High-rate Global Navigation Satellite System Research Articles

Super-shear ruptures steered by pre-stress heterogeneities during the 2023 Kahramanmaraş earthquake doublet

The 2023 M7.8 and M7.5 earthquake doublet near Kahramanmaraş, Turkey, provides insight regarding how large earthquakes rupture complex faults. Here we determine the faults geometry using surface ruptures and Synthetic Aperture Radar measurements, and the rupture kinematics from the joint inversion of high-rate Global Navigation Satellite System (GNSS), strong-motion waveforms, and GNSS static displacement. The M7.8 event initiated on a splay fault and subsequently propagated along the main East Anatolian Fault with an average rupture velocity between 3.0 and 4.0 km/s. In contrast, the M7.5 event demonstrated a bilateral supershear rupture of about 5.0–6.0 km/s over an 80 km length. Despite varying strike and dip angles, the sub-faults involved in the mainshock are nearly optimally oriented relative to the local stress tensor. The second event ruptured a fault misaligned with respect to the regional stress, also hinting at the effect of local stress heterogeneity in addition to a possible free surface effect.

Feasibility of Coseismic Landslide Prediction Based on GNSS Observations: A Case Study of the 2022 Ms 6.8 Luding, China, Earthquake

Abstract On 5 September 2022, an M 6.8 earthquake struck Luding County in Sichuan Province, China, triggering extensive landslides and causing severe damages. In this study, taking this event as an exemplary case study, we test the feasibility of fast earthquake-induced landslide prediction utilizing Global Navigation Satellite System (GNSS) observations. Particularly, we construct finite-slip models based on static offsets and 1 Hz displacement waveforms. Employing these slip models, physics-based simulation (PBS) is applied separately to obtain peak ground velocity (PGV). The PGVs are then integrated into landslide spatial distribution probability prediction based on the Deep Forest algorithm. Our results show that the predicted landslides probability distribution of fast inversion models using static and high-rate GNSS data align well with the landslide catalog. Furthermore, high-rate GNSS data can improve the model performance by providing the evolution information of rupture. In addition, we also derive PGV from the empirically regressed ground-motion prediction equations (GMPEs) and incorporate it into landslide prediction. The GMPEs exhibits an advantage in terms of prediction recall for landslides and a relatively reduced accuracy compared with the PBS. Despite the inherent uncertainties in this study, based on the case study of the 2022 M 6.8 Luding earthquake, we utilize GNSS data and present a set of methods for real-time landslide prediction. The achieved model performance is relatively satisfactory, considering the challenges and uncertainties involved.

The Impact of the Three-Dimensional Structure of a Subduction Zone on Time-dependent Crustal Deformation Measured by HR-GNSS

Accurately modeling time-dependent coseismic crustal deformation as observed on high-rate Global Navigation Satellite System (HR-GNSS) lends insight into earthquake source processes and improves local earthquake and tsunami early warning algorithms. Currently, time-dependent crustal deformation modeling relies most frequently on simplified 1D radially symmetric Earth models. However, for shallow subduction zone earthquakes, even low-frequency shaking is likely affected by the many strongly heterogeneous structures such as the subducting slab, mantle wedge, and the overlying crustal structure. We demonstrate that including 3D structure improves the estimation of key features of coseismic HR-GNSS time series, such as the peak ground displacement (PGD), the time to PGD (tPGD), static displacements (SD), and waveform cross-correlation values. We computed synthetic 1D and 3D, 0.25 Hz and 0.5 Hz waveforms at HR-GNSS stations for four M7.3+ earthquakes in Japan using MudPy and SW4, respectively. From these synthetics, we computed intensity-measure residuals between the synthetic and observed GNSS waveforms. Comparing 1D and 3D residuals, we observed that the 3D simulations show better fits to the PGD and SD in the observed waveforms than the 1D simulations for both 0.25 Hz and 0.5 Hz simulations. We find that the reduction in PGD residuals in the 3D simulations is a combined effect of both shallow and deep 3D structures; hence incorporating only the upper 30 km of 3D structure will still improve the fit to the observed PGD values. Our results demonstrate that 3D simulations significantly improve models of GNSS waveform characteristics and will not only help understand the underlying processes, but also improve local tsunami warning.

