Rapid inversion of source parameters for the 2022 Mengyuan Mw 6.6 earthquake with combined high-rate GNSS and strong motion data

doi:10.21203/rs.3.rs-4818838/v1

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Rapid inversion of source parameters for the 2022 Mengyuan Mw 6.6 earthquake with combined high-rate GNSS and strong motion data

https://doi.org/10.21203/rs.3.rs-4818838/v1

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To investigate the reliability of high-rate GNSS in the rapid inversion of source parameters for the 2022 Menyuan Mw 6.6 earthquake, we collected high-rate GNSS and strong motion data within a 200 km radius of the epicenter. We performed high-precision calculations to obtain three-dimensional dynamic displacement waveforms for all stations and analyzed the reliability of coseismic deformation acquisition and source parameter inversion. The three-dimensional dynamic displacement waveforms show that the amplitude of displacement fluctuations at each station decreases with increasing distance from the epicenter. At the closest station to the epicenter, C007, the maximum amplitude in the east-west direction reached 15 cm, while the maximum amplitude in the north-south direction reached 10.8 cm. The inverted epicenter location using displacement waveforms is (101.263°E, 37.802°N), with an origin time of 17:45:25.9 (UTC) and a magnitude of Mw 6.65. These results are generally consistent with those obtained from seismological methods using seismic wave information. Based on the displacement waveforms 100 seconds before and after the earthquake, the horizontal coseismic deformation of this event can be quickly obtained. The results show opposite motion trends on the south and north sides of the seismogenic fault, with no significant vertical movement, indicating that the earthquake is predominantly characterized by left-lateral strike slip motion. The results indicate that using near-field high-rate GNSS and strong motion data can rapidly and effectively invert strong earthquake source parameters and coseismic deformation results. This can provide valuable reference for post-earthquake emergency response and rapid disaster assessment.

Menyuan earthquake

high-rate GNSS

strong motion

coseismic deformation

source parameters

The Tibetan Plateau region has complex tectonic movements and is one of the most seismically active regions in the world (He X et al. 2020) Strong earthquake events often cause severe destruction (Fig. 1), leading to significant casualties and economic losses. Effectively mitigating the casualties and economic damage caused by destructive earthquakes is an urgent issue that needs to be addressed(Kanamori. 2005). Currently, short-term earthquake prediction remains a global challenge. Accurate prediction of earthquake magnitude and timing is still not feasible. Therefore, the rapid inversion of source parameters after an earthquake is of great significance for quick emergency response, disaster prevention, and mitigation (Allen and Kanamori 2003).

Traditional earthquake observation methods mainly use broadband seismometers and strong motion accelerometers to record velocity and acceleration data. During a strong earthquake, broadband seismometers can easily be affected by clipping and tilting of the stations, which can lead to the underestimation of magnitude (Brown et al. 2011). Additionally, the acceleration data recorded by strong motion accelerometers may experience baseline drift during integration (Boore. 2001). The Global Navigation Satellite System (GNSS) not only can directly record instantaneous dynamic displacements caused by earthquakes but also can directly obtain the permanent displacements of surface stations. In the rapid acquisition of strong earthquake source parameters, GNSS serves as an effective complement to traditional seismological methods(Geng et al. 2016; Shan et al. 2021;Wright et al. 2012). However, due to the limited density and lower spatial resolution of high-rate GNSS stations in China, incorporating near-field strong motion data for joint inversion can quickly and accurately derive source parameters such as magnitude and epicenter location(Gao et al. 2023; Improta et al. 2023; Wang et al. 2011; Zheng et al. 2020)). Static GNSS data for obtaining coseismic deformation relies on several days of observation data before and after the earthquake(Wu et al. 2013). Similarly, Interferometric Synthetic Aperture Radar (InSAR) technology also has a time delay issue in acquiring coseismic deformation(Wu et al. 2013). In contrast, high-rate GNSS requires only a few minutes of data before and after the earthquake to yield reliable coseismic deformation results, effectively avoiding the impact of postseismic effects. It is evident that high-rate GNSS data has unique advantages in the inversion of source parameters and the acquisition of coseismic deformation compared to other data. This is of great significance for post-earthquake emergency response and rescue operations.

