Strong‐Motion Seismogeodesy by Deeply Coupling GNSS Receivers with Inertial Measurement Units

Geng, Jianghui; Wen, Qiang; Zhang, Tisheng; Li, Chenghong

doi:10.1029/2020gl087161

Cited by 10 publications

(3 citation statements)

References 20 publications

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“…To validate the performance of the proposed integration method, we utilized a shake table dataset open accessed by Wuhan University [52]. In this dataset, a Quanser Shake Table II was applied to simulate the seismic waveforms of a real earthquake.…”

Section: Shake Table Simulation Experimentsmentioning

confidence: 99%

Integration of High-Rate GNSS and Strong Motion Record Based on Sage–Husa Kalman Filter with Adaptive Estimation of Strong Motion Acceleration Noise Uncertainty

Zhang,

Nie,

Wang

et al. 2024

Remote Sensing

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A strong motion seismometer is a kind of inertial sensor, and it can record middle- to high-frequency ground accelerations. The double-integration from acceleration to displacement amplifies errors caused by tilt, rotation, hysteresis, non-linear instrument response, and noise. This leads to long-period, non-physical baseline drifts in the integrated displacements. GNSS enables the direct observation of the ground displacements, with an accuracy of several millimeters to centimeters and a sample rate of 1 Hz to 50 Hz. Combining GNSS and a strong motion seismometer, one can obtain an accurate displacement series. Typically, a Kalman filter is adopted to integrate GNSS displacements and strong motion accelerations, using the empirical values of noise uncertainty. Considering that there are significantly different errors introduced by the above-mentioned tilt, rotation, hysteresis, and non-linear instrument response at different stations or at different times at the same station, it is inappropriate to employ a fixed noise uncertainty for strong motion accelerations. In this paper, we present a Sage–Husa Kalman filter, where the noise uncertainty of strong motion acceleration is adaptively estimated, to integrate GNSS and strong motion acceleration for obtaining the displacement series. The performance of the proposed method was validated by a shake table simulation experiment and the GNSS/strong motion co-located stations collected during the 2023 Mw 7.8 and Mw 7.6 earthquake doublet in southeast Turkey. The experimental results show that the proposed method enhances the adaptability to the variation of strong motion accelerometer noise level and improves the precision of integrated displacement series. The displacement derived from the proposed method was up to 28% more accurate than those from the Kalman filter in the shake table test, and the correlation coefficient with respect to the references arrived at 0.99. The application to the earthquake event shows that the proposed method can capture seismic waveforms at a promotion of 46% and 23% in the horizontal and vertical directions, respectively, compared with the results of the Kalman filter.

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Section: Shake Table Simulation Experimentsmentioning

confidence: 99%

Integration of High-Rate GNSS and Strong Motion Record Based on Sage–Husa Kalman Filter with Adaptive Estimation of Strong Motion Acceleration Noise Uncertainty

Zhang,

Nie,

Wang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…GNSS static positioning performance is typically tested and evaluated by mounting the receiver on a control point with known coordinates. However, precise evaluation of the [12][13][14][15] Measured by an embedded high-resolution encoder µm High-precision Limited region; High cost…”

Section: Introductionmentioning

confidence: 99%

“…Other researchers proposed evaluation methods for highprecision GNSS kinematic positioning but within a limited testing region. In the field of GNSS seismology, a highprecision shake table was typically used in bench tests to evaluate the GNSS positioning accuracy [12][13][14][15]. The highfrequency GNSS positions, inertial measurement unit (IMU) data, or strong motion sensor measurements are combined in post-processing mode to provide accurate references [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A trajectory similarity-based method to evaluate GNSS kinematic precise positioning performance with a case study

Si-qi

Chen²,

Niu³

et al. 2022

Meas. Sci. Technol.

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There is a lack of effective testing methods to evaluate high-precision global navigation satellite system (GNSS) kinematic positioning solutions, such as GNSS real-time kinematic (RTK) or post-processed kinematic (PPK), with centimeter-level accuracy. Current methods either process static GNSS data in kinematic mode to perform a pseudo-kinematic test or use a precise motion table to make a real kinematic test but within a very limited travel distance. This study proposes a trajectory similarity method by moving a track trolley platform along a railway track, which can match the GNSS positioning trajectory and the pre-surveyed reference track. The GNSS trajectory offsets from the reference track along the cross-track and vertical directions are regarded as GNSS kinematic positioning errors. Lever-arm compensation was applied to achieve millimeter-level accuracy for this evaluation method. A case study was conducted to evaluate the positioning performance of the GPS/BDS PPK using the proposed method. The results indicate that the proposed method can provide a reference trajectory on the order of a few millimeters, which is sufficiently accurate even for PPK positioning performance evaluation and error source tracing in wide regions. In this case, cycle slips as small as 10 cm in the carrier phase measurements can be detected and studied based on the proposed method.

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First results using high-rate BDS-3 observations: retrospective real-time analysis of 2021 Mw 7.4 Madoi (Tibet) earthquake

Zheng

Liu

Zhang

et al. 2022

J Geod

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Strong‐Motion Seismogeodesy by Deeply Coupling GNSS Receivers with Inertial Measurement Units

Cited by 10 publications

References 20 publications

Integration of High-Rate GNSS and Strong Motion Record Based on Sage–Husa Kalman Filter with Adaptive Estimation of Strong Motion Acceleration Noise Uncertainty

Integration of High-Rate GNSS and Strong Motion Record Based on Sage–Husa Kalman Filter with Adaptive Estimation of Strong Motion Acceleration Noise Uncertainty

A trajectory similarity-based method to evaluate GNSS kinematic precise positioning performance with a case study

First results using high-rate BDS-3 observations: retrospective real-time analysis of 2021 Mw 7.4 Madoi (Tibet) earthquake

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