Related Experiment Videos
Data error covariance in matched-field geoacoustic inversion.
Stan E Dosso1, Peter L Nielsen, Michael J Wilmut
1School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia V8W 3P6, Canada. sdosso@uvic.ca
The Journal of the Acoustical Society of America
|February 4, 2006
Summary
Geoacoustic inversion accuracy improves by accounting for correlated data errors. This study develops a method to estimate and include the full residual covariance matrix, enhancing parameter estimation and uncertainty quantification in acoustic data analysis.
Area of Science:
- Oceanography
- Acoustics
- Geophysics
Background:
- Traditional geoacoustic inversion methods often assume uncorrelated data errors, which can be inaccurate.
- Ignoring spatial correlations in data residuals leads to less precise geoacoustic parameter estimates and underestimates uncertainty.
Purpose of the Study:
- To investigate the impact of data error covariance on matched-field geoacoustic inversion.
- To develop and validate a novel inversion approach that incorporates full residual covariance.
Main Methods:
- A nonparametric method was used to estimate the complete data residual covariance matrix, including off-diagonal terms.
- The estimated covariance matrix was explicitly incorporated into the misfit function for Bayesian geoacoustic inversion.
- Statistical tests were employed to assess Gaussianity and correlations within the residuals.
Main Results:
- The developed approach effectively accounts for spatial correlations in data errors.
- Incorporating full covariance improved the accuracy of geoacoustic parameter estimates.
- Uncertainty quantification for geoacoustic parameters was more reliable.
Conclusions:
- Accounting for non-diagonal data error covariance is crucial for accurate geoacoustic inversion.
- The nonparametric estimation of the full covariance matrix provides a robust method for improving inversion results.
- This technique enhances the reliability of geoacoustic models derived from acoustic data.