Simultaneous magnitude and slip distribution characterization from high-rate GNSS using deep learning: case studies of the 2021 Mw 7.4 Maduo and 2023 Turkey doublet events

SUMMARY Rapid and accurate characterization of earthquake sources is crucial for mitigating seismic hazards. In this study, based on 18 000 scenario ruptures ranging from Mw 6.4 to Mw 8.3 and corresponding synthetic high-rate Global Navigation Satellite System (HR-GNSS) waveforms, we developed a multibranch neural network framework, the continental large earthquake agile response (CLEAR), to simultaneously determine the magnitude and slip distributions. We apply CLEAR to recent large strike-slip events, including the 2021 Mw 7.4 Maduo earthquake and the 2023 Mw 7.8 and Mw 7.6 Turkey doublet. The model generally estimates the magnitudes successfully at 32 s with errors of less than 0.15, and predicts the slip distributions acceptably at 64 s, requiring only approximately 30 ms on a single CPU (Central Processing Unit). With optimal azimuthal coverage of stations, the system is relatively robust to the number of stations and the time length of the received data.

GNSS gyroscopes: determination of angular velocity and acceleration with very high-rate GNSS

Although global navigation satellite systems (GNSS) have been routinely applied to determine attitudes, there exists no literature on determining angular velocity and/or angular acceleration from GNSS. Motivated by the invention of computerized accelerometers of the correspondence author and following the success of accurately recovering translational velocity and acceleration waveforms from very high-rate GNSS precise positioning by Xu and his collaborators in 2021, we propose the concept of GNSS gyroscopes and reconstruct angular velocity and acceleration from very high-rate GNSS attitudes by applying regularization under the criterion of minimum mean squared errors. The major results from the experiments can be summarized in the following: (i) angular velocity and acceleration waveforms computed by applying the difference methods to high-rate GNSS attitudes are too noisy and can be physically not meaningful and numerically incorrect. The same can be said about inertial measurement unit (IMU) attitudes, if IMU gyros are not of very high accuracy; (ii) regularization is successfully applied to reconstruct the high-rate angular velocity and acceleration waveforms from 50 Hz GNSS attitudes and significantly outperforms the difference methods, validating the proposed concept of GNSS gyroscopes. By comparing the angular velocity and acceleration results by using the difference methods and regularization, we find that the peak values of angular velocity and acceleration by regularization are much smaller by a maximum factor of 1.57 in the angular velocity to a maximum factor of 8662.53 times in the angular acceleration in the case of high-rate GNSS, and by a maximum factor of 1.26 in the angular velocity to a maximum factor of 2819.85 times in the angular acceleration in the case of IMU, respectively; and (iii) the IMU attitudes apparently lead to better regularized angular velocity and acceleration waveforms than the high-rate GNSS attitudes, which can well be explained by the fact that the former is of better accuracy than the latter. As a result, to suppress the significant amplification of noise in GNSS attitudes, larger regularization parameters have to be chosen for the high-rate GNSS attitudes, resulting in smaller peak angular accelerations by a maximum factor of 37.55 percent in the angular velocity to a maximum factor of 6.20 times in the angular acceleration in comparison of the corresponding IMU results. Nevertheless, the regularized angular acceleration waveforms for both GNSS and IMU look more or less similar in pattern or waveform shape.

