Study on Trend and Characteristics of Spatio-Temporal Evolution of COVID-19 Epidemic in China—Based on Spatial Markov Chain and STL Time Series Model

2022 ◽  
Vol 12 (01) ◽  
pp. 8-19
Author(s):  
小英 张
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Temporal Evolution ◽  
Time Series Model ◽  
Spatial Markov Chain ◽  
Spatio Temporal

scholarly journals Adjoint-based direct data assimilation of GNSS time series for optimizing frictional parameters and predicting postseismic deformation following the 2003 Tokachi-oki earthquake

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Masayuki Kano ◽  
Shin’ichi Miyazaki ◽  
Yoichi Ishikawa ◽  
Kazuro Hirahara
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Evaluation Method ◽  
Temporal Evolution ◽  
Recovery Process ◽  
Postseismic Deformation ◽  
Coseismic Slip ◽  
Megathrust Earthquakes ◽  
Spatio Temporal ◽  
Gnss Time Series

Abstract Postseismic Global Navigation Satellite System (GNSS) time series followed by megathrust earthquakes can be interpreted as a result of afterslip on the plate interface, especially in its early phase. Afterslip is a stress release process accumulated by adjacent coseismic slip and can be considered a recovery process for future events during earthquake cycles. Spatio-temporal evolution of afterslip often triggers subsequent earthquakes through stress perturbation. Therefore, it is important to quantitatively capture the spatio-temporal evolution of afterslip and related postseismic crustal deformation and to predict their future evolution with a physics-based simulation. We developed an adjoint data assimilation method, which directly assimilates GNSS time series into a physics-based model to optimize the frictional parameters that control the slip behavior on the fault. The developed method was validated with synthetic data. Through the optimization of frictional parameters, the spatial distributions of afterslip could roughly (but not in detail) be reproduced if the observation noise was included. The optimization of frictional parameters reproduced not only the postseismic displacements used for the assimilation, but also improved the prediction skill of the following time series. Then, we applied the developed method to the observed GNSS time series for the first 15 days following the 2003 Tokachi-oki earthquake. The frictional parameters in the afterslip regions were optimized to A–B ~ O(10 kPa), A ~ O(100 kPa), and L ~ O(10 mm). A large afterslip is inferred on the shallower side of the coseismic slip area. The optimized frictional parameters quantitatively predicted the postseismic GNSS time series for the following 15 days. These characteristics can also be detected if the simulation variables can be simultaneously optimized. The developed data assimilation method, which can be directly applied to GNSS time series following megathrust earthquakes, is an effective quantitative evaluation method for assessing risks of subsequent earthquakes and for monitoring the recovery process of megathrust earthquakes.

scholarly journals Adjoint-based Direct Data Assimilation of Gnss Time Series for Optimizing Frictional Parameters and Predicting Postseismic Deformation Following the 2003 Tokachi-oki Earthquake

2020 ◽  
Author(s):  
Masayuki Kano ◽  
Shin'ichi Miyazaki ◽  
Yoichi Ishikawa ◽  
Kazuro Hirahara
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Evaluation Method ◽  
Temporal Evolution ◽  
Recovery Process ◽  
Postseismic Deformation ◽  
Coseismic Slip ◽  
Megathrust Earthquakes ◽  
Spatio Temporal ◽  
Gnss Time Series

Abstract PostseismicGlobal Navigation Satellite System (GNSS) time seriesfollowed by megathrust earthquakes can be interpreted as a result of afterslip on the plate interface especially in its early phase. Afterslip is a stress release process accumulated by adjacent coseismic slip andcan be considered a recovery process for future events during earthquake cycles. Spatio-temporal evolution of afterslip often triggers subsequent earthquakes through stress perturbation. Therefore, it is important toquantitativelycapture the spatio-temporal evolution of afterslip and related postseismic crustal deformation and to predict their future evolution with a physics-based simulation. We developedanadjoint data assimilation method, which directly assimilates GNSS time series into a physics-based model to optimize the frictional parameters that control the slip behavior on the fault.The developed method was validated with synthetic data. Through the optimization of frictional parameters, the spatial distributions of afterslip can be roughly reproduced but not in detail if the observation noise is included. The optimization of frictional parameters provides not only the reproduction ofpostseismic displacements used for the assimilation but also the improvement in the prediction skill of the following time series. Then, we appliedthe developed method to the observed GNSS time series for the first 15 d following the 2003 Tokachi-oki earthquake. The frictional parameters in the afterslip regions were optimized to A-B ~ O(10 kPa), A ~ O(100 kPa), and L ~ O(10 mm). The large afterslip is inferred on the shallower side of the coseismic slip area. The optimized frictional parameters quantitatively predicted the postseismicGNSS time series for the following 15 d. These characteristics can be also detected if the simulation variables can be simultaneously optimized. The developed data assimilation method, which can be directly applied to GNSS time series following megathrust earthquakes, isan effective quantitative evaluation method for assessing risks of subsequent earthquakes and for monitoring the recovery process of megathrust earthquakes.

