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  • 1
    Monograph available for loan
    Monograph available for loan
    Global optimization methods in geophysical inversion (2013)
    Cambridge : Cambridge Univ. Press
    Details Availability
    Call number: M 13.0143
    Description / Table of Contents: Contents: 1. Preliminary statistics; 2. Direct, linear, and iterative-linear inverse methods; 3. Monte Carlo methods; 4. Simulated annealing methods; 5. Genetic algorithms; 6. Other global optimization methods; 7. Geophysical applications of SA and GA; 8. Uncertainty estimation
    Type of Medium: Monograph available for loan
    Pages: VII, 289 S. : Ill., graph. Darst.
    Edition: 2nd ed.
    ISBN: 9781107011908
    Classification:
    Geophysical Deep Sounding
    Location: Upper compact magazine
    Branch Library: GFZ Library
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  • 2
    Electronic Resource
    Electronic Resource
    Application of Very Fast Simulated Annealing to the Determination of the Crustal Structure Beneath Tibet (1996)
    Oxford, UK : Blackwell Publishing Ltd
    Geophysical journal international 125 (1996), S. 0 
    Details
    ISSN: 1365-246X
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Geosciences
    Notes: The simulated annealing method has recently been applied to several multiparameter optimization problems, including those of geophysical inversion. A new variant of simulated annealing, called very fast simulated annealing (VFSA), overcomes some of the drawbacks of a conventional simulated annealing; it has been found to be a practical tool for geophysical inversion (Sen & Stoffa 1995). the method is particularly useful for non-linear problems with multiple parameters where a grid-search method is prohibitively expensive. Here, we have applied VFSA to retrieve the crustal structure beneath seismic stations in Tibet using teleseismic body-waveform data. Our approach is to compare the radial-component records with generalized ray synthetics directly in the time domain. For any given crustal structure, we have formulated a direct relationship between the radial- and the vertical-component signal, so that we can generate synthetic radial-component data from recorded vertical-component seismograms (Zhao & Frohlich 1996). We have tested the VFSA inversion algorithm using synthetics and confirmed that it works very well, i.e. it finds solutions very close to the true solution. Using the algorithm, we have determined the crustal structures beneath 11 stations in Tibet. From south to north, the total crustal thickness is quite uniform. Our tectonic interpretation of these crustal models is that they may represent crust from the Eurasian plate injected beneath the crust of the converging Indian plate. Certain features of the models are consistent with the presence of a small convection cell or plume beneath north-central Tibet, as suggested by Molnar (1990).
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1365-246X.1996.tb00004.x
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  • 3
    Electronic Resource
    Electronic Resource
    Multifold phase space path integral synthetic seismograms (1991)
    Oxford, UK : Blackwell Publishing Ltd
    Geophysical journal international 104 (1991), S. 0 
    Details
    ISSN: 1365-246X
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Geosciences
    Notes: We consider a medium consisting of homogeneous layers separated by curved interfaces. In order to evaluate the response of a single generalized ray, the source and receiver wavefields are expanded in a series of plane waves. The coupling of these plane waves at each point of the surfaces of material discontinuity is determined by means of a Kirchhoff integral using generalized reflection and transmission coefficients. The resulting integral, called the multifold phase space path integral (PSPI) consists of a series of integrals over ray parameters and over interfaces touched by the generalized ray on its way from the source to the receiver. This approach is a generalization of the multifold configuration space path integral (CSPI) to which it reduces by successive application of the stationary phase point method over the ray-parameter integrals.The PSPI like the CSPI automatically includes diffractions from corners. In addition classical head waves are included, although for curved interfaces the head waves are only approximate. 2-D synthetic seismograms are converted to equivalent approximate point-source responses by assuming cylindrical symmetry about source and/or receiver. The waveforms and amplitude of PSPI synthetic seismograms compare well with those computed by generalized ray theory for a 1-D model, and with finite difference synthetics for a 2-D model.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1365-246X.1991.tb05695.x
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  • 4
    Electronic Resource
    Electronic Resource
    Rapid sampling of model space using genetic algorithms: examples from seismic waveform inversion (1992)
    Oxford, UK : Blackwell Publishing Ltd
    Geophysical journal international 108 (1992), S. 0 
    Details
    ISSN: 1365-246X
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Geosciences
    Notes: The seismic waveform inversion problem is usually cast into the framework of Bayesian statistics in which prior information on the model parameters is combined with the data and physics of the forward problem to estimate the a posteriori probability density (PPD) in model space. The PPD is a function of an objective or fitness function computed from the observed and synthetic data. In general, the PPD or the fitness function is multimodal and its shape is unknown. Global optimization methods such as simulated annealing (SA) and genetic algorithms (GA) do not require that the shape of the fitness function be known. In this paper, we investigate GA to rapidly sample the most significant portion or portions of the PPD, when very little prior information is available. First, we use a simple three operator (selection, crossover and mutation) GA acting on a randomly chosen finite population of haploid binary coded models. We use plane wave transformed synthetic seismic data and a normalized cross-correlation function [E(m)] in the frequency domain as a fitness function. A moderate value of crossover probability, a low value of mutation probability, a high value of update probability and a proper population size are required to reach very close to the global maximum of the fitness function. Next, with an attempt to accelerate convergence we show that the concepts from simulated annealing can be used in stretching of the fitness function, i.e., we use exp [E(m)/T] rather than E(m) as the fitness function, where T is a control parameter analogous to temperature in simulated