Assessing seismic displacements and early warning using kinematic PPP with MADOCA real-time products: A case study of 2023 Kahramanmaraş (Türkiye) earthquakes

Early warning and emergency hazard response studies in large earthquakes require rapid and reliable determination of earthquake magnitude. The magnitude of an earthquake can be estimated based on peak ground displacement (PGD) values captured from high-rate GNSS records. In this study, two major earthquakes that hit Kahramanmaraş Province on February 6, 2023, were investigated by using the real-time Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) technique to confirm the usability of GNSS in early warning. Data from high-rate (1-Hz) GNSS stations were evaluated with the real-time kinematic PPP approach utilizing the multi-GNSS advanced demonstration tool for orbit and clock analysis (MADOCA) products and JPL's GipsyX software. In addition, static PPP was also used to identify the permanent displacements caused by earthquakes. Static PPP results demonstrated that significant displacements were detected at stations close to the epicenters, particularly the 446.86 cm horizontal displacement in the EKZ1 station. Considering the kinematic PPP results revealed that when PGD values calculated from seismic waves, functional model, and updated coefficients were implemented, the moment magnitude of the Pazarcık earthquake was estimated as Mw 7.78, which was reported as Mw 7.8 by USGS and Mw 7.7 by AFAD, whilst the magnitude of the Elbistan earthquake was computed as Mw 7.56, which was reported as Mw 7.5 by USGS and 7.6 by AFAD. Overall, this study validates the effectiveness of the real-time GNSS PPP as an attractive alternative for analyzing coseismic events and supporting early warning.

GNSS-Constrained Rupture Kinematics of the 2022 Mw 6.7 Luding, China, Earthquake: Directivity Pulse during the Asymmetrical Bilateral Rupture

Abstract Impulse motion characterized by a large amplitude in the fault-normal direction can be observed at near-fault strong motion sites during strike-slip earthquakes. The large pulse, which always causes high intensity and stronger damage to structures close to faults, is usually attributed to the directivity effect of rupture propagating along strike and the proximity to the fault. We present an analysis of such a large directivity pulse captured by the near-fault high-rate Global Navigation Satellite System (GNSS) during the 2022 Mw 6.7 Luding, China, earthquake—the largest event ever observed by space geodesy on the seismically active Xianshuihe fault in the eastern Tibetan Plateau. We invert the displacement waveforms and offsets derived from the continuous and campaign GNSS for the rupture kinematics. The inferred slip model reveals a rupture zone of 30 km in length above 15 km depth along the Moxi segment, yielding a seismic moment of 1.1×1019 N·m and a source duration of 13 s. The high-rate GNSS (hrGNSS) waveforms suggest an asymmetric bilateral rupture: most slips with long rise time are concentrated on the southern part of the ruptured fault, whereas a short-duration pulse-like slip rate with low final slip propagates during the northward rupture. We found that the directivity pulse observed by the nearest hrGNSS site is controlled primarily by the sharp pulse-like slip rate and rapid rupture velocity approximating the local S-wave velocity. Along with additional local amplification, this large directivity pulse may be responsible for the heavy damage in Moxi town close to the northern ruptured fault.

High-rate GNSS multi-frequency uncombined PPP-AR for dynamic deformation monitoring

Precise point positioning (PPP) is an important alternative to real-time kinematic (RTK) positioning for deformation monitoring. Global Navigation Satellite System (GNSS) multi-frequency and multi-system integration can benefit PPP dynamic deformation monitoring due to increased measurement redundancy and facilitated ambiguity resolution (AR). This study investigates the performance of GNSS multi-frequency uncombined (UC) PPP-AR with GPS triple-frequency, BDS-3 triple-frequency, and Galileo five-frequency signals for monitoring high-rate vibrations based on 20-Hz observations from a shaking table. The GNSS multi-frequency UC fractional cycle bias (FCB) estimation method and PPP-AR method are developed. The results of vibration tests indicate that the displacement monitoring accuracy of GPS/BDS-3/Galileo multi-frequency PPP-AR solution is 5.3–8.4 mm. BDS-3 dual-frequency PPP-AR, BDS-3 triple-frequency PPP-AR, and GPS/BDS-3/Galileo multi-frequency PPP-AR solutions improve the accuracy by 8%, 17%, and 46% over the BDS-3 dual-frequency float solutions, respectively. GNSS multi-frequency UC PPP-AR can accurately identify the vibration frequency and amplitude, but the PPP-derived displacement is nosier than that of RTK.

