Gravity Gradiometer Survey System (GGSS) Post-Mission Data Processing

Abstract

Post-mission processing of local airborne gravity gradient measurements simultaneously in a computationally efficient manner is addressed. The gravity signal model is obtained by solving Laplace's equation with the unknown mass distribution below the surface of the earth modelled as one or more two-dimensional (2D) white noise layers representing the vertical derivative of the disturbance potential to any order. A non-stationary non-isotropic disturbance potential covariance is obtained by invoking the Karhunen-Loeve condition on the unknown coefficients of the series solution. 2D grids of the six gravity gradients contaminated by additive white noise is the measurement model. For gaussian noise statistics closed form solutions of the continuous domain optimal estimator for each Karhunen-Loeve coefficient is obtained as a linear functional of all the measurement data. Utilization of toeplitz circulant properties of sine and cosine transforms permits discretization of the continuous algorithm as a sequence of matrix multiplications without any necessity of matrix inversions. Form the Karhunen-Loeve coefficient estimates, 2D grid estimates of the gravity vector components at any altitude and at any grid spacing are obtained by performing appropriate left and right sine and cosine transforms. Keywords: Gravity gradiometry; Post mission processing; Mass distribution layers; Laplace's equation; Non stationary non isotropic covariance.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1987
Accession Number
ADA200739

Entities

People

  • Sam C. Bose

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Accuracy
  • Computational Science
  • Data Processing
  • Differential Equations
  • Errors
  • Estimators
  • Filtration
  • Frequency Domain
  • Image Processing
  • Integral Equations
  • Long Wavelengths
  • Mathematical Filters
  • Measurement
  • Optimal Estimators
  • Partial Differential Equations
  • Random Variables
  • Two Dimensional

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Marine Hydrodynamics
  • Statistical inference.

Technology Areas

  • Space