According to the official determination by the China Earthquake Networks Center (CENC), the Ms 6.9 earthquake occurred in Menyuan Hui Autonomous County, Qinghai Province, at the intersection of the western segment of the Lenglongling Fault and the eastern segment of the Tuolaishan Fault on January 7, 2022, at 17:45:27 (UTC). The epicenter was located at 37.77°N, 101.26°E, with a focal depth of 10 km. We collected high-rate GNSS data (sampling rate of 1 Hz) and strong motion data (sampling rate of 100 Hz) within a 200 km radius of the epicenter for this earthquake. First, the collected high-rate GNSS data were processed using the precise point positioning (PPP) method to obtain three-dimensional dynamic displacement waveforms. Next, the strong motion data were processed using an automated empirical baseline correction method. This involved performing double integration to obtain displacement time series and removing baseline drift phenomena. Subsequently, the data were resampled to obtain three-dimensional dynamic displacement at 1 Hz, thereby compensating for the sparse spatial distribution of existing high-rate GNSS station locations. Finally, using the processed three-dimensional dynamic displacement data, the earthquake's source parameters were inverted. This includes determining the origin time, epicenter location, and moment magnitude. Additionally, a step function fitting method was employed to obtain the coseismic deformation caused by the earthquake. This exploration aimed to assess the feasibility of using high-rate GNSS data for rapid inversion of source parameters and rapid acquisition of coseismic deformation.

2.1 High-rate GNSS Data

We collected high-rate GNSS observation data from 14 continuously operating reference stations within a 200 km radius of the epicenter, located in Qinghai Province and Gansu Province. The distribution of the stations is shown as blue solid circles in Fig. 1. The data sampling rate for all stations is 1 Hz. We processed the data using the Precise Point Positioning (PPP) method (PRIDE PPP-AR software(Geng et al. 2019)) to obtain the displacement time series results. Existing research indicates that the PPP method, using carrier phase observations along with high-precision satellite orbits and clock correction products, can achieve centimeter-level positioning accuracy using only single-station undifferenced phase and pseudorange observations. This method has been widely applied in the rapid acquisition of coseismic displacement and quick inversion of earthquake magnitude (Larson et al. 2003; Shen et al. 2012; Zang et al. 2022). Additionally, we collected displacement time series results from 8 high-rate GNSS stations as reported in literature(Li et al. 2017).

We plotted the dynamic displacement waveforms of 14 high-rate GNSS stations around the epicenter, as shown by the black lines in Fig. 2. It can be observed that before the seismic waves reached the stations, the displacement waveforms in all directions at all stations remained in a nearly steady state. After the seismic waves arrived, each station recorded changes in the displacement waveforms sequentially. The EBAO station, which is closest to the epicenter or the fault rupture location, recorded the displacement waveform changes first, with maximum amplitudes of 4.4 cm in the east-west direction and 7.8 cm in the north-south direction. The displacement waveform changes at other stations showed a gradual attenuation trend with increasing epicentral distance. Stations north of the epicenter (such as EBAO and ZMYC) exhibited a northwestward motion trend, while stations south of the epicenter (such as MENY and BADT) exhibited a southeastward motion trend. The amplitude of vertical displacement waveform changes was not as significant as the horizontal displacement, suggesting that the earthquake was predominantly characterized by left-lateral strike slip motion.