Modal frequencies evaluation of a damaged bridge using RCVMD algorithm based on sensor dynamic responses

This paper aims to employ high-rate global navigation satellite system real-time kinematic (GNSS-RTK) and triaxial accelerometer sensors to evaluate the modal frequencies of a damaged long-span bridge based on the proposed modified variational mode decomposition algorithm. Firstly, in the modified VMD, the relative root mean square error was presented to optimize the decomposition parameter K, the correlation criterion was introduced to select the suitable intrinsic mode function components. The above procedure is called the RCVMD algorithm. Subsequently, the performance comparison evaluation experiments proved that the RCVMD algorithm was more advantageous in the decomposition and reconstruction of signals, had a better noise reduction effect in the signal with different noise levels, and was suitable to process the actual measurement response. Finally, in the field experiment, the modal frequencies of the damaged bridge were correctly detected from the GNSS-RTK displacement and acceleration responses according to the RCVMD algorithm, and the accelerometer picked up the high-order structural frequencies, demonstrating the reliability of the proposed method. The frequency characteristics of the damaged bridge are different from the normal state. The first and third-order frequencies increase, which is especially obvious in the first-order frequency, yet the second-order frequency decreases. The measured results have practical applications and can provide data support for the maintenance of the damaged bridge.

Deep learning for denoising High-Rate Global Navigation Satellite System data

High-rate global navigation satellite system (HR-GNSS) data records ground displacements and can be used to identify earthquakes and slow slip events. One limitation of such data is the high amplitude, cm-level noise which make it difficult to identify processes that produce surface displacements smaller than these values. Deep learning has proven adept at performing many useful tasks in seismology and geophysics. Here we explore using deep learning to denoise HR-GNSS data. We develop three different convolutional neural networks with similar architectures but different targets. Training data are synthetic HR-GNSS records and actual noise recordings that are superimposed to generate noisy signals. We train each of the three models to output masks that can be used to reconstruct the true signal. We use a set of performance metrics that quantify the models’ ability to denoise the testing data and find that denoising significantly improves the signal-to-noise ratio and the ability to identify first arrivals. Finally, we test the models on HR-GNSS records from the Ridgecrest earthquakes recorded at stations that have nearly colocated strong-motion sites used ground-truth the denoising results. We find that the models greatly improve the signal-to-noise ratios in these records and make the P-wave onset clearly identifiable.

Slip distribution of the February 6, 2023 Mw 7.8 and Mw 7.6, Kahramanmaraş, Turkey earthquake sequence in the East Anatolian Fault Zone

On February 6, 2023, two large earthquakes occurred near the Turkish town of Kahramanmaraş. The moment magnitude (Mw) 7.8 mainshock ruptured a 310 km-long segment of the left-lateral East Anatolian Fault, propagating through multiple releasing step-overs. The Mw 7.6 aftershock involved nearby left-lateral strike-slip faults of the East Anatolian Fault Zone, causing a 150 km-long rupture. We use remote-sensing observations to constrain the spatial distribution of coseismic slip for these two events and the February 20 Mw 6.4 aftershock near Antakya. Pixel tracking of optical and synthetic aperture radar data of the Sentinel-2 and Sentinel-1 satellites, respectively, provide near-field surface displacements. High-rate Global Navigation Satellite System data constrain each event separately. Coseismic slip extends from the surface to about 15 km depth with a shallow slip deficit. Most aftershocks cluster at major fault bends, surround the regions of high coseismic slip, or extend outward of the ruptured faults. For the mainshock, rupture propagation stopped southward at the diffuse termination of the East Anatolian fault and tapered off northward into the Pütürge segment, some 20 km south of the 2020 Mw 6.8 Elaziğ earthquake, highlighting a potential seismic gap. These events underscore the high seismic potential of immature fault systems.

