scholarly journals Consistent estimation of strain-rate fields from GNSS velocity data using basis function expansion with ABIC

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Tomohisa Okazaki ◽  
Yukitoshi Fukahata ◽  
Takuya Nishimura
Keyword(s):  
Strain Rate ◽  
Basis Function ◽  
Crustal Deformation ◽  
Information Criterion ◽  
Satellite System ◽  
Least Square ◽  
Inversion Method ◽  
Simple Formulation ◽  
Function Expansion ◽  
Gnss Data

AbstractPresent day crustal displacement rates can be accurately observed at stations of global navigation satellite system (GNSS), and crustal deformation has been investigated by estimating strain-rate fields from discrete GNSS data. For this purpose, a modified least-square inversion method was proposed by Shen et al. (J Geophys Res 101:27957–27980, 1996). This method offers a simple formulation for simultaneously estimating smooth velocity and strain-rate fields from GNSS data, and it has contributed to clarify crustal deformation fields in many regions all over the world. However, we notice three theoretical points to be examined when we apply the method: mathematical inconsistency between estimated velocity and strain-rate fields, difficulty in objectively determining the optimal value of a hyperparameter that controls smoothness, and inappropriate estimation of uncertainty. In this study, we propose a method of basis function expansion with Akaike’s Bayesian information criterion (ABIC), which overcomes the above difficulties. Application of the two methods to GNSS data in Japan reveals that the inconsistency in the method of Shen et al. is generally insignificant, but could be clear in regions with sparser observation stations such as in islet areas. The method of basis function expansion with ABIC shows a significantly better performance than the method of Shen et al. in terms of the trade-off curve between the residual of fitting and the roughness of velocity field. The estimated strain-rate field with the basis function expansion clearly exhibits a low strain-rate zone in the forearc from the southern Tohoku district to central Japan. We also find that the Ou Backbone Range has several contractive spots around active volcanoes and that these locations well correspond to the subsidence areas detected by InSAR after the 2011 Tohoku-oki earthquake. Thus, the method of basis function expansion with ABIC would serve as an effective tool for estimating strain-rate fields from GNSS data.

Consistent Estimation of Strain Rate Fields from GNSS Velocity Data Using Basis Function Expansion with ABIC

2021 ◽  
Author(s):  
Tomohisa Okazaki ◽  
Yukitoshi Fukahata ◽  
Takuya Nishimura
Keyword(s):  
Strain Rate ◽  
Basis Function ◽  
Crustal Deformation ◽  
Information Criterion ◽  
Satellite System ◽  
Simple Formulation ◽  
Function Expansion ◽  
Global Navigation Satellite ◽  
Gnss Data ◽  
Strain Rate Field

Abstract Present day crustal displacement rates can be accurately observed at stations of global navigation satellite system (GNSS), and crustal deformation has been investigated by estimating strain-rate fields from discrete GNSS data. The method proposed by Shen et al. (J Geophys Res 101:27957–27980, 1996) offers a simple formulation for simultaneously estimating smooth velocity and strain-rate fields, and it has contributed to clarify crustal deformation fields in many regions all over the world. However, in this paper, we point out three theoretical disadvantages of the method: mathematical inconsistency between estimated velocity and strain-rate fields, inability to objectively determine the optimal value of a hyperparameter that controls smoothness, and inaccurate estimation of uncertainty. As an alternative, we propose a method of basis function expansion with Akaike's Bayesian information criterion (ABIC), which overcomes the above difficulties. Application of the two methods to GNSS data in Japan reveals that the inconsistency in the method of Shen et al. is generally insignificant, but could be serious in regions with sparser observation stations such as in islet areas. More importantly, the method of basis function expansion with ABIC shows a significantly better performance than the method of Shen et al. in terms of the trade-off curve between the residual of fitting and the roughness of velocity field. The estimated strain-rate field with the basis function expansion clearly exhibits a low strain-rate zone in the forearc from the southern Tohoku district to central Japan. We also find that the Ou Backbone Range has several contractive spots around active volcanoes and that these locations well correspond to the subsidence areas detected by InSAR after the 2011 Tohoku-oki earthquake. Thus, the method of basis function expansion with ABIC would serve as an effective tool for estimating strain-rate fields from GNSS data.