2.2 Strong Motion Data

Due to the sparse distribution of high-rate GNSS stations around the 2022 Menyuan Mw 6.6 earthquake, we incorporated data from 8 strong motion stations within a 100 km radius of the epicenter to increase the spatial density of stations. The distribution of these stations is shown as black triangles in Fig. 1. This addition aims to improve the accuracy of the inverted source parameters. Strong motion stations record acceleration data. Due to the dynamic motion characteristics of the accelerometers, such as rotation and translation, directly integrating the acceleration data twice to obtain displacement waveforms can result in significant baseline drift(Boore et al.2002; Lin et al. 2018).We used the automatic empirical baseline correction method proposed in literature(Wang et al. 2011) to process the displacement waveforms from these strong motion stations. The effectiveness of this method has been confirmed in several earthquake case studies(Shan et al. 2021). First, we analyzed the original displacement waveforms to determine the range of values for the start $\:{T}_{1}$ and end $\:{T}_{2}$ times of the earthquake event. Then, we derived the baseline correction parameters after the earthquake by differentiating the best-fit equation of the original displacement. Using a grid search method and iteratively adjusting $\:{T}_{1}$ and $\:{T}_{2}$, we determined $\:{T}_{1}$ and $\:{T}_{2}$based on minimizing the step function fitting error. After determining $\:{T}_{1}$ and $\:{T}_{2}$ we performed baseline correction on the segmented velocity time series. By integrating, we obtained the displacement time series, and by differentiating, we obtained the corrected acceleration time series. Then, we resampled the strong motion data to a sampling rate of 1 Hz. After the aforementioned processing, the strong motion data were treated as equivalent to high-rate GNSS data for subsequent analysis. As shown in Fig. 2, the displacement waveforms of the strong motion stations exhibit motion trends that are generally consistent with those of the adjacent high-rate GNSS stations. The amplitude of the waveform changes decreases with increasing epicentral distance. Compared to the positions of the high-rate GNSS stations (Fig. 1), the strong motion stations are closer to the epicenter or rupture fault, thereby increasing the spatial density of the data.

3.1 Inversion of Epicenter Location Based on High-Rate GNSS

The inversion of the epicenter location is an urgent issue that needs to be addressed after an earthquake. Traditional seismology usually employs the Geiger method, a classical linear localization technique(Geiger 1912). Literature (Fang et al.2014) assumes that the propagation speed of seismic waves is equal in different directions. The inversion of the epicenter location using high-rate GNSS displacement waveforms primarily combines the earthquake arrival times and station coordinates of multiple high-rate GNSS stations. Using the least squares adjustment method, it is possible to simultaneously invert for the epicenter location (B, L), the seismic wave propagation speed v, and the origin time T₀. Using the method proposed in literature (Fang et al.2014), assuming the epicenter location is (B, L), the epicentral distance for each high-rate GNSS and strong motion station can be calculated as follows:

$${D_{\text{i}}}=\arccos (\sin {B_i}\sin B+\cos {B_i}\cos B\cos ({L_i} - L))R$$

In the equation, R represents the average radius of the Earth, set at 6371 km; D_i is the epicentral distance for each high-rate GNSS and strong motion station to the epicenter location; B_i and L_i are the latitude and longitude coordinates of each high-rate GNSS and strong motion station.

Based on the displacement waveforms from 22 high-rate GNSS and strong motion stations, the short-time average/long-time average (STA/LTA) method (Allen and Ziv 2011) is used to determine the arrival times of the seismic waves t_i. Assuming the seismic waves propagate at the same speed v in all directions, the arrival times of the seismic waves at each observation station can be determined by the following equation:

$$\left\{ \begin{gathered} {D_2} - {D_1} - v({t_2} - {t_1})=0 \hfill \\ {D_3} - {D_1} - v({t_3} - {t_1})=0 \hfill \\ ...... \hfill \\ {D_{14}} - {D_1} - v({t_{14}} - {t_1})=0 \hfill \\ \end{gathered} \right.$$

After linearization, the least squares adjustment method can be used to invert for the epicenter location (B, L) and the seismic wave propagation speed v. Based on these, the origin time T₀ can be further calculated. The calculation formula is as follows:

$${T_0}=\frac{{\sum\limits_{{i=1}}^{n} {({t_i} - \frac{{{D_i}}}{v})} }}{n}$$

In the formula, n is the number of high-rate GNSS and strong motion stations.

3.2 Magnitude Inversion Based on High-Rate GNSS Data

The earthquake magnitude is a measure that characterizes the strength of an earthquake. There are four commonly used magnitude scales: local magnitude M_L, body wave magnitude M_B, surface wave magnitude M_S, and moment magnitude M_w. Among them, M_L, also known as the Richter magnitude, is determined based on the maximum amplitude recorded by the Wood-Anderson standard seismograph. Because the Wood-Anderson standard seismograph is not widely used, M_L is not commonly employed. M_B and M_S were both proposed by Gutenberg and are frequently used in rapid earthquake reporting. M_w is a physical quantity that represents the absolute size of an earthquake and is directly related to the physical processes at the earthquake source(Hanks 1979). It is the magnitude scale recommended for priority use by the international seismological community today.

The methods commonly used to determine M_w using high-rate GNSS include the finite fault inversion method and the empirical relationship method.