Interchangeable Use of GNSS and Seismic Data for Rapid Earthquake Characterization: 2021 Chignik, Alaska, Earthquake

AbstractEarthquake magnitude estimation using peak ground velocities (PGVs) derived from Global Navigation Satellite Systems (GNSS) data has shown promise for rapid characterization of damaging earthquakes. Here, we examine the feasibility of using GNSS-derived velocity waveforms as interchangeable data for rapid magnitude and ground motion estimation that typically rely on strong-motion seismic records. Our study compares PGVs derived from high-rate GNSS to those computed from high-rate seismic records (strong-motion and velocity) at collocated and closely located stations. The recent 2021 Mw 8.2 Chignik earthquake in Alaska that was recorded on collocated GNSS and strong-motion sensors provides the perfect opportunity to compare the two data streams and their application in rapid response. The Chignik velocity records appear almost identical at collocated GNSS and strong-motion stations when observed at frequencies <0.25 Hz. GNSS and strong-motion derived velocity data are further employed to generate rapid estimates of PGV-derived moment magnitudes for the earthquake. The moment magnitude estimates from GNSS and joint GNSS and joint (GNSS and seismic) data are within ∼±0.4 magnitude units (Fang et al., 2020) of the final magnitude (Mw 8.2). ShakeMaps generated for the 2021 Chignik earthquake using GNSS and seismic PGVs show notable agreement between them, and show negligible shifts in PGV contours when collocated and closely located GNSS and seismic stations are substituted for one another. Therefore, we posit that GNSS is a powerful alternative or addition to seismic data and vice versa.

Unique characteristics of ultrabroadband seismograms for the 2011 Mw 9.1 Tohoku earthquake from high-rate global navigation satellite system and continuous-stress records

Unique characteristics of ultrabroadband seismograms for the 2011 Mw 9.1 Tohoku earthquake from high-rate global navigation satellite system and continuous-stress records

Real-Time Source Modeling of the 2022 Mw 6.6 Menyuan, China Earthquake with High-Rate GNSS Observations

On 7 January 2022, a Mw 6.6 earthquake struck Menyuan County in the Qinghai province of China and the earthquake caused severe damage to infrastructures. In this study, the performance of the high-rate global navigation satellite system (GNSS) on real-time source modeling of the 2022 Mw 6.6 Menyuan earthquake was validated. We conducted the warning magnitude calculation, centroid moment tensor (CMT) inversion, and static fault slip distribution inversion using displacements collected from 14 1-Hz GNSS stations. Our results indicate that the warning magnitude derived from the peak ground displacement (PGD) first exceeds Mw 6.0 approximately 9 s after the earthquake and tends to be stable after about 45 s. The derived finally stable magnitude is Mw 6.5, which is near the USGS magnitude of Mw 6.6. Based on the inverted CMT and static fault slip distribution results, it can be determined that the 2022 Menyuan earthquake is a left-lateral strike-slip event after about 20 s of the earthquake. Although the fault slips, inverted with the 30-s smoothed coseismic offsets, are unstable after about 40 s, all the inverted slip models after that time present the obvious surface rupture and the most fault motions are concentrated between the depth of 0 km and 8 km. Compared with the results inverted with the 30-s smoothed coseismic offsets, the CMT and fault slips inverted with the 70-s smoothed coseismic offsets are more stable. The results obtained in this study indicate that the high-rate GNSS has the potential to be used for real-time source modeling for earthquakes with a magnitude less than 7; the stability of the inverted CMT and fault slips can be improved by using the coseismic offsets averaged by a relatively long-time sliding window.

