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Stan E. Dosso

Bio: Stan E. Dosso is an academic researcher from University of Victoria. The author has contributed to research in topics: Seabed & Geology. The author has an hindex of 33, co-authored 245 publications receiving 3904 citations. Previous affiliations of Stan E. Dosso include Victoria University, Australia & University of British Columbia.


Papers
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Journal ArticleDOI
TL;DR: Comparison of FGS, GS, and Monte Carlo integration for noisy synthetic benchmark test cases indicates that FGS provides rigorous estimates of PPD moments while requiring orders of magnitude less computation time.
Abstract: This paper develops a new approach to estimating seabed geoacoustic properties and their uncertainties based on a Bayesian formulation of matched-field inversion. In Bayesian inversion, the solution is characterized by its posterior probability density (PPD), which combines prior information about the model with information from an observed data set. To interpret the multi-dimensional PPD requires calculation of its moments, such as the mean, covariance, and marginal distributions, which provide parameter estimates and uncertainties. Computation of these moments involves estimating multi-dimensional integrals of the PPD, which is typically carried out using a sampling procedure. Important goals for an effective Bayesian algorithm are to obtain efficient, unbiased sampling of these moments, and to verify convergence of the sample. This is accomplished here using a Gibbs sampler (GS) approach based on the Metropolis algorithm, which also forms the basis for simulated annealing (SA). Although GS can be computationally slow in its basic form, just as modifications to SA have produced much faster optimization algorithms, the GS is modified here to produce an efficient algorithm referred to as the fast Gibbs sampler (FGS). An automated convergence criterion is employed based on monitoring the difference between two independent FGS samples collected in parallel. Comparison of FGS, GS, and Monte Carlo integration for noisy synthetic benchmark test cases indicates that FGS provides rigorous estimates of PPD moments while requiring orders of magnitude less computation time.

214 citations

Journal ArticleDOI
TL;DR: An adaptive hybrid algorithm to invert ocean acoustic field measurements for seabed geoacoustic parameters is presented, employing an adaptive approach to control the trade off between random variation and gradient-based information in the inversion.
Abstract: 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.

140 citations

Journal ArticleDOI
TL;DR: This paper develops a general trans-dimensional Bayesian methodology for geoacoustic inversion that results in environmental estimates that quantify appropriate seabed structure as supported by the data, allowing sharp discontinuities while approximating smooth transitions where needed.
Abstract: This paper develops a general trans-dimensional Bayesian methodology for geoacoustic inversion. Trans-dimensional inverse problems are a generalization of fixed-dimensional inversion that includes the number and type of model parameters as unknowns in the problem. By extending the inversion state space to multiple subspaces of different dimensions, the posterior probability density quantifies the state of knowledge regarding inversion parameters, including effects due to limited knowledge about appropriate parametrization of the environment and error processes. The inversion is implemented here using a reversible-jump Markov chain Monte Carlo algorithm and the seabed is parametrized using a partition model. Unknown data errors are addressed by including a data-error model. Jumps between dimensions are implemented with a birth–death methodology that allows transitions between dimensions by adding or removing interfaces while maintaining detailed balance in the Markov chain. Trans-dimensional inversion results in an inherently parsimonious solution while partition modeling provides a naturally self-regularizing algorithm based on data information content, not on subjective regularization functions. Together, this results in environmental estimates that quantify appropriate seabed structure as supported by the data, allowing sharp discontinuities while approximating smooth transitions where needed. This approach applies generally to geoacoustic inversion and is illustrated here with seabed reflection-coefficient data.