The finite fault inversion method starts from the physical process of the earthquake source. It uses GNSS data to obtain co-seismic displacement, which is then inverted to derive the slip distribution on the fault. From the slip amount, the seismic moment M₀ is calculated using the following formula:

$${M_0}=\mu LW\overline {m}$$

In the formula, L and W are the length and width of the finite fault (unit: km), $\mu$is the rigidity coefficient of the medium, usually 30 GPa, and $\overline {m}$ is the average slip (unit: km). Then, using the relationship between M₀ and M_w, the moment magnitude M_w can be calculated:

$${M_w}=\frac{2}{3}\lg {M_0} - 6.033$$

This method yields the M_w result directly during the slip distribution inversion, serving as an effective validation tool for rapid magnitude assessment using empirical relationships.

The empirical relationship method uses displacement or velocity waveform data obtained from high-rate GNSS, combined with empirical relationships between certain seismic phase amplitudes and magnitude, to obtain M_w. Common methods include the empirical relationship of the horizontal maximum peak displacement P_d within 5 seconds after the P-wave arrival, the peak ground displacement (PGD) empirical relationship method, and the peak ground velocity (PGV) empirical relationship method. According to existing research results, when using the same high-rate GNSS station data for strong earthquake magnitude estimation, the PGD and PGV methods provide more source information over time compared to the Pd method, leading to more accurate magnitude estimation results(Gao et al. 2021).

In this paper, the PGD method is used for magnitude estimation. The PGD values can be extracted based on the three-direction components of the dynamic displacements obtained from high-rate GNSS and strong motion data. The calculation formula is as follows:

$$PGD=\hbox{max} (\sqrt {N_{d}^{2}(t)+E_{d}^{2}(t)+U_{d}^{2}(t)} )$$

In the formula, $N_{{\text{d}}}^{{}}(t)$,$E_{{\text{d}}}^{{}}(t)$ and $U_{{\text{d}}}^{{}}(t)$ represent the north-south, east-west, and vertical displacement components, respectively. Literature (Crowell et al. 2013) utilized high-rate GNSS data from five earthquakes in the United States and Japan to derive the empirical relationship between PGD and Mw, with the regression model being:

$$\log (PGD)=A+B \times {M_{\text{w}}}+C \times {M_{\text{w}}} \times \log (R)$$

$${M_{\text{w}}}=\frac{{\log (PGD) - A}}{{B+C \times \log (R)}}$$

In the formula, R represents the epicentral distance (unit: km), and A, B and C are regression coefficients. The coefficients are shown in Table 1. Additionally, literature summarized a series of regression models for magnitude estimation based on more earthquake cases, with the coefficients also listed in Table 1. To avoid the effects of earthquake rupture directionality and station location amplification, and to ensure the reliability of the magnitude estimation results, the average value of all station magnitude estimates is taken as the final magnitude estimate.

Table 1

PGD magnitude estimation model coefficients
Number	A	B	C	Source	Note
1	-5.013	1.219	-0.178	Literature(Crowell et al. 2013)	PGD in cm
2	-4.434	1.047	-0.138	Literature(Melgar et al. 2015)
3	-6.687	1.500	-0.214	Literature(Crowell et al. 2016)
4	-5.919	1.009	-0.145	Literature(Ruhl et al. 2019)	PGD in m

The epicentral location, origin time, and magnitude results obtained using high-rate GNSS and strong motion data are shown in Table 2. It can be seen that the epicentral location differs from the one published by the United States Geological Survey (USGS) by 3.7 km and from the one published by the China Earthquake Networks Center (CENC) by 3.5 km. The origin time differs from the time published by USGS by 4.1 seconds and from the time published by CENC by 1.1 seconds. It can be seen that the epicentral location, origin time, and magnitude results obtained using these densely distributed near-field high-rate GNSS data differ only slightly from those published by CENC and USGS. Additionally, the seismic wave velocity obtained from the high-rate GNSS inversion is 3.8 km/s, which is consistent with other inversion results and falls within the actual range of surface wave velocities (3–5 km/s)(Wang et al. 2016).