Supervised Machine Learning of High Rate GNSS Velocities for Earthquake Strong Motion Signals

AbstractHigh rate Global Navigation Satellite System (GNSS) processed time series capture a broad spectrum of earthquake strong motion signals, but experience regular sporadic noise that can be difficult to distinguish from true seismic signals. The range of possible seismic signal frequencies amidst a high, location‐varying noise floor makes filtering difficult to generalize. Existing methods for automatic detection rely on external inputs to mitigate false alerts, which limit their usefulness. For these reasons, geodetic seismic signal detection makes for a compelling candidate for data‐driven machine learning classification. In this study we generated high rate GNSS time differenced carrier phase (TDCP) velocity time series concurrent in space and time with expected signals from 77 earthquakes occurring over nearly 20 years. TDCP velocity processing has increased sensitivity relative to traditional geodetic displacement processing without requiring sophisticated corrections. We trained, validated and tested a random forest classifier to differentiate seismic events from noise. We find our supervised random forest classifier outperforms the existing detection methods in stand‐alone mode by combining frequency and time domain features into decision criteria. The classifier achieves a 90% true positive rate of seismic event detection within the data set of events ranging from MW4.8–8.2, with typical detection latencies seconds behind S‐wave arrivals. We conclude the performance of this model provides sufficient confidence to enable these valuable ground motion measurements to run in stand‐alone mode for development of edge processing, geodetic infrastructure monitoring and inclusion in operational ground motion observations and models.

Experimental Study of Accuracy of High-Rate GNSS in Context of Structural Health Monitoring

Global Navigation Satellite Systems (GNSS)-based technologies have become an indispensable part of current structural health monitoring (SHM) systems because of the unique capability of the GNSS technologies to provide accurate real-time displacement information. GNSS equipment with a data sampling rate of up to about 20 Hz has been widely used for this purpose. High-rate GNSS systems (typically up to about 100 Hz) offer additional advantages in structural health monitoring as some highly dynamic civil structures, such as some bridges, require high-rate monitoring data to capture the dynamic behaviors. However, the performance of high-rate GNSS positioning in the context of structural health monitoring is not entirely known, as studies on structural monitoring with high-rate GNSS positioning are very limited, especially considering that some of the satellite systems just reached their full constellations very recently. We carried out a series of experiments with the help of a shaking table to assess the SHM performance of a set of 100 Hz GNSS equipment and three commonly used GNSS positioning techniques, PPP (precise point positioning), PPP-AR (precise point positioning with ambiguity resolution), and RTK (real-time kinematic). We found that the standard deviations of the 100 Hz GNSS displacement solutions derived from PPP, PPP-AR, and RTK techniques were 5.5 mm, 3.6 mm, and 0.8 mm, respectively, when the antenna was in quasi-static motion, and about 9.2 mm, 6.2 mm, and 3.5 mm, respectively, when the antenna was in vibration (up to about 0.7 Hz), under typical urban observational conditions in Hong Kong. We also found that the higher a sampling rate is, the lower the accuracy of a measured displacement series is. On average, the 10 Hz and 100 Hz results are 5.5% and 10.3%, respectively, noisier than the 1 Hz results.

Overall subshear but locally supershear rupture of the 2021 Mw 7.4 Maduo earthquake from high-rate GNSS waveforms and three-dimensional InSAR deformation

An Mw 7.4 left-lateral earthquake hit Maduo County in Qinghai province, China, on 21 May 2021. In this paper, we combined the 1-Hz global navigation satellite system (GNSS) displacement waveforms, GNSS static offsets and the three-dimensional (3D) co-seismic deformation field from the interferometric synthetic radar (InSAR) measurements to estimate the source kinematics of this event. Our results reveal that there was about 160 km of surface ruptures with varying strike angles, and the earthquake propagated bilaterally, with the bulk of the slip formed to the southeast of the epicenter. Substantial slip occurred to a depth of ~6 km, and the estimated maximum slip was ~5 m. The source time function presents a ~ 35-s total duration with two peaks, and the total geodetic seismic moment is 1.61 × 1020 Nm (equivalent to Mw 7.4). The near-field high-rate GNSS waveforms inversion results indicate that this earthquake is an overall subshear but locally supershear rupture event. The preferred average rupture velocity is 2.8 km/s while the velocities to the southeast and northwest of epicenter are 3.8 and 2.2 km/s, respectively. Supershear enhanced ground motion and simulation using the 3D curvilinear grid finite-difference method shows the acceleration caused by the earthquake exceeded the building code limits for bridges that collapsed due to strong ground shaking. Furthermore, this earthquake increased the stress level of the Maqin–Maqu segment of the East Kunlun Fault, and was also responsible for an off-fault aftershock sequence at the northwest end of the earthquake rupture.