Crustal strain-rate fields estimated from GNSS data with a Bayesian approach and its correlation to seismic activity

2021 ◽  
Author(s):  
Ziyao Xiong ◽  
Jiancang Zhuang
Keyword(s):  
Strain Rate ◽  
Bayesian Approach ◽  
Seismic Activity ◽  
Mainland China ◽  
Information Criterion ◽  
Satellite System ◽  
Displacement Rate ◽  
Rate Data ◽  
Maximum Shear Strain ◽  
Crustal Strain

<p>We proposed a new Bayesian approach to estimate continuous crustal strain-rate fields from spatially discrete displacement-rate data, based on Global Navigation Satellite System (GNSS) observations, under the prior constraint on spatial flatness of the strain-rate fields. The optimal values of the hyperparameters in the model of strain-rate fields are determined by using Akaike's Bayesian Information Criterion. A methodological merit of this approach is that, by introducing a two-layer Delaunay tessellation technique, the time-consuming computation of strain rates can be omitted through the model estimation process. We applied the Bayesian approach to GNSS displacement-rate data in Mainland China and examined the correlation between the estimated strain-rate fields and seismic activity by using Molchan’s Error Diagram. The results show that the increase rate of maximum shear strain is positively correlated with the occurrence of earthquakes, indicating the strain rate can be used to augment probability earthquake models for background seismicity forecasting.</p>

A strain rate model for Europe from a dense network of GNSS

2021 ◽  
Author(s):  
Holger Steffen ◽  
Rebekka Steffen ◽  
Ambrus Kenyeres ◽  
Alessandro Caporali ◽  
Joaquin Zurutuza ◽  
...  
Keyword(s):  
Velocity Field ◽  
Strain Rate ◽  
Velocity Model ◽  
Satellite System ◽  
Least Square ◽  
Plate Boundaries ◽  
Strain Rates ◽  
Rate Model ◽  
2D Strain ◽  
Global Navigation Satellite

<p>Strain rates are an important factor to find areas that are under stress. Higher strain rates are usually observed along plate boundaries, while lower strain rates are found in intraplate regions. The increased availability of velocity solutions from Global Navigation Satellite System (GNSS) for entire Europe allows a 2D strain rate to be estimated at high resolution. Thus, regions of high and low strain become clearly visible. Here, we will present a new strain rate model, which is based on a recent velocity field solution by the EUREF Permanent Network Densification (EPND2100). This velocity field is obtained by the combination of weekly position SINEX solutions generated by 28 EPND Analysis Centres. More details on EPND can be found in the www.epnd.sgo-penc.hu website. The homogenized and quality checked velocity field is then interpolated via a least-square collocation using a fixed scale length of 135 km. In addition, the effect of known plate boundaries is considered during the interpolation to avoid a smoothing of nearby velocities on different tectonic plates. We also apply a moving variance approach to avoid effects of non-stationarity, which arise due to the variable station densities. The interpolated velocity model is then used to estimate a 2D strain rate covering most of Europe. We will highlight the situation in intraplate areas with very low strain rates but dense GNSS networks.</p>

Regional Global Navigation Satellite System Networks for Crustal Deformation Monitoring

2019 ◽  
Vol 91 (2A) ◽  
pp. 552-572 ◽  
Author(s):  
Jessica R. Murray ◽  
Noel Bartlow ◽  
Yehuda Bock ◽  
Benjamin A. Brooks ◽  
James Foster ◽  
...  
Keyword(s):  
Seismic Hazard ◽  
Real Time ◽  
Global Navigation Satellite System ◽  
Crustal Deformation ◽  
Satellite System ◽  
Navigation Satellite ◽  
Global Navigation ◽  
Seismic Networks ◽  
Global Navigation Satellite ◽  
Gnss Data