Table 2

2022 Mengyuan Mw 6.6 earthquake source parameter information
Source	Epicenter		Origin time	Magnitude
Source	longitude/°	latitude/°	Origin time	Magnitude
CENC	37.770	101.260	17:45:27	M_s 6.9
USGS	37.828	101.290	17:45:30	M_w 6.6
This study	37.802	101.263	17:45:25.9	M_w 6.65

Considering that the 2022 Menyuan Mw 6.6 earthquake was primarily characterized by left-lateral strike slip motion, and the vertical displacement components recorded by GNSS are usually noisier by 3 to 5 orders of magnitude compared to the horizontal components(Li et al. 2018), we explore the reliability of the existing PGD magnitude estimation empirical formulas (Table 1) using PGD values that exclude vertical displacement. The results are evaluated using the Root Mean Square Error (RMSE) value.Table 3 presents the magnitude estimation results calculated using four different empirical formulas, where PGDh represents the peak ground displacement excluding the vertical component. M_w1, M_w2, M_w3 and M_w4 are calculated using the empirical formulas from literatures (Crowell et al. 2013), (Melgar et al. 2015), (Crowell et al. 2016) and (Ruhl et al. 2019) respectively. It can be seen that for this strike-slip earthquake event, excluding the vertical displacement has a minor impact on the magnitude estimation results. As shown in Fig. 3, the magnitudes obtained using the other three empirical formulas differ significantly from those obtained using the formula from literature (Gao et al. 2021) and also show a considerable deviation from the actual magnitude results. Notably, the magnitude estimation result using the formula from literature (Ruhl et al. 2019) has the highest RMSE value of 7.1.The high uncertainty may be attributed to the fact that existing empirical formulas primarily use data from subduction zone earthquake events with a single tectonic environment, while earthquake events in the tectonically complex Tibetan Plateau region have hardly been used. Additionally, these empirical formulas are based on a single-scale pattern of global observations from different tectonic systems, making them a first-order approximation (Melgar et al. 2015). Therefore, there are still certain limitations in estimating the magnitude of strong earthquakes.

Table 3

Estimation of magnitude using different empirical formulas
Number	Stations name	R/km	PGDh/cm	M_w1	M_w2	M_w3	M_w4
1	5245	51	6.20	6.59	6.17	6.34	6.44
2	BADT	167	4.52	6.82	6.34	6.54	6.56
3	BUQL	125	2.70	6.64	6.05	6.30	6.31
4	EBAO	36	7.40	6.46	6.10	6.23	6.36
5	HAIY	120	4.49	6.84	6.35	6.56	6.59
6	HUZH	115	2.89	6.78	6.20	6.46	6.45
7	JCJC	96	7.41	7.16	6.76	6.93	6.98
8	MENY	100	5.48	6.56	6.14	6.31	6.40
9	MOLE	35	4.98	6.59	6.14	6.33	6.40
10	WWHS	99	2.62	6.93	6.31	6.60	6.55
11	WWLC	122	2.97	6.80	6.22	6.49	6.48
12	ZMYC	120	12.04	6.65	6.37	6.46	6.62
13	ZYAY	53	1.59	6.53	5.82	6.00	5.86
14	ZYSD	60	2.75	6.72	6.13	6.40	6.39
15	C005	53	4.27	6.47	6.00	6.19	6.26
16	C007	17	15.82	6.38	5.00	6.22	6.43
17	C010	100	2.56	6.62	6.02	6.28	6.28
18	C012	79	3.56	6.62	6.10	6.32	6.35
19	C013	36	6.90	6.45	6.08	6.22	6.34
20	C014	85	3.85	6.69	7.50	6.39	6.43
21	C046	61	4.46	6.55	6.09	6.28	6.35
22	MYQS	35	9.00	6.53	6.21	6.33	6.47
Maximum	——	——	——	7.16	7.50	6.93	6.98
Minimum	——	——	——	6.38	5.00	6.19	6.19
Average	——	——	——	6.65	6.18	6.37	6.42

It is worth emphasizing that the use of high-rate GNSS data to invert the epicenter, origin time, and magnitude of earthquakes is not intended to replace traditional seismometer-based location techniques, but rather to enrich the means of determining earthquake source parameters. This approach is particularly valuable when strong earthquakes occur in areas without a seismic network. The inversion of the epicenter using high-rate GNSS data and strong motion data can serve as an effective supplementary method to traditional seismological techniques (Zhen et al. 2023).

