Articles | Volume 12, issue 3
Nonlin. Processes Geophys., 12, 373–379, 2005
https://doi.org/10.5194/npg-12-373-2005
Nonlin. Processes Geophys., 12, 373–379, 2005
https://doi.org/10.5194/npg-12-373-2005

  01 Mar 2005

01 Mar 2005

Deeper understanding of non-linear geodetic data inversion using a quantitative sensitivity analysis

C. Tiede1, K. Tiampo2, J. Fernández3, and C. Gerstenecker1 C. Tiede et al.
  • 1Department of Civil Engineering and Geodesy, Institute of Physical Geodesy, Darmstadt, University of Technology, Petersenstr. 13, 64287 Darmstadt, Germany
  • 2Department of Earth Sciences, University of Western Ontario, London, ON, Canada.
  • 3Instituto de Astronomia y Geodesia (CSIC-UCM), Facultad de Ciencias Matemáticas, Ciudad Universitaria, Pza. de Ciencias 3, 28040 Madrid, Spain

Abstract. A quantitative global sensitivity analysis (SA) is applied to the non-linear inversion of gravity changes and displacement data which measured in an active volcanic area. The common inversion of this data is based on the solution of the generalized Navier equations which couples both types of observation, gravity and displacement, in a homogeneous half space. The sensitivity analysis has been carried out using Sobol's variance-based approach which produces the total sensitivity indices (TSI), so that all interactions between the unknown input parameters are taken into account. Results of the SA show quite different sensitivities for the measured changes as they relate to the unknown parameters for the east, north and height component, as well as the pressure, radial and mass component of an elastic-gravitational source. The TSIs are implemented into the inversion in order to stabilize the computation of the unknown parameters, which showed wide dispersion ranges in earlier optimization approaches. Samples which were computed using a genetic algorithm (GA) optimization are compared to samples in which the results of the global sensitivity analysis are integrated by a reweighting of the cofactor matrix in the objective function. The comparison shows that the implementation of the TSI's can decrease the dispersion rate of unknown input parameters, producing a great improvement the reliable determination of the unknown parameters.