128 citations

Journal ArticleDOI
TL;DR: In this paper, a method for estimating properties of the ocean bottom such as bathymetry and geoacoustic parameters such as sound speed, density and attenuation, using matched-field inversion is considered.
Abstract: A method for estimating properties of the ocean bottom such as bathymetry and geoacoustic parameters such as sound speed, density and attenuation, using matched-field inversion is considered. The inversion can be formulated as an optimization problem by assuming a discrete model of unknown parameters and a bounded search space for each parameter. The optimization then involves finding the set of parameter values which minimizes the mismatch between the measured acoustic field and modeled replica fields. Since the number of possible models can be extremely large, the method of simulated annealing, which provides an efficient optimization that avoids becoming trapped in suboptimal solutions, has been used. The matching fields are computed using a normal mode model. In inversions for range-dependent parameters, the adiabatic approximation is employed. This allows mode values to be precomputed for a grid of parameter values and stored in look-up tables for fast reference, which greatly improves computational efficiency. Synthetic inversion examples are presented for realistic range-independent and range-dependent environments. >

124 citations

Journal ArticleDOI
TL;DR: The hybrid inversion is found to be faster by more than an order of magnitude for a benchmark testcase in which the form of the geoacoustic model is known and an underparameterized approach is employed to determine a minimum-structure solution.
Abstract: In this paper, local, global, and hybrid inversion algorithms are developed and applied to the problem of determining geoacoustic properties by minimizing the mismatch between measured and modeled acoustic fields. Local inversion methods are sensitive to gradients in the mismatch and move effectively downhill, but generally become trapped in local minima and must be initiated from a large number of starting points. Global inversion methods use a directed random process to search the parameter space for the optimal solution. They include the ability to escape from local minima, but as no gradient information is used, the search can be relatively inefficient. Hybrid inversion methods combine local and global approaches to produce a more efficient and effective algorithm. Here, downhill simplex (local) and simulated annealing (global) methods are developed individually and combined to produce a hybrid simplex simulated annealing algorithm. The hybrid inversion is found to be faster by more than an order of magnitude for a benchmark testcase in which the form of the geoacoustic model is known. The hybrid inversion algorithm is also applied to a testcase consisting of an unknown number of layers representing a general geoacoustic profile. Since the form of the model is not known, an underparameterized approach is employed to determine a minimum-structure solution.

121 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, a derivative-free search method for finding models of acceptable data fit in a multidimensional parameter space is presented, which falls into the same class of method as simulated annealing and genetic algorithms, which are commonly used for global optimization problems.
Abstract: SUMMARY This paper presents a new derivative-free search method for finding models of acceptable data fit in a multidimensional parameter space. It falls into the same class of method as simulated annealing and genetic algorithms, which are commonly used for global optimization problems. The objective here is to find an ensemble of models that preferentially sample the good data-fitting regions of parameter space, rather than seeking a single optimal model. (A related paper deals with the quantitative appraisal of the ensemble.) The new search algorithm makes use of the geometrical constructs known as Voronoi cells to derive the search in parameter space. These are nearest neighbour regions defined under a suitable distance norm. The algorithm is conceptually simple, requires just two ‘tuning parameters’, and makes use of only the rank of a data fit criterion rather than the numerical value. In this way all diYculties associated with the scaling of a data misfit function are avoided, and any combination of data fit criteria can be used. It is also shown how Voronoi cells can be used to enhance any existing direct search algorithm, by intermittently replacing the forward modelling calculations with nearest neighbour calculations. The new direct search algorithm is illustrated with an application to a synthetic problem involving the inversion of receiver functions for crustal seismic structure. This is known to be a non-linear problem, where linearized inversion techniques suVer from a strong dependence on the starting solution. It is shown that the new algorithm produces a sophisticated type of ‘self-adaptive’ search behaviour, which to our knowledge has not been demonstrated in any previous technique of this kind.

