An adaptive-hybrid algorithm for geoacoustic inversion

This paper presents an adaptive hybrid algorithm to invert ocean acoustic field measurements for seabed geoacoustic parameters. The inversion combines a global search (simulated annealing) and a local method (downhill simplex), employing an adaptive approach to control the trade off between random variation and gradient-based information in the inversion. The result is an efficient and effective algorithm that successfully navigates challenging parameter spaces including large numbers of local minima, strongly correlated parameters, and a wide range of parameter sensitivities. The algorithm is applied to a set of benchmark test cases, which includes inversion of simulated measurements with and without noise, and cases where the model parameterization is known and where the parameterization most be determined as part of the inversion. For accurate data, the adaptive inversion often produces a model with a Bartlett mismatch lower than the numerical error of the propagation model used to compute the replica fields. For noisy synthetic data, the inversion produces a model with a mismatch that is lower than that for the true parameters. Comparison with previous inversions indicates that the adaptive hybrid method provides the best results to date for the benchmark cases.

[1]  Stan E. Dosso,et al.  Linear and non-linear appraisal using extremal models of bounded variation , 1989 .

[2]  M. Snellen,et al.  Multi-Frequency Matched-Field Inversion of Benchmark Data using a Genetic Algorithm , 1998 .

[3]  A. Tolstoy,et al.  MFP benchmark inversions via the rigs method. , 1998 .

[4]  David P. Knobles,et al.  The Inversion of Ocean Waveguide Parameters Using a Nonlinear Least Squares Approach , 1998 .

[5]  N. R. Chapman,et al.  A hybrid simplex genetic algorithm for estimating geoacoustic parameters using matched-field inversion , 1999 .

[6]  James F. Lynch,et al.  Perturbative inversion methods for obtaining bottom geoacoustic parameters in shallow water , 1987 .

[7]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[8]  L. Ingber.  Very fast simulated re-annealing , 1989 .

[9]  Peter Gerstoft,et al.  Inversion of seismoacoustic data using genetic algorithms and a posteriori probability distributions , 1994 .

[10]  H. Szu.  Fast simulated annealing , 1987 .

[11]  W. Menke.  Geophysical data analysis : discrete inverse theory , 1984 .

[12]  Peter Gerstoft,et al.  Broadband Geoacoustic Inversion from Sparse Data Using Genetic Algorithms , 1998 .

[13]  Peter Gerstoft,et al.  Subspace Approach to Inversion by Genetic Algorithms Involving Multiple Frequencies , 1998 .

[14]  Michael D. Collins,et al.  Efficient navigation of parameter landscapes , 1994 .

[15]  D. Oldenburg.  An introduction to linear inverse theory , 1984, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Viggo Westerlin.  Multi-Frequency Inversion of Synthetic Transmission Loss Data Using a Genetic Algorithm , 1998 .

[17]  Peter Gerstoft,et al.  Inversion of acoustic data using a combination of genetic algorithms and the Gauss–Newton approach , 1995 .

[18]  Garry J. Heard,et al.  Genetic Algorithm Inversion of the 1997 Geoacoustic Inversion Workshop Test Case Data , 1998 .

[19]  John M. Ozard,et al.  Estimation of Geoacoustic Parameters from Narrowband Data Using a Search-Optimization Technique , 1998 .

[20]  Michael I. Taroudakis,et al.  Bottom Geoacoustic Inversion by "Broadband" Matched-Field Processing , 1998 .

[21]  Olivier Sarzeaud,et al.  Neural Direct Approaches for Geoacoustic Inversion , 1998 .

[22]  Sergio M. Jesus.  Can Maximum Likelihood Estimators Improve Genetic Algorithm Search in Geoacoustic Inversion , 1998 .

[23]  F. James.  Statistical Methods in Experimental Physics , 1973 .

[24]  N. R. Chapman,et al.  Matched field inversion for geoacoustic model parameters using adaptive simulated annealing , 1993 .

[25]  Fenghua Li,et al.  Effects of Source Position and Frequency on Geoacoustic Inversion , 1998 .

[26]  R. Parker,et al.  Occam's inversion; a practical algorithm for generating smooth models from electromagnetic sounding data , 1987 .

[27]  J. Scales,et al.  Regularisation of nonlinear inverse problems: imaging the near-surface weathering layer , 1990 .

[28]  Stan E. Dosso,et al.  Estimation of ocean-bottom properties by matched-field inversion of acoustic field data , 1993 .

[29]  Henrik Schmidt,et al.  Nonlinear inversion for ocean‐bottom properties , 1992 .

[30]  A. Tolstoy,et al.  Using matched-field processing to estimate shallow-water bottom properties from shot data taken in the Mediterranean Sea , 1996 .

[31]  Stan E. Dosso,et al.  GEOACOUSTIC INVERSION VIA LOCAL, GLOBAL, AND HYBRID ALGORITHMS , 1999 .

[32]  Mrinal K. Sen,et al.  Global Optimization Methods in Geophysical Inversion , 1995 .

[33]  Stan E. Dosso,et al.  Geoacoustic Inversion for the Workshop '97 Benchmark Test Cases Using Simulated Annealing , 1998 .

[34]  Evan K. Westwood,et al.  A normal mode model for acousto‐elastic ocean environments , 1996 .