In addition, co-seismic deformation at 22 stations near the epicenter was obtained using high-rate GNSS data for 100 seconds before and after the earthquake combined with the step function fitting method. The results are shown in Fig. 4. It can be seen that the station closest to the epicenter, C007, experienced the largest horizontal co-seismic displacement, moving ~ 55.16 mm towards the EN direction. The station farthest from the epicenter, WWHS, moved ~ 4.44 mm towards the WS direction. Stations within 170 km of the epicenter recorded millimeter-level horizontal permanent deformation. Horizontal co-seismic deformation gradually decreased with increasing distance from the epicenter, but some stations did not strictly follow this trend due to influences such as fault rupture direction, seismic wave propagation path, and site effects. The horizontal co-seismic results clearly show that stations on the north and south sides of the epicenter moved in opposite horizontal directions, while vertical movement was not significant, consistent with a left-lateral strike slip earthquake deformation pattern and previous research findings. The study results indicate that using high-rate GNSS data for 100 seconds before and after the earthquake can capture the co-seismic deformation of this event, significantly speeding up the acquisition of co-seismic deformation compared to static GNSS data and InSAR data, and also reducing the impact of post-seismic effects. These results will provide important data sources for slip distribution inversion, rupture process studies, and other related research.

We collected high-rate GNSS and strong motion data within a 200 km radius of the epicenter of the 2022 Menyuan Mw 6.6 earthquake. Through high-precision processing, we obtained the surface displacement waveforms caused by the earthquake and inverted the earthquake source parameters, obtaining the co-seismic deformation results. The main conclusions are as follows:

The displacement waveforms of high-rate GNSS and strong motion stations show a gradual attenuation trend with increasing distance from the epicenter. The station closest to the epicenter, C007, recorded a maximum east-west amplitude of 15 cm and a north-south amplitude of 10.8 cm. Stations within 200 km of the epicenter recorded centimeter-level amplitude changes.The epicenter location obtained from the inversion using high-rate GNSS and strong motion data differs by less than 4 km from the results published by USGS and CENC. The origin time differs by less than 5 seconds from the times announced by these agencies, and the magnitude differs by less than 0.1 magnitude units. This indicates that high-rate GNSS and strong motion data can be used to quickly and accurately invert the source parameters of strong earthquakes, serving as an effective supplement to traditional seismological methods.

There are significant differences in the magnitude results obtained using different empirical relationships for magnitude estimation. This may be due to the existing models following a single-scale pattern that cannot account for the local effects of the Tibetan Plateau region. Considering the complex tectonics of the Tibetan Plateau, there is an urgent need to establish a magnitude estimation model suitable for this region to lay the foundation for accurately inverting the magnitude of strong earthquakes in the future.

The co-seismic deformation results show that the stations on the north and south sides of the epicenter exhibit opposite horizontal movement trends, with no significant vertical movement. The closest station to the epicenter, C007, recorded permanent deformations of 5.14 cm east-west and 2 cm north-south, with a total horizontal permanent deformation of 5.51 cm. These deformation results indicate that the earthquake was predominantly a left-lateral strike slip event, providing data support for subsequent inversions of the rupture process and regional seismic hazard analysis.

Acknowledgements

We thank the continuous operating reference stations in Qinghai Province and Gansu Province, China, for providing the high-rate GNSS observation data. We also acknowledge Professor ZhiCai Li's team for providing some of the high-rate GNSS displacement waveform data. All figures were created using GMT software (Wessel et al. 2013).

Data availability statement

All data that support the findings of this study are included within the article (and any supplementary files).

Conflict of interest statement

All authors have read and agreed to the published version of the manuscript.

Funding

National Natural Science Foundation of China（42174003、41604007）

China Postdoctoral Science Foundation（2023T160285）

Natural Science Foundation of Gansu Province, China（22JR5RA315）

National Natural Science Foundation of China(42174003、41604007)

Author Contribution

Y handled the data processing and paper writing, S provided the data, theoretical foundation, and plotting, G offered research ideas, and K provided technical support. All authors revised and reviewed the paper.

