Difference between pages "JSG T.29" and "JSG T.32"

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(Created page with "<big>'''JSG 0.16: Earth’s inner structure from combined geodetic and geophysical sources'''</big> Chairs: ''Robert Tenzer (China)''<br> Affiliation: ''Comm. 2 and 3'' __TO...")
 
 
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<big>'''JSG 0.16: Earth’s inner structure from combined geodetic and geophysical sources'''</big>
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<big>'''JSG 0.19: Time series analysis in geodesy'''</big>
  
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Chairs: ''Robert Tenzer (China)''<br>
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Chair: ''Wieslaw Kosek (Poland)''<br>
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Affiliation: ''Comm. 2 and 3''
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Affiliation:''Comm. 3 and GGOS''
  
 
__TOC__
 
__TOC__
  
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===Introduction===
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===Terms of Reference===
  
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The satellite gravimetry missions, CHAllenging Mini-satellite Payload (CHAMP), the GRavity field and Climate Experiment (GRACE) and the Gravity field and steady-state Ocean Circulation Explorer (GOCE), significantly improved our knowledge on the external gravitational field of the Earth at the long-to-medium wavelengths (approximately up to a spherical harmonic degree of 250). Such improved information in terms of the accuracy and resolution has been utilized in studies of the Earth’s interior for a better understanding of the Earth’s inner structure and processes occurring within the lithosphere and sub-lithospheric mantle. Whereas the long-wavelength spectrum of the Earth’s gravitational field comprises mainly the signature of deep mantle density heterogeneities attributed to mantle convection, the medium wavelengths reflect the density structure of more shallow sources within the lithosphere. This allows studying and interpreting in more detail the gravitational features which are related to the global tectonism (including the oceanic subduction, orogenic formations, earthquakes, global lithospheric plate configuration, etc.), sub-lithospheric stresses, isostatic mechanisms, glacial isostatic adjustment, and other related geodynamic phenomena. Moreover, the Global Gravitational Models (GGMs) have been extensively used in studies of the lithospheric density structure and density interfaces such as for the gravimetric recovery of the Moho depth, lithospheric thickness as well as structure of sedimentary basins.  
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Observations of the space geodesy techniques and on the Earth's surface deliver a global picture of the Earth dynamics represented in the form of time series which describe 1) changes of the Earth surface geometry, 2) the fluctuations in the Earth orientation, and 3) the variations of the Earth’s gravitational field. The Earth's surface geometry, rotation and gravity field are the three components of the Global Geodetic Observing System (GGOS) which integrates them into one unique physical and mathematical model. However, temporal variations of these three components represent the total, integral effect of all global mass exchange between all elements of the Earth’s system including the Earth's interior and fluid layers:  atmosphere, ocean and land hydrology.
  
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Since the gravity observations could not be used alone to interpret the Earth’s inner density structure due to a non-uniqueness of inverse solutions (i.e. infinity many 3-D density structures could be attributed to the Earth’s gravity field), additional information is required to constrain the gravimetric methods for interpreting the Earth’s interior. These constraining data comprise primarily results of seismic surveys as well as additional geophysical, geothermal and geochemical parameters of the Earth. Moreover, numerous recent gravimetric studies of the Earth’s interior focus on the global and regional Moho recovery. The classical isostatic models (according to Airy and Pratt theories) are typically not able to model realistically the actual Moho geometry, due to the fact that the isostatic mass balance depends on loading and effective elastic thickness, rigidity, rheology of the lithosphere and viscosity of the asthenosphere. Moreover, geodynamic processes such as the glacial isostatic adjustment, present-day glacial melting, plate motion and mantle convection contribute to the time-dependent isostatic balance. To overcome these issues, processing strategies of combining gravity and seismic data (and possibly also additional constraining information) have to be applied to determine the actual Moho geometry.  
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Different time series analysis methods have been applied to analyze all these geodetic time series for better understanding of the relations between all elements of the Earth’s system as well as their geophysical causes. The interactions between different components of the Earth’s system are very complex so the nature of considered signals in the geodetic time series is mostly wideband, irregular and non-stationary. Thus, it is recommended to apply wavelet based spectra-temporal analysis methods to analyze these geodetic time series as well as to explain their relations to geophysical processes in different frequency bands using time-frequency semblance and coherence methods. These spectra-temporal analysis methods and time-frequency semblance and coherence may be further developed to display reliably the features of the temporal or spatial variability of signals existing in various geodetic data, as well as in other source data sources.
  