scholarly journals Adjoint-based Direct Data Assimilation of GNSS time series for optimizing frictional parameters and predicting postseismic deformation following the 2003 Tokachi-oki earthquake

2020 ◽  
Author(s):  
Masayuki Kano ◽  
Shin'ichi Miyazaki ◽  
Yoichi Ishikawa ◽  
Kazuro Hirahara
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Temporal Evolution ◽  
Recovery Process ◽  
Postseismic Deformation ◽  
Coseismic Slip ◽  
Megathrust Earthquakes ◽  
Spatio Temporal ◽  
Gnss Time Series ◽  
O 10

Abstract Postseismic Global Navigation Satellite System (GNSS) time series followed by megathrust earthquakes can be interpreted as a result of afterslip on the plate interface, especially in its early phase. Afterslip is a stress release process accumulated by adjacent coseismic slip and can be considered a recovery process for future events during earthquake cycles. Spatio-temporal evolution of afterslip often triggers subsequent earthquakes through stress perturbation. Therefore, it is important to quantitatively capture the spatio-temporal evolution of afterslip and related postseismic crustal deformation and to predict their future evolution with a physics-based simulation. We developed an adjoint data assimilation method, which directly assimilates GNSS time series into a physics-based model to optimize the frictional parameters that control the slip behavior on the fault. The developed method was validated with synthetic data. Through the optimization of frictional parameters, the spatial distributions of afterslip could roughly (but not in detail) be reproduced if the observation noise was included. The optimization of frictional parameters reproduced not only the postseismic displacements used for the assimilation but also improved the prediction skill of the following time series. Then, we applied the developed method to the observed GNSS time series for the first 15 days following the 2003 Tokachi-oki earthquake. The frictional parameters in the afterslip regions were optimized to A-B ~ O (10 kPa), A ~ O (100 kPa), and L ~ O (10 mm). A large afterslip is inferred on the shallower side of the coseismic slip area. The optimized frictional parameters quantitatively predicted the postseismic GNSS time series for the following 15 days. These characteristics can also be detected if the simulation variables can be simultaneously optimized. The developed data assimilation method, which can be directly applied to GNSS time series following megathrust earthquakes, is an effective quantitative evaluation method for assessing risks of subsequent earthquakes and for monitoring the recovery process of megathrust earthquakes.

scholarly journals Evaluation of the potential of InSAR time series to study the spatio-temporal evolution of piezometric levels in the Madrid aquifer

2015 ◽  
Vol 372 ◽  
pp. 29-32 ◽  
Author(s):  
M. Béjar-Pizarro ◽  
P. Ezquerro Martín ◽  
G. Herrera ◽  
R. Tomás ◽  
C. Guardiola-Albert ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Evolution ◽  
Ground Deformation ◽  
Complex Structure ◽  
Ground Subsidence ◽  
Piezometric Level ◽  
Extraction Recovery ◽  
The North ◽  
Spatio Temporal ◽  
Recovery Cycles

Abstract. The Tertiary detritic aquifer of Madrid (TDAM), with an average thickness of 1500 m and a heterogeneous, anisotropic structure, supplies water to Madrid, the most populated city of Spain (3.2 million inhabitants in the metropolitan area). Besides its complex structure, a previous work focused in the north-northwest of Madrid city showed that the aquifer behaves quasi elastically trough extraction/recovery cycles and ground uplifting during recovery periods compensates most of the ground subsidence measured during previous extraction periods (Ezquerro et al., 2014). Therefore, the relationship between ground deformation and groundwater level through time can be simulated using simple elastic models. In this work, we model the temporal evolution of the piezometric level in 19 wells of the TDAM in the period 1997–2010. Using InSAR and piezometric time series spanning the studied period, we first estimate the elastic storage coefficient (Ske) for every well. Both, the Ske of each well and the average Ske of all wells, are used to predict hydraulic heads at the different well locations during the study period and compared against the measured hydraulic heads, leading to very similar errors when using the Ske of each well and the average Ske of all wells: 14 and 16 % on average respectively. This result suggests that an average Ske can be used to estimate piezometric level variations in all the points where ground deformation has been measured by InSAR, thus allowing production of piezometric level maps for the different extraction/recovery cycles in the TDAM.

scholarly journals Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model

2021 ◽  
Vol 18 (2) ◽  
pp. 774
Author(s):  
Rongxiang Rui ◽  
Maozai Tian ◽  
Man-Lai Tang ◽  
George To-Sum Ho ◽  
Chun-Ho Wu
Keyword(s):  
Time Series ◽  
Minimum Temperature ◽  
Multivariate Time Series ◽  
Time Series Model ◽  
Maximum Temperature ◽  
Atmospheric Ozone ◽  
Temperature Minimum ◽  
Risk Effect ◽  
The Usa ◽  
Spatio Temporal