Abstract Regional networks of Global Navigation Satellite System (GNSS) stations cover seismically and volcanically active areas throughout the United States. Data from these networks have been used to produce high-precision, three-component velocity fields covering broad geographic regions as well as position time series that track time-varying crustal deformation. This information has contributed to assessing interseismic strain accumulation and related seismic hazard, revealed previously unknown occurrences of aseismic fault slip, constrained coseismic slip estimates, and enabled monitoring of volcanic unrest and postseismic deformation. In addition, real-time GNSS data are now widely available. Such observations proved invaluable for tracking the rapidly evolving eruption of Kīlauea in 2018. Real-time earthquake source modeling using GNSS data is being incorporated into tsunami warning systems, and a vigorous research effort is focused on quantifying the contribution that real-time GNSS can make to improve earthquake early warnings as part of the Advanced National Seismic System ShakeAlert system. Real-time GNSS data can also aid in the tracking of ionospheric disturbances and precipitable water vapor for weather forecasting. Although regional GNSS and seismic networks generally have been established independently, their spatial footprints often overlap, and in some cases the same institution operates both types of networks. Further integration of GNSS and seismic networks would promote joint use of the two data types to better characterize earthquake sources and ground motion as well as offer opportunities for more efficient network operations. Looking ahead, upgrading network stations to leverage new GNSS technology could enable more precise positioning and robust real-time operations. New computational approaches such as machine learning have the potential to enable full utilization of the large amounts of data generated by continuous GNSS networks. Development of seafloor Global Positioning System-acoustic networks would provide unique information for fundamental and applied research on subduction zone seismic hazard and, potentially, monitoring.

scholarly journals Detection of Plastic Strain Using GNSS Data of Pre- and Post-Seismic Deformation of the 2011 Tohoku-oki Earthquake

2020 ◽  
Author(s):  
Yukitoshi Fukahata ◽  
Angela Meneses-Gutierrez ◽  
Takeshi Sagiya
Keyword(s):  
Plastic Deformation ◽  
Plastic Strain ◽  
Strain Rate ◽  
Crustal Deformation ◽  
Japan Trench ◽  
Strain Rates ◽  
Tectonic Zone ◽  
Viscous Deformation ◽  
Before And After ◽  
Gnss Data

<p>In general, there are three mechanisms causing crustal deformation: elastic, viscous, and plastic deformation. The separation of observed crustal deformation to each component has been a challenging problem. Meneses-Gutierrez and Sagiya (2016, EPSL) have successfully separated inelastic deformation from observed geodetic data from the comparison of GNSS data before and after the 2011 Tohoku-oki earthquake in the northern Niigata-Kobe tectonic zone (NKTZ), central Japan. In this study, we further succeed in separating plastic deformation as well as viscous deformation in the northern NKTZ using GNSS data before and after the 2011 Tohoku-oki earthquake, under the assumptions that elastic deformation is principally caused by the plate coupling along the Japan trench and that plastic deformation ceased after the Tohoku-oki earthquake due to the stress drop caused by the earthquake. The cease of plastic deformation can be understood with the concept of stress shadow used in the field of seismic activity. The separated strain rates are about 30 nanostrain/yr both for the plastic deformation in the preseismic period and for the viscous deformation in both the pre- and post-seismic periods, which means that the inelastic strain rate in the northern NKTZ is about 60 and 30 nanostrain/yr in the pre- and post-seismic periods, respectively. This result requires the revision of the strain rate paradox in Japan. The strain rate was exceptionally faster before the Tohoku-oki earthquake due to the effect of plastic strain, and the discrepancy between the geodetic and geologic strain rates is much smaller in usual time, when the plastic strain is off. In oder to understand the onset timing of plastic deformation, the information on stress history is essentially important.</p><p> </p>

scholarly journals Crustal strain-rate fields estimated from GNSS data with a Bayesian approach and its correlation to seismic activity in Mainland China