Allen R M and Ziv A (2011) Application of real‐time GPS to earthquake early warning. Geophysical Research Letters. 38(16) : L16310.
Allen R M and Kanamori H (2003) The potential for earthquake early warning in southern California. Science. 300(5620) 786–789.
Brown H M, Allen R M, Hellweg M, Khainovski O, Neuhauser D and Souf A (2011) Development of the ElarmS methodology for earthquake early warning: Realtime application in California and offline testing in Japan. Soil Dynamics and Earthquake Engineering. 31(2) 188–200.
Boore D M (2001) Effect of baseline corrections on displacements and response spectra for several recordings of the 1999 Chi-Chi, Taiwan, earthquake. Bulletin of the Seismological Society of America. 91(5) 1199–1211.
Boore D M, Stephens C D and Joyner W B (2002) Comments on baseline correction of digital strong-motion data: Examples from the 1999 Hector Mine, California, earthquake[J]. Bulletin of the Seismological Society of America. 92(4) 1543–1560.
Crowell B W, Schmidt D A, Bodin P, Vidale J E, Gomberg J, Renate Hartog J and Jamison D G (2016) Demonstration of the Cascadia G‐FAST geodetic earthquake early warning system for the Nisqually, Washington, earthquake. Seismological Research Letters. 87(4) 930–943.
Crowell B W, Melgar D, Bock Y, Haase J S and Geng J (2013) Earthquake magnitude scaling using seismogeodetic data. Geophysical Research Letters 40(23) 6089–6094.
Fang R, Shi C, Wang G and Liu J (2014) Epicenter and magnitude of large earthquake determined from high-rate GPS observations: A case study of the 2008 M 8.0 Wenchuan earthquake. Science China Earth Sciences. 57 1645–1652.
He X, Zhang Y, Shen X, Zheng W, Zhang P and Zhang D (2020) Examination of the repeatability of two Ms6.4 Menyuan earthquakes in Qilian-Haiyuan fault zone (NE Tibetan Plateau) based on source parameters. Physics of the Earth and Planetary Interiors. 299 106408.
Hu J, Li Z W, Ding X L, Zhu J J, Zhang L and Sun Q (2012) 3D coseismic displacement of 2010 Darfield, New Zealand earthquake estimated from multi-aperture InSAR and D-InSAR measurements. Journal of Geodesy. 86 1029–1041.
Hanks, Thomas C and Hiroo Kanamori (1979) A moment magnitude scale. Journal of Geophysical Research: Solid Earth 84.B5 (1979): 2348–2350.
Improta L, Cirella A, Pezzo G, Molinari I and Piatanesi A (2023) Joint inversion of geodetic and strong motion data for the 2012, Mw 6.1–6.0, May 20th and May 29th, northern Italy earthquakes: Source models and seismotectonic interpretation. Journal of Geophysical Research: Solid Earth. 128(5) e2022JB026278.
Geng J, Chen X, Pan Y, Mao S, Li C, Zhou J and Zhang K (2019) PRIDE PPP-AR: an open-source software for GPS PPP ambiguity resolution. GPS solutions. 23 1–10.
Gao ZhiYu,Li YanChuan and Shan XinJian (2023) Rapid Inversion study of strong earthquake slip distribution on the anninghe fault based on simulated high-gate GNSS data. Journal of Wuhan University (Information Science Edition) 1–20[2024-07-04] https://doi.org/10.13203/j.whugis20230221
Geng T, Xie X, Fang R, Su X, Zhao Q, Liu G and Liu J (2016) Real‐time capture of seismic waves using high‐rate multi‐GNSS observations: Application to the 2015 Mw 7.8 Nepal earthquake. Geophysical Research Letters. 43(1) 161–167.
Geiger L. (1912) Probability method for the determination of earthquake epicentres from the arrival time only. Bull Saint Louis University. 1912 8: 60.
Gao Z, Li Y, Shan X and Zhu C (2021) Earthquake magnitude estimation from high-rate GNSS data: A case study of the 2021 Mw 7.3 Maduo earthquake. Remote sensing. 13(21) 4478.
Kanamori H (2005) Real-time seismology and earthquake damage mitigation. Annu. Rev. Earth Planet. 33(1) 195–214.
Larson K M, Bodin P and Gomberg J (2003) Using 1-Hz GPS data to measure deformations caused by the Denali fault earthquake. Science. 300(5624) 1421–1424.