With the rapid spread of the pandemic due to the coronavirus disease 2019 (COVID-19), the virus has already led to considerable mortality and morbidity worldwide, as well as having a severe impact on economic development. In this article, we analyze the state-level correlation between COVID-19 risk and weather/climate factors in the USA. For this purpose, we consider a spatio-temporal multivariate time series model under a hierarchical framework, which is especially suitable for envisioning the virus transmission tendency across a geographic area over time. Briefly, our model decomposes the COVID-19 risk into: (i) an autoregressive component that describes the within-state COVID-19 risk effect; (ii) a spatiotemporal component that describes the across-state COVID-19 risk effect; (iii) an exogenous component that includes other factors (e.g., weather/climate) that could envision future epidemic development risk; and (iv) an endemic component that captures the function of time and other predictors mainly for individual states. Our results indicate that maximum temperature, minimum temperature, humidity, the percentage of cloud coverage, and the columnar density of total atmospheric ozone have a strong association with the COVID-19 pandemic in many states. In particular, the maximum temperature, minimum temperature, and the columnar density of total atmospheric ozone demonstrate statistically significant associations with the tendency of COVID-19 spreading in almost all states. Furthermore, our results from transmission tendency analysis suggest that the community-level transmission has been relatively mitigated in the USA, and the daily confirmed cases within a state are predominated by the earlier daily confirmed cases within that state compared to other factors, which implies that states such as Texas, California, and Florida with a large number of confirmed cases still need strategies like stay-at-home orders to prevent another outbreak.

RENEWAL SEQUENCES WITH PERIODIC DYNAMICS

2011 ◽  
Vol 26 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Brian Fralix ◽  
James Livsey ◽  
Robert Lund
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Stochastic Processes ◽  
Discrete Time ◽  
Stationary Sequence ◽  
Time Series Model ◽  
Periodic Dynamics

Discrete-time renewal sequences play a fundamental role in the theory of stochastic processes. This article considers periodic versions of such processes; specifically, the length of an interrenewal is allowed to probabilistically depend on the season at which it began. Using only elementary renewal and Markov chain techniques, computational and limiting aspects of periodic renewal sequences are investigated. We use these results to construct a time series model for a periodically stationary sequence of integer counts.

scholarly journals Illuminating the Spatio-Temporal Evolution of the 2008–2009 Qaidam Earthquake Sequence with the Joint Use of Insar Time Series and Teleseismic Data

2020 ◽  
Vol 12 (17) ◽  
pp. 2850 ◽  
Author(s):  
Simon Daout ◽  
Andreas Steinberg ◽  
Marius Paul Isken ◽  
Sebastian Heimann ◽  
Henriette Sudhaus
Keyword(s):  
Time Series ◽  
Temporal Evolution ◽  
Kinematic Model ◽  
Earthquake Sequence ◽  
Back Projection ◽  
High Angle ◽  
Sedimentary Sequences ◽  
Teleseismic Data ◽  
Oblique Convergence ◽  
Spatio Temporal

Inferring the geometry and evolution of an earthquake sequence is crucial to understand how fault systems are segmented and interact. However, structural geological models are often poorly constrained in remote areas and fault inference is an ill-posed problem with a reliability that depends on many factors. Here, we investigate the geometry of the Mw 6.3 2008 and 2009 Qaidam earthquakes, in northeast Tibet, by combining InSAR time series and teleseismic data. We conduct a multi-array back-projection analysis from broadband teleseismic data and process three overlapping Envisat tracks covering the two earthquakes to extract the spatio-temporal evolution of seismic ruptures. We then integrate both geodetic and seismological data into a self-consistent kinematic model of the earthquake sequence. Our results constrain the depth and along-strike segmentation of the thrust-faulting sequence. The 2008 earthquake ruptured a ∼32° north-dipping fault that roots under the Olongbulak pop-up structure at ∼12 km depth and fault slip evolved post-seismically in a downdip direction. The 2009 earthquake ruptured three south-dipping high-angle thrusts and propagated from ∼9 km depth to the surface and bilaterally along the south-dipping segmented 55–75° high-angle faults of the Olonbulak pop-up structure that displace basin deformed sedimentary sequences above Paleozoic bedrock. Our analysis reveals that the inclusion of the post-seismic afterslip into modelling is beneficial in the determination of fault geometry, while teleseismic back-projection appears to be a robust tool for identifying rupture segmentation for moderate-sized earthquakes. These findings support the hypothesis that the Qilian Shan is expanding southward along a low-angle décollement that partitions the oblique convergence along multiple flower and pop-up structures.

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