1,336 citations

Journal ArticleDOI
TL;DR: This article showed that anthropogenic perturbation may have increased the flux of carbon to inland waters by as much as 1.0 Pg C yr−1 since pre-industrial times, mainly owing to enhanced carbon export from soils.
Abstract: A substantial amount of the atmospheric carbon taken up on land through photosynthesis and chemical weathering is transported laterally along the aquatic continuum from upland terrestrial ecosystems to the ocean. So far, global carbon budget estimates have implicitly assumed that the transformation and lateral transport of carbon along this aquatic continuum has remained unchanged since pre-industrial times. A synthesis of published work reveals the magnitude of present-day lateral carbon fluxes from land to ocean, and the extent to which human activities have altered these fluxes. We show that anthropogenic perturbation may have increased the flux of carbon to inland waters by as much as 1.0 Pg C yr−1 since pre-industrial times, mainly owing to enhanced carbon export from soils. Most of this additional carbon input to upstream rivers is either emitted back to the atmosphere as carbon dioxide (~0.4 Pg C yr−1) or sequestered in sediments (~0.5 Pg C yr−1) along the continuum of freshwater bodies, estuaries and coastal waters, leaving only a perturbation carbon input of ~0.1 Pg C yr−1 to the open ocean. According to our analysis, terrestrial ecosystems store ~0.9 Pg C yr−1 at present, which is in agreement with results from forest inventories but significantly differs from the figure of 1.5 Pg C yr−1 previously estimated when ignoring changes in lateral carbon fluxes. We suggest that carbon fluxes along the land–ocean aquatic continuum need to be included in global carbon dioxide budgets.

948 citations

Book
07 Aug 1995
TL;DR: In this paper, the authors present a mathematical model of a GA multimodal fitness function, genetic drift, GA with sharing, and repeat (parallel) GA uncertainty estimates evolutionary programming -a variant of GA.
Abstract: Part 1 Preliminary statistics: random variables random nunmbers probability probability distribution, distribution function and density function joint and marginal probability distributions mathematical expectation, moments, variances and covariances conditional probability Monte Carlo integration importance sampling stochastic processes Markov chains homogeneous, inhomogeneous, irreducible and aperiodic Markov chains the limiting probability. Part 2 Direct, linear and iterative-linear inverse methods: direct inversion methods model based inversion methods linear/linearized inverse methods iterative linear methods for quasi-linear problems Bayesian formulation solution using probabilistic formulation. Part 3 Monte Carlo methods: enumerative or grid search techniques Monte Carlo inversion hybrid Monte Carlo-linear inversion directed Monte Carlo methods. Part 4 Simulated annealing methods: metropolis algorithm heat bath algorithm simulated annealing without rejected moves fast simulated annealing very fast simulated reannealing mean field annealing using SA in geophysical inversion. Part 5 Genetic algorithms: a classical GA schemata and the fundamental theorem of genetic algorithms problems combining elements of SA into a new GA a mathematical model of a GA multimodal fitness functions, genetic drift, GA with sharing, and repeat (parallel) GA uncertainty estimates evolutionary programming - a variant of GA. Part 6 Geophysical applications of SA and GA: 1-D seismic waveform inversion pre-stack migration velocity estimation inversion of resistivity sounding data for 1-D earth models inversion of resistivity profiling data for 2-D earth models inversion of magnetotelluric sounding data for 1-D earth models stochastic reservoir modelling seismic deconvolution by mean field annealing and Hopfield network. Part 7 Uncertainty estimation: methods of numerical integration simulated annealing - the Gibbs' sampler genetic algorithm - the parallel Gibbs' sampler numerical examples.

710 citations

Journal ArticleDOI
TL;DR: The development and application of Monte Carlo methods for inverse problems in the Earth sciences and in particular geophysics are traced from the earliest work of the Russian school and the pioneering studies in the west by Press [1968] to modern importance sampling and ensemble inference methods.
Abstract: [1] Monte Carlo inversion techniques were first used by Earth scientists more than 30 years ago. Since that time they have been applied to a wide range of problems, from the inversion of free oscillation data for whole Earth seismic structure to studies at the meter-scale lengths encountered in exploration seismology. This paper traces the development and application of Monte Carlo methods for inverse problems in the Earth sciences and in particular geophysics. The major developments in theory and application are traced from the earliest work of the Russian school and the pioneering studies in the west by Press [1968] to modern importance sampling and ensemble inference methods. The paper is divided into two parts. The first is a literature review, and the second is a summary of Monte Carlo techniques that are currently popular in geophysics. These include simulated annealing, genetic algorithms, and other importance sampling approaches. The objective is to act as both an introduction for newcomers to the field and a comprehensive reference source for researchers already familiar with Monte Carlo inversion. It is our hope that the paper will serve as a timely summary of an expanding and versatile methodology and also encourage applications to new areas of the Earth sciences.

648 citations