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The gravimetric methods applied in studies of the Earth’s inner density structure comprise - in principle - two categories. The methods for the gravimetric forward modeling are applied to model (and remove) the gravitational signature of known density structures in order to enhance the gravitational contribution of unknown (and sought) density structures and interfaces. The gravimetric inverse methods are then used to interpret these unknown density structures from the refined gravity data. It is obvious that the combination of gravity and seismic data (and other constraining information) is essential especially in solving the gravimetric inverse problems.  
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Geodetic time series include for example horizontal and vertical deformations of site positions determined from observations of space geodetic techniques. These site positions change due to e.g. plate tectonics, postglacial rebound, atmospheric, hydrology and ocean loading and earthquakes. However they are used to build the global international terrestrial reference frame (ITRF) which must be stable reference for all other geodetic observations including e.g. satellite orbit parameters and Earth's orientation parameters which consist of precession, nutation, polar motion and UT1-UTC that are necessary for transformation between the terrestrial and celestial reference frames. Geodetic time series include also temporal variations of Earth's gravity field where 1 arc-deg spherical harmonics correspond to the Earth’s centre of mass variations (long term mean of them determines the ITRF origin) and 2 degree spherical harmonics correspond to Earth rotation changes. Time series analysis methods can be also applied to analyze data on the Earth's surface including maps of the gravity field, sea level, ice covers, ionospheric total electron content and tropospheric delay as well as temporal variations of such surface data. The main problems to deal with include the estimation of deterministic (including trend and periodic variations) and stochastic (non-periodic variations and random changes) components of the geodetic time series as well as the application of digital filters for extracting specific components with a chosen frequency bandwidth.
  
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This gives us the platform and opportunities towards improving the theoretical and numerical methods applied in studies of Earth’s interior from multiple data sources, primarily focusing but not restricting only to combining gravimetric and seismic data. It is expected that the gravity data could improve our knowledge of the Earth’s interior over significant proportion of the world where seismic data are sparse or completely absent (such large parts of oceanic areas, Antarctica, Greenland and Africa). The gravity data could also provide additional information on the lithospheric structure and mechanisms, such as global tectonic configuration, geometry of subducted slabs, crustal thickening of orogenic formations and other phenomena.
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The multiple methods of time series analysis may be encouraged to be applied to the preprocessing of raw data from various geodetic measurements in order to promote the quality level of enhancement of signals existing in these data. The topic on the improvement of the edge effects in time series analysis may also be considered, since they may affect the reliability of long-range tendency (trends) estimated from data series as well as the real-time data processing and prediction.
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For coping with small geodetic samples one can apply simulation-based methods and if the data are sparse, Monte-Carlo simulation or bootstrap technique may be useful. Understanding the nature of geodetic time series is very important from the point of view of appropriate spectral analysis as well as application of filtering and prediction methods.
  
 
===Objectives===
 
===Objectives===
  
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* Development of the theoretical and numerical algorithms for combined processing of gravity, seismic and other types of geophysical data for a recovery of the Earth’s density structures and interfaces.
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* Study of the nature of geodetic time series to choose optimum time series analysis methods for filtering, spectral analysis, time frequency analysis and prediction.
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* Development of fast numerical algorithms for combined data inversions.
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* Study of Earth's geometry, rotation and gravity field variations and their geophysical causes in different frequency bands.
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* Development of stochastic models for combined inversion including optimal weighting, regularization and spectral filtering.
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* Evaluation of appropriate covariance matrices for the time series by applying the law of error propagation to the original measurements, including weighting schemes, regularization, etc.
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* Better understanding of uncertainties of interpreted results based on the error analysis of input data and applied numerical models. Geophysical and geodynamic clarification of results and their uncertainties.
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* Determination of the statistical significance levels of the results obtained by different time series analysis methods and algorithms applied to geodetic time series.
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* Recommendations for optimal data combinations, better understanding of possibilities and limiting factors associated with individual data types used for geophysical and geodynamic interpretations.
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* Development and comparison of different time series analysis methods in order to point out their advantages and disadvantages.
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* Recommendations of different time series analysis methods for solving problems concerning specific geodetic time series.
  
 
===Program of activities===
 
===Program of activities===
  
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* Launching of a web page with emphasis on exchange of ideas and recent progress, providing and updating bibliographic list of references of research results and relevant publications from different disciplines.
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* Launching of a website about time series analysis in geodesy providing list of papers from different disciplines as well as unification of terminology applied in time series analysis.
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* Work progress meetings at the international symposia and presentation of research results at the appropriate sessions.
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* Working meetings at the international symposia and presentation of research results at the appropriate sessions.
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* Possible collaboration between various geoscience study groups dealing with the modeling of the Earth’s interior and related scientific topics.  
 
  
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===Members===
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===Membership===
  
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'' '''Robert Tenzer (China), chair''' <br /> Lars Sjöberg (Sweden) <br /> Mohammad Bagherbandi (Sweden) <br /> Carla Braitenberg (Italy) <br /> Mehdi Eshagh (Sweden) <br /> Mirko Reguzzoni (Italy) <br /> Xiaodong Song (USA) <br />''
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'' '''Wieslaw Kosek (Poland), chair''' <br /> Michael Schmidt (Germany) <br /> Jan Vondrák (Czech Republic) <br /> Waldemar Popinski (Poland) <br /> Tomasz Niedzielski (Poland) <br /> Johannes Boehm (Austria) <br /> Dawei Zheng (China) <br /> Yonghong Zhou (China) <br /> Mahmut O. Karslioglu (Turkey) <br /> Orhan Akyilmaz (Turkey) <br /> Laura Fernandez (Argentina) <br /> Richard Gross (USA) <br /> Olivier de Viron (France) <br /> Sergei Petrov (Russia) <br /> Michel Van Camp (Belgium) <br /> Hans Neuner (Germany) <br /> Xavier Collilieux (France) <br />''