annealing. By a schemata analysis, we show that at low temperatures, schemata with above average fitness values are reproduced in large numbers causing a much more rapid convergence of the algorithm. A high value of temperature T assigns nearly equal selection probability to most of the schemata and thus retains diversity among the members of the population. Thus a GA with a step function type cooling schedule (very high temperature in the beginning followed by rapid cooling to a very low temperature) improves the performance dramatically: high values of the fitness function are obtained rapidly using only half as many models as would be required by a conventional GA. Similar performance could also be achieved by first using a high mutation probability and then decreasing the mutation probability to a very low value, while retaining the same low temperature throughout.We also address the problem of ‘genetic drift’ which causes the finite GAs to converge to one peak or the other when the algorithm is applied to a highly multimodal fitness function with several peaks of nearly the same height. A parallel genetic algorithm based on the concept of ‘punctuated equilibria’ is implemented to circumvent the problem. We run several GAs each with a finite subpopulation in parallel and collect many good models from each one of these runs. These are then used to grasp the most significant portion(s) of the PPD in model space. We then compute the weighted mean model and use the derived good models to estimate uncertainty in the derived model parameters.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1365-246X.1992.tb00857.x
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  • 5
    Electronic Resource
    Electronic Resource
    Non-linear inversion of resistivity profiling data for some regular geometrical bodies (1995)
    Oxford, UK : Blackwell Publishing Ltd
    Geophysical prospecting 43 (1995), S. 0 
    Details
    ISSN: 1365-2478
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Geosciences , Physics
    Notes: The inversion of resistivity profiling data involves estimation of the spatial distribution of resistivities and thicknesses of rock layers from the apparent resistivity data values measured in the field as a function of electrode separation. The drawbacks of using traditional curve-matching techniques to solve this inverse problem have been overcome by iterative linear techniques but these require good starting models even if the shape of the causative body is asssumed known. In spite of the recent developments in inversion techniques, no robust method exists for the inversion of resistivity profiling data for the simple model of dikes and spheres which are the classical models of geophysical prospecting.We apply three different non-linear inversion schemes to invert synthetic resistivity profiling data for the classical models embedded in a uniform matrix of contrasting resistivity. The three non-linear algorithms used are called the Metropolis simulated annealing (SA), very fast simulated annealing (VFSA) and a genetic algorithm (GA). We compare the performance of the three algorithms using synthetic data for an outcropping vertical dike model. Although all three methods were successful in obtaining optimal solutions for arbitrary starting models, VFSA proved to be computationally the most efficient.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1365-2478.1995.tb00292.x
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  • 6
    Electronic Resource
    Electronic Resource
    Bayesian inference, Gibbs' sampler and uncertainty estimation in geophysical inversion (1996)
    Oxford, UK : Blackwell Publishing Ltd
    Geophysical prospecting 44 (1996), S. 0 
    Details
    ISSN: 1365-2478
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Geosciences , Physics
    Notes: The posterior probability density function (PPD), σ(m|dobs), of earth model m, where dobs are the measured data, describes the solution of a geophysical inverse problem, when a Bayesian inference model is used to describe the problem. In many applications, the PPD is neither analytically tractable nor easily approximated and simple analytic expressions for the mean and variance of the PPD are not available. Since the complete description of the PPD is impossible in the highly multi-dimensional model space of many geophysical applications, several measures such as the highest posterior density regions, marginal PPD and several orders of moments are often used to describe the solutions. Calculation of such quantities requires evaluation of multidimensional integrals. A faster alternative to enumeration and blind Monte-Carlo integration is importance sampling which may be useful in several applications. Thus how to draw samples of m from the PPD becomes an important aspect of geophysical inversion such that importance sampling can be used in the evaluation of these multi-dimensional integrals. Importance sampling can be carried out most efficiently by a Gibbs' sampler (GS). We also introduce a method which we called parallel Gibbs' sampler (PGS) based on genetic algorithms (GA) and show numerically that the results from the two samplers are nearly identical.We first investigate the performance of enumeration and several sampling based techniques such as a GS, PGS and several multiple maximum a posteriori (MAP) algorithms for a simple geophysical problem of inversion of resistivity sounding data. Several non-linear optimization methods based on simulated annealing (SA), GA and some of their variants can be devised which can be made to reach very close to the maximum of the PPD. Such MAP estimation algorithms also sample different points in the model space. By repeating these MAP inversions several times, it is possible to sample adequately the most significant portion(s) of the PPD and all these models can be used to construct the marginal PPD, mean) covariance, etc. We observe that the GS and PGS results are identical and indistinguishable from the enumeration scheme. Multiple MAP algorithms slightly underestimate the posterior variances although the correlation values obtained by all the methods agree very well. Multiple MAP estimation required 0.3% of the computational effort of enumeration and 40% of the effort of a GS or PGS for this problem. Next, we apply GS to the inversion of a marine seismic data set to quantify uncertainties in the derived model, given the prior distribution determined from several common midpoint gathers.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1365-2478.1996.tb00152.x
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  • 7
    Electronic Resource
    Electronic Resource
    Background velocity estimation using non-linear optimization for reflection tomography and migration misfit (1998)
    Oxford BSL : Blackwell Science Ltd
    Geophysical