Related Experiment Video
Updated: May 18, 2026
08:45
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
Published on: October 24, 2012
Trans-dimensional matched-field geoacoustic inversion with hierarchical error models and interacting Markov chains
1School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia V8W 3P6, Canada. jand@uvic.ca
The Journal of the Acoustical Society of America
|October 9, 2012
Summary
This study introduces a flexible geoacoustic inversion method using a trans-dimensional approach and hierarchical models. It improves efficiency and provides more realistic uncertainty estimates for seabed parameters by relaxing prior assumptions.
Area of Science:
- Oceanography
- Geophysics
- Acoustics
Background:
- Geoacoustic inversion is crucial for understanding the seabed.
- Traditional methods often require strong prior assumptions about seabed structure and data errors.
- Improving the realism of uncertainty estimates in geoacoustic inversion remains a challenge.
Purpose of the Study:
- To develop a trans-dimensional approach for matched-field geoacoustic inversion.
- To incorporate interacting Markov chains for improved computational efficiency.
- To address correlated data errors using a hierarchical autoregressive error model.
Main Methods:
- A trans-dimensional approach allowing unknown seabed parametrization (e.g., number of sediment layers).
- Hierarchical seabed and error models to account for correlated errors (covariance).
- Interacting Markov chains to overcome low acceptance rates in trans-dimensional jumps.
Main Results:
- Substantially increased efficiency in geoacoustic inversion through interacting Markov chains.
- Relaxed prior assumptions, leading to more realistic seabed parameter uncertainty estimates.
- Demonstrated application to real-world acoustic data from the Mediterranean Sea.
Conclusions:
- The developed trans-dimensional geoacoustic inversion approach offers a more automated and robust method.
- It provides improved uncertainty quantification by accounting for model choice and data error statistics.
- This method enhances the reliability of seabed characterization from acoustic data.