Revision as of 10:10, 29 April 2016

JSG 0.19: Time series analysis in geodesy

Chair: Wieslaw Kosek (Poland)
Affiliation:Comm. 3 and GGOS

Terms of Reference

Observations of the space geodesy techniques and on the Earth's surface deliver a global picture of the Earth dynamics represented in the form of time series which describe 1) changes of the Earth surface geometry, 2) the fluctuations in the Earth orientation, and 3) the variations of the Earth’s gravitational field. The Earth's surface geometry, rotation and gravity field are the three components of the Global Geodetic Observing System (GGOS) which integrates them into one unique physical and mathematical model. However, temporal variations of these three components represent the total, integral effect of all global mass exchange between all elements of the Earth’s system including the Earth's interior and fluid layers: atmosphere, ocean and land hydrology.

Different time series analysis methods have been applied to analyze all these geodetic time series for better understanding of the relations between all elements of the Earth’s system as well as their geophysical causes. The interactions between different components of the Earth’s system are very complex so the nature of considered signals in the geodetic time series is mostly wideband, irregular and non-stationary. Thus, it is recommended to apply wavelet based spectra-temporal analysis methods to analyze these geodetic time series as well as to explain their relations to geophysical processes in different frequency bands using time-frequency semblance and coherence methods. These spectra-temporal analysis methods and time-frequency semblance and coherence may be further developed to display reliably the features of the temporal or spatial variability of signals existing in various geodetic data, as well as in other source data sources.

Geodetic time series include for example horizontal and vertical deformations of site positions determined from observations of space geodetic techniques. These site positions change due to e.g. plate tectonics, postglacial rebound, atmospheric, hydrology and ocean loading and earthquakes. However they are used to build the global international terrestrial reference frame (ITRF) which must be stable reference for all other geodetic observations including e.g. satellite orbit parameters and Earth's orientation parameters which consist of precession, nutation, polar motion and UT1-UTC that are necessary for transformation between the terrestrial and celestial reference frames. Geodetic time series include also temporal variations of Earth's gravity field where 1 arc-deg spherical harmonics correspond to the Earth’s centre of mass variations (long term mean of them determines the ITRF origin) and 2 degree spherical harmonics correspond to Earth rotation changes. Time series analysis methods can be also applied to analyze data on the Earth's surface including maps of the gravity field, sea level, ice covers, ionospheric total electron content and tropospheric delay as well as temporal variations of such surface data. The main problems to deal with include the estimation of deterministic (including trend and periodic variations) and stochastic (non-periodic variations and random changes) components of the geodetic time series as well as the application of digital filters for extracting specific components with a chosen frequency bandwidth.

The multiple methods of time series analysis may be encouraged to be applied to the preprocessing of raw data from various geodetic measurements in order to promote the quality level of enhancement of signals existing in these data. The topic on the improvement of the edge effects in time series analysis may also be considered, since they may affect the reliability of long-range tendency (trends) estimated from data series as well as the real-time data processing and prediction.

For coping with small geodetic samples one can apply simulation-based methods and if the data are sparse, Monte-Carlo simulation or bootstrap technique may be useful. Understanding the nature of geodetic time series is very important from the point of view of appropriate spectral analysis as well as application of filtering and prediction methods.

Objectives

  • Study of the nature of geodetic time series to choose optimum time series analysis methods for filtering, spectral analysis, time frequency analysis and prediction.
  • Study of Earth's geometry, rotation and gravity field variations and their geophysical causes in different frequency bands.
  • Evaluation of appropriate covariance matrices for the time series by applying the law of error propagation to the original measurements, including weighting schemes, regularization, etc.
  • Determination of the statistical significance levels of the results obtained by different time series analysis methods and algorithms applied to geodetic time series.
  • Development and comparison of different time series analysis methods in order to point out their advantages and disadvantages.
  • Recommendations of different time series analysis methods for solving problems concerning specific geodetic time series.

Program of activities

  • Launching of a website about time series analysis in geodesy providing list of papers from different disciplines as well as unification of terminology applied in time series analysis.
  • Working meetings at the international symposia and presentation of research results at the appropriate sessions.

Membership

Wieslaw Kosek (Poland), chair
Michael Schmidt (Germany)
Jan Vondrák (Czech Republic)
Waldemar Popinski (Poland)
Tomasz Niedzielski (Poland)
Johannes Boehm (Austria)
Dawei Zheng (China)
Yonghong Zhou (China)
Mahmut O. Karslioglu (Turkey)
Orhan Akyilmaz (Turkey)
Laura Fernandez (Argentina)
Richard Gross (USA)
Olivier de Viron (France)
Sergei Petrov (Russia)
Michel Van Camp (Belgium)
Hans Neuner (Germany)
Xavier Collilieux (France)

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