Efficient Bayesian geoacoustic inversion using mixture density networksa).

Area of Science:

• Geophysics
• Ocean Acoustics
• Computational Science

Background:

• Bayesian seabed geoacoustic inversion is crucial for understanding subsurface properties.
• Traditional Markov chain Monte Carlo (McMC) methods are computationally intensive and time-consuming.

Purpose of the Study:

• To develop an efficient Bayesian geoacoustic inversion approach using mixture density networks (MDNs).
• To model the joint posterior probability distribution of all geoacoustic parameters simultaneously.
• To analytically deduce geoacoustic statistics from the posterior probability density (PPD).

Main Methods:

• Utilized mixture density networks (MDNs) to model the joint posterior probability distribution of geoacoustic parameters.
• Employed MDN theory to analytically derive geoacoustic statistics, avoiding numerical integration.
• Compared MDN inversion results with traditional McMC methods across various scenarios.

Main Results:

• The MDN approach effectively models the joint posterior probability distribution of geoacoustic parameters.
• Analytical deduction of statistics from MDN-enhanced PPD provides deeper insights and avoids computationally expensive integration.
• MDN inversion results show good agreement with McMC trends, capturing inter-parameter correlations and trade-offs.

Conclusions:

• Mixture density networks offer an efficient and insightful alternative for Bayesian seabed geoacoustic inverse problems.
• This approach shows promise for real-time geoacoustic inversion applications.
• MDNs can effectively capture complex relationships and trade-offs between geoacoustic parameters.