Tabu for matched-field source localization and geoacoustic inversion

Michalopoulou, Zoi‐Heleni; Ghosh-Dastidar, Urmi

doi:10.1121/1.1635408

Cited by 28 publications

(13 citation statements)

References 31 publications

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“…Tabu search has traditionally been applied to combinational optimization problems [149,150]. The tabu search begins by marching to a local minimum.…”

Section: Optimization Techniquesmentioning

confidence: 99%

Advanced Applications for Underwater Acoustic Modeling

Etter

2012

Advances in Acoustics and Vibration

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Changes in the ocean soundscape have been driven by anthropogenic activity (e.g., naval-sonar systems, seismic-exploration activity, maritime shipping and windfarm development) and by natural factors (e.g., climate change and ocean acidification). New regulatory initiatives have placed additional restrictions on uses of sound in the ocean: mitigation of marine-mammal endangerment is now an integral consideration in acoustic-system design and operation. Modeling tools traditionally used in underwater acoustics have undergone a necessary transformation to respond to the rapidly changing requirements imposed by this new soundscape. Advanced modeling techniques now include forward and inverse applications, integrated-modeling approaches, nonintrusive measurements, and novel processing methods. A 32-year baseline inventory of modeling techniques has been updated to reflect these new developments including the basic mathematics and references to the key literature. Charts have been provided to guide soundscape practitioners to the most efficient modeling techniques for any given application.

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“…Tabu search has traditionally been applied to combinational optimization problems [149,150]. The tabu search begins by marching to a local minimum.…”

Section: Optimization Techniquesmentioning

confidence: 99%

Advanced Applications for Underwater Acoustic Modeling

Etter

2012

Advances in Acoustics and Vibration

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“…The function q(z) contains information about the SSP in the ocean sediment and S(μ) can be recovered using an experiment which is described in the following section. That is, (8) provides the solution to the inverse problem. Using data S(μ) and moving through a sound propagation model, we estimate a profile q(z).…”

Section: B the Inverse Problemmentioning

confidence: 99%

“…In several of the references above as well as in other works, regardless of whether inversion was performed relying on the complete field or part of it, numerical approaches were employed for the global search of the typically highdimensional parameter space and the calculation of implicit multiple integrals [3]- [8], [11]- [13], [16]. A different family of methods is local optimization based [17]- [22].…”

Section: Introductionmentioning

confidence: 99%

“…MFI has produced very good results in both synthetic and real data cases; however, it is computationally intensive. Global optimization techniques have been successful in accelerating matched-field methods [3]- [8], but, still, the computational cost of MFI remains a challenging problem. A factor that is both an advantage and disadvantage for MFI is that the complete field is exploited in the inversion process.…”

Section: Introductionmentioning

confidence: 99%

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A Direct Method for the Estimation of Sediment Sound Speed With a Horizontal Array in Shallow Water

Lin

Michalopoulou

2016

IEEE J. Oceanic Eng.

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In this paper, a fast approach for estimating sediment sound speed in a shallow-water environment is developed. Under certain assumptions, this algorithm recovers the seabed sound-speed profile using pressure field measurements at low frequencies. The geometry of the problem involves measuring the pressure at horizontally placed hydrophones in the water column. The Deift-Trubowitz integral equation is then solved. This work introduces two methods for this task. The first is a modified Born approximation that builds upon a standard first-order approximation; the second is based on interpolating the integrand. It is shown with synthetic data that these methods work well with successful sound-speed estimation and identification of sharp discontinuities in sound speed. Although the methods are stable and effective with noise-free data, problems arise when noise contaminates the acoustic field. Regularization approaches, reducing the disruptive effect of singular points and smoothing a measured reflection coefficient, are developed to remedy this problem, leading to improved results in noisy environments. In addition to providing soundspeed estimates, the method also computes sediment thickness. This feature is of particular interest, since it makes the method suitable as a preprocessing step providing useful information to other inversion methods. Sensitivity analyses demonstrate that some assumptions required for the approach implementation are not restrictive.Index Terms-Direct methods, geoacoustic inversion, sediment sound speed.

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“…Instead, the literature appears to treat other problems with elegant approaches where some of the variables are known without error ͑such as some or all of the receiver coordinates͒ and/or some linear approximation is adopted. 12,[15][16][17][18][19][20] The approach taken here has its root in a method for estimating the distribution of an animal's location given realistic a priori estimates for the distributions of sound speed, receiver locations, and measurement error. 9 We explain how that approach can be generalized to estimate the distributions of all the variables and how to include dynamical models for the evolution of all variables between the receptions of different animal calls.…”

Section: Introductionmentioning

confidence: 99%

Probability distributions for locations of calling animals, receivers, sound speeds, winds, and data from travel time differences

Spiesberger

2005

The Journal of the Acoustical Society of America

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A new nonlinear sequential Monte Carlo technique is used to estimate posterior probability distributions for the location of a calling animal, the locations of acoustic receivers, sound speeds, winds, and the differences in sonic travel time between pairs of receivers from measurements of those differences, while adopting realistic prior distributions of the variables. Other algorithms in the literature appear to be too inefficient to yield distributions for this large number of variables (up to 41) without recourse to a linear approximation. The new technique overcomes the computational inefficiency of other algorithms because it does not sequentially propagate the joint probability distribution of the variables between adjacent data. Instead, the lower and upper bounds of the distributions are propagated. The technique is applied to commonly encountered problems that were previously intractable such as estimating how accurately sound speed and poorly known initial locations of receivers can be estimated from the differences in sonic travel time from calling animals, while explicitly modeling distributions of all the variables in the problem. In both cases, the new technique yields one or two orders of magnitude improvements compared with initial uncertainties. A new nonlinear sequential Monte Carlo technique is used to estimate posterior probability distributions for the location of a calling animal, the locations of acoustic receivers, sound speeds, winds, and the differences in sonic travel time between pairs of receivers from measurements of those differences, while adopting realistic prior distributions of the variables. Other algorithms in the literature appear to be too inefficient to yield distributions for this large number of variables ͑up to 41͒ without recourse to a linear approximation. The new technique overcomes the computational inefficiency of other algorithms because it does not sequentially propagate the joint probability distribution of the variables between adjacent data. Instead, the lower and upper bounds of the distributions are propagated. The technique is applied to commonly encountered problems that were previously intractable such as estimating how accurately sound speed and poorly known initial locations of receivers can be estimated from the differences in sonic travel time from calling animals, while explicitly modeling distributions of all the variables in the problem. In both cases, the new technique yields one or two orders of magnitude improvements compared with initial uncertainties. The technique is suitable for accurately estimating receiver locations from animal calls.

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Tabu for matched-field source localization and geoacoustic inversion

Cited by 28 publications

References 31 publications

Advanced Applications for Underwater Acoustic Modeling

Advanced Applications for Underwater Acoustic Modeling

A Direct Method for the Estimation of Sediment Sound Speed With a Horizontal Array in Shallow Water

Probability distributions for locations of calling animals, receivers, sound speeds, winds, and data from travel time differences

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