Li Y, Liu S, Chen L, Du Y, Li H and Liu D (2017) Mechanism of crustal deformation in the Sichuan-Yunnan region, southeastern Tibetan Plateau: Insights from numerical modeling. Journal of Asian Earth Sciences. 146 142–151.
Lin Y, Zong Z, Tian S and Lin J (2018) A new baseline correction method for near-fault strong-motion records based on the target final displacement. Soil Dynamics and Earthquake Engineering. Sci. 114 27–37.
Li Y, Liu M, Wang Q and Cui D (2018) Present-day crustal deformation and strain transfer in northeastern Tibetan Plateau. Earth and Planetary Science Letters. 487 179–189.
Melgar D, Crowell B W, Geng J, Allen R M, Bock Y, Riquelme S and Ganas A (2015) Earthquake magnitude calculation without saturation from the scaling of peak ground displacement. Geophysical Research Letters. 42(13) 5197–5205.
Ruhl C J, Melgar D, Geng J, Goldberg D E, Crowell B W, Allen R M and d'Anastasio E (2019) A global database of strong‐motion displacement GNSS recordings and an example application to PGD scaling. Seismological Research Letters. 90(1) 271–279.
Shen F, Li J and Guo F (2012) Analysis of the coseismic displacements along Eastern Coast of China caused by the 2011 Tohoku-Oki earthquake using epoch-by-epoch PPP. Geomatics and Information Science of Wuhan University. 37(11) 1345–1347.
Shan X, Li Y, Wang Z, Yin H, Liu X, Liu Y and Qu C (2021) GNSS for quasi‐real‐time earthquake source determination in eastern Tibet: A prototype system toward early warning applications. Seismological Society of America. 92(5) 2988–2997.
Wright T J, Houlié N, Hildyard M and Iwabuchi T (2012) Real‐time, reliable magnitudes for large earthquakes from 1 Hz GPS precise point positioning: The 2011 Tohoku‐Oki (Japan) earthquake. Geophysical research letters. 39(12).
Wang M, Hubbard J, Plesch A, Shaw J H and Wang L (2016) Three‐dimensional seismic velocity structure in the Sichuan basin, China. Journal of Geophysical Research: Solid Earth, 121(2) 1007–1022.
Wessel P, Smith W H, Scharroo R, Luis J and Wobbe F (2013) Generic mapping tools: improved version released. Eos, Transactions American Geophysical Union. 94(45) 409–410.
Wang R, Schurr B, Milkereit C, Shao Z and Jin M (2011) An improved automatic scheme for empirical baseline correction of digital strong-motion records. Bulletin of the Seismological Society of America. 101(5) 2029–2044.
Wu Y, Jiang Z, Wang M, Che S, Liao H, Li Q and Liu X (2013) Preliminary results pertaining to coseismic displacement and preseismic strain accumulation of the Lushan MS 7.0 earthquake, as reflected by GPS surveying. Chinese Science Bulletin. 58 3460–3466.
Zhen Jiawei,L Min and Fang Rongxin (2023) Rapid inversion of fault rupture characteristics of the Luding 6.8 earthquake in sichuan using combined high-rate GNSS and strong motion data. Chinese Journal of Geophysics. 66(04): 1419–1427.
Zang J, Wen Y, Li Z, Xu C, He K, Zhang P and Fan, S (2022) Rapid source models of the 2021 Mw 7.4 Maduo, China, earthquake inferred from high-rate BDS3/2, GPS, Galileo and GLONASS observations. Journal of Geodesy. 96(9) 58.
Zheng X, Zhang Y, Wang R, Zhao L, Li W and Huang Q (2020) Automatic inversions of strong‐motion records for finite‐fault models of significant earthquakes in and around Japan. Journal of Geophysical Research: Solid Earth. 125(9) e2020JB019992.

No competing interests reported.

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Rapid inversion of source parameters for the 2022 Mengyuan Mw 6.6 earthquake with combined high-rate GNSS and strong motion data

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Abstract

Figures

1. Introduction

2. Data Sources and Data Processing

2.1 High-rate GNSS Data

2.2 Strong Motion Data

3. Inversion Method for Source Parameters

3.1 Inversion of Epicenter Location Based on High-Rate GNSS

3.2 Magnitude Inversion Based on High-Rate GNSS Data

4. Results and Discussion

5. Conclusions

Declarations

Author Contribution

References

Additional Declarations

Status:

Version 1