Cucapah earthquake (M w 7.2) on 4 April 2010 and the small Brawley seismic swarm (M w 4.6-5.5) on 26 August 2012. The performance of the proposed method was compared with those of modified sidereal filtering, Stacking filtering, and MSF plus Stacking. The comparison showed that the proposed method is more precise than other widely used method. It can significantly remove high-frequency white noise and low-frequency colored noise caused by CME, multipath errors, and/or other unmodeled systematic errors in high-rate GNSS displacements. The results were also compared with collocated strong motion data (50 and 200 Hz). The high precision of the proposed method was mainly afforded by the high performance of complete ensemble empirical mode decomposition, which was used to decompose the GNSS signal into different frequency modes. However, the normalized autocorrelation function and correlation coefficients used to determine the noise-dominated high-frequency modes and the “wavelet-like” soft-threshold used for direct denoising of the noise-dominated high-frequency modes also contributed. Despite the high noise levels of GNSS solutions, especially regarding the vertical displacement components, some small-amplitude details, which are usually only detectable by seismic instruments, could be observed in the denoised displacements in this study. The results reported herein indicate that the proposed method significantly improves the precision and reliability of GNSS displacements and the effectiveness of seismic signal detection, which is particularly critical for the measurement of earthquake-induced coseismic displacements.

Highlights

We first analyze the results of the application of the proposed method to the 5-Hz Global Navigation Satellite System (GNSS) seismic time series, compared with those of modified sidereal filtering (MSF) [23], Stacking filtering [60] and MSF plus Stacking
The performance of the proposed method was validated using the high-rate (5 Hz) GNSS data obtained during the moderate EI Mayor–Cucapah earthquake (Mw 7.2) of 4 April 2010 and the small Brawley seismic swarm (Mw 4.6–5.5) of 26 August 2012
The results showed that for denoising the high-rate GNSS displacements, the proposed method is more precise than the other widely used method, such as MSF, Stacking, and MSF plus Stacking

Summary

INTRODUCTION

Global Navigation Satellite Systems (GNSSs) play an important role in structure health monitoring [1], [2], seismic. Y. Li et al.: Adaptive Denoising Approach for High-Rate GNSS Seismic Waveform Preservation wave detection, and moderate and large earthquake early warning (EEW) systems [3], [4]. To improve the precision and reliability of high-rate GNSS displacements and enable the detection of P-wave arrival, denoising algorithms are required to reduce or eliminate inherent noise and preserve seismic waveforms in measured signals. Achieving residue noise toleration, the key is determining the cut-off point for the transition between noise-dominated and signal-dominated IMFs. To improve precision and reliability and preserve precise seismic waveforms in high-rate GNSS seismic displacements, the present study implemented a new improved CEEMD-MPCA denoising approach based on a correlation coefficient, a normalized autocorrelation function, and ‘‘wavelet-like’’ threshold denoising. The results were compared with collocated strong motion data (50 and 200 Hz)

THEORY Assume that n-dimensional noisy seismic signals can be modeled as

RESULTS AND DISCUSSION

MODERATE EARTHQUAKE EXAMPLE

CONCLUSION