2021 ◽  
pp. 229003
Author(s):  
Ziyao Xiong ◽  
Jiancang Zhuang ◽  
Shiyong Zhou ◽  
Mitsuhiro Matsu'ura ◽  
Ming Hao ◽  
...  
Keyword(s):  
Strain Rate ◽  
Bayesian Approach ◽  
Seismic Activity ◽  
Mainland China ◽  
Crustal Strain ◽  
Gnss Data

Study the Effect of Radio Interference of the EHV Transmission Line on GNSS Signals

2013 ◽  
Vol 805-806 ◽  
pp. 851-854
Author(s):  
Zhi Ge Jia ◽  
Zhao Sheng Nie ◽  
Wei Wang ◽  
Xiao Guan ◽  
Di Jin Wang
Keyword(s):  
Transmission Line ◽  
Transmission Lines ◽  
Internal Noise ◽  
Field Testing ◽  
Satellite System ◽  
Field Analysis ◽  
Radio Interference ◽  
Gnss Receiver ◽  
Global Navigation Satellite ◽  
Gnss Data

This work describes the field testing process of Global Navigation Satellite System (GNSS) receiver under 220KV, 500KV UHV transmission line and standard calibration field. Analysis for GNSS data results shows that the radio interference generated by EHV transmission lines have no effect on GNSS receiver internal noise levels and valid GNSS observation rate. Within 50 meters of the EHV transmission lines, the multi-path effects (mp1 and mp2 value) significantly exceeded the normal range and becomes larger with the increase of the voltage .outside 50 meters of the EHV transmission line, the multi-path effects have almost no effect on the high-precision GNSS observations.

Second-hand housing batch evaluation model of zhengzhou city based on big data and MGWR model

2021 ◽  
pp. 1-20
Author(s):  
Chaojie Liu ◽  
Jie Lu ◽  
Wenjing Fu ◽  
Zhuoyi Zhou
Keyword(s):  
Real Estate ◽  
Geographically Weighted Regression ◽  
Euclidean Distance ◽  
Evaluation Model ◽  
Housing Prices ◽  
Information Criterion ◽  
Access Point ◽  
Least Square ◽  
Weighted Regression ◽  
Spatial Error

How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03–0.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model.

scholarly journals GNSS-Acoustic Observations of Seafloor Crustal Deformation Using a Wave Glider

2021 ◽  
Vol 9 ◽  
Author(s):  
Takeshi Iinuma ◽  
Motoyuki Kido ◽  
Yusaku Ohta ◽  
Tatsuya Fukuda ◽  
Fumiaki Tomita ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Crustal Deformation ◽  
Satellite Communication ◽  
Satellite System ◽  
Japan Trench ◽  
Research Vessel ◽  
Solar Panels ◽  
Wave Glider ◽  
Human Costs ◽  
Global Navigation Satellite

Crustal deformation of the seafloor is difficult to observe solely using global navigation satellite system (GNSS). The GNSS-acoustic (GNSS-A) technique was developed to observe seafloor crustal deformation, and it has produced a steady series of successful observations with remarkable results related to crustal deformation associated with huge earthquakes around the Japanese Islands. However, utilizing GNSS-A incurs very large financial and human costs as it requires the use of a research vessel as a surface platform and has a limited observation frequency, which is less than once a year at seafloor stations along the Japan Trench. To conduct frequent observations, an automatic GNSS-A data acquisition system was developed that operates via an unmanned surface vehicle (wave glider). The first observations using this system were performed at a seafloor station off Aomori Prefecture in July 2019. The wave glider was equipped with two GNSS antennas, an acoustic transducer, a microelectromechanical system gyroscope, and associated control and logging units. Data acquisition and autonomous activation of the seafloor stations were successfully executed by controlling the power supply to the payload via satellite communication with the wave glider. The glider rarely strayed off the configured course and the solar panels generated sufficient power to perform the observations although the weather was mostly cloudy. The GNSS-A data processing results show that the position of the station was determined with the same accuracy and precision as in previous observations performed using a research vessel.

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