Rapid Source Determination of the 2021 Mw 7.4 Maduo Earthquake by a Dense High-Rate GNSS Network in the Tibetan Plateau

Abstract High-rate Global Navigation Satellite Systems (GNSS) observations from an integrated network in the Tibetan plateau were used for rapid source determination of the 2021 Mw 7.4 Maduo earthquake. Constrained by high-rate displacements from nine stations near the source, the magnitude was essentially determined as early as 20 s after the origin time (OT), whereas the 39-s-long rupture propagated only approximately halfway, and together with the epicenter location and rupture length, they were completely determined 29 s after the OT or 10 s before the rupture completion. In addition, to test the capability and feasibility of current GNSS networks for rapid source determination in the Tibetan plateau, two experiments were further implemented to invert source parameters by real observations at farther sites and synthetic observations induced by an Mw 7.0 modeled earthquake. The results demonstrate that current GNSS networks are capable and feasible to provide rapid source determination and earthquake early warning in approximately 30 s after the OT for large earthquakes (Mw 7.0+) based on current station density, spatial coverage and communication conditions in the Tibetan plateau.

Assessment of Real-Time PPP with Trimble RTX correction service for real-time dynamic displacement monitoring based on high-rate GNSS observations

Real-time displacement monitoring of earth crust and engineering structures with the Global Navigation Satellite System (GNSS) is highly important for detecting and preventing the damage and possible loss of life during natural phenomena. Nowadays, the Real-Time Kinematic Precise Point Positioning (RT-PPP) method using a stand-alone GNSS receiver can provide fast and accurate dynamic displacement. Trimble Real-Time Extended (RTX) technology enables real-time precise positioning with cm-level accuracy in a short convergence time. The present study investigates, for the first time, the dynamic displacement detection performance of RT-PPP based on CenterPoint RTX correction service for early warning systems and rapid risk assessment. To evaluate the performance of Trimble RTX technology, a series of shake table experiments were conducted and harmonic oscillations with different frequencies and amplitudes were generated. Trimble RTX solutions were obtained at 20 Hz sampling rate and compared with Linear Variable Differential Transformer (LVDT) data, considered as reference, in both frequency and time domains. Results show that Trimble RTX technology can accurately detect the frequency of harmonic motions, whose amplitudes are slightly different from LVDT values because of GNSS inherent noise. The average of Root Mean Square Error (RMSE) values obtained from the differences between LVDT and filtered RTX-derived time series for all events is 3.6 mm. These results indicated that the CenterPoint RTX technology could be employed for real-time and near-real-time applications to monitor structural and earthquake-induced motions accurately.

On the Potential of Rapid Moment Magnitude Estimation for Strong Earthquakes in Sichuan–Yunnan Region, China, Using Real-Time CMONOC GNSS Observations

Abstract High-rate (≥1 Hz) real-time Global Navigation Satellite Systems (GNSS) data have been now incorporated into earthquake early warning systems where dense GNSS network exists, like the United States west coast, Japan, and so on. In this manuscript, we exploit the potential of rapid moment magnitude (Mw) estimation in Sichuan–Yunnan region using GNSS records from Crustal Movement Observation Network of China (CMONOC) for seven magnitude (M) ≥ 6 events from 2011 to 2020. We find that currently it is still quite challenging to get reliable Mw using the peak ground displacement or peak ground velocity approach, which is caused by the sparse stations and probably the inaccurate scaling law itself. By contrast, the Mw estimations from focal mechanism grid search are all consistent with Global Centroid Moment Tensor catalog, even by one station. Considering the CMONOC network is going to be densified in the coming decade, it is expected that high-rate GNSS waveforms will play a more important role in fast postevent response.