Benoist et al., 2020 - Google Patents
Accounting for spatiotemporal correlations of GNSS coordinate time series to estimate station velocitiesBenoist et al., 2020
View PDF- Document ID
- 14852534347414579214
- Author
- Benoist C
- Collilieux X
- Rebischung P
- Altamimi Z
- Jamet O
- Métivier L
- Chanard K
- Bel L
- Publication year
- Publication venue
- Journal of Geodynamics
External Links
Snippet
It is well known that GNSS permanent station coordinate time series exhibit time-correlated noise. Spatial correlations between coordinate time series of nearby stations are also long- established and generally handled by means of spatial filtering techniques. Accounting for …
- 238000000034 method 0 abstract description 50
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/30—Noise handling
- G01V2210/32—Noise reduction
- G01V2210/322—Trace stacking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/003—Seismic data acquisition in general, e.g. survey design
- G01V1/005—Seismic data acquisition in general, e.g. survey design with exploration systems emitting special signals, e.g. frequency swept signals, pulse sequences or slip sweep arrangements
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V99/00—Subject matter not provided for in other groups of this subclass
- G01V99/005—Geomodels or geomodelling, not related to particular measurements
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Benoist et al. | Accounting for spatiotemporal correlations of GNSS coordinate time series to estimate station velocities | |
| Bagnardi et al. | Inversion of surface deformation data for rapid estimates of source parameters and uncertainties: A Bayesian approach | |
| García-Pintado et al. | Scheduling satellite-based SAR acquisition for sequential assimilation of water level observations into flood modelling | |
| Lohman et al. | Some thoughts on the use of InSAR data to constrain models of surface deformation: Noise structure and data downsampling | |
| Cao et al. | Advanced InSAR tropospheric corrections from global atmospheric models that incorporate spatial stochastic properties of the troposphere | |
| Maubant et al. | Independent component analysis and parametric approach for source separation in InSAR time series at regional scale: Application to the 2017–2018 slow slip event in Guerrero (Mexico) | |
| Cavalié et al. | Ground motion measurement in the Lake Mead area, Nevada, by differential synthetic aperture radar interferometry time series analysis: Probing the lithosphere rheological structure | |
| EP3866105B1 (en) | Method for processing insar images to extract ground deformation signals | |
| Dong et al. | Spatiotemporal filtering using principal component analysis and Karhunen‐Loeve expansion approaches for regional GPS network analysis | |
| Kusche et al. | Decorrelated GRACE time-variable gravity solutions by GFZ, and their validation using a hydrological model | |
| Kurtenbach et al. | Improved daily GRACE gravity field solutions using a Kalman smoother | |
| Rui et al. | A geodetic strain rate and tectonic velocity model for China | |
| Xu et al. | Monte Carlo SSA to detect time-variable seasonal oscillations from GPS-derived site position time series | |
| Chen et al. | ARU-net: Reduction of atmospheric phase screen in SAR interferometry using attention-based deep residual U-net | |
| Nickles et al. | How does the unique space‐time sampling of the SWOT mission influence river discharge series characteristics? | |
| Watson et al. | An InSAR‐GNSS velocity field for Iran | |
| Wang et al. | Stochastic filtering for determining gravity variations for decade‐long time series of GRACE gravity | |
| Raynaud et al. | Spatial averaging of ensemble‐based background‐error variances | |
| Cao et al. | Mapping ground displacement by a multiple phase difference-based InSAR approach: With stochastic model estimation and turbulent troposphere mitigation | |
| Hasan et al. | Comparison of decadal water storage trends from common GRACE releases (RL05, RL06) using spatial diagnostics and a modified triple collocation approach | |
| Liu et al. | Mitigating atmospheric delays in InSAR time series: the DetrendInSAR method and its validation | |
| Scott et al. | Sensitivity of earthquake source inversions to atmospheric noise and corrections of InSAR data | |
| Guillet et al. | Bayesian estimation of glacier surface elevation changes from DEMs | |
| Hang et al. | Outlier-insensitive Bayesian inference for linear inverse problems (OutIBI) with applications to space geodetic data | |
| Hu et al. | Spatiotemporal interpolation of precipitation across Xinjiang, China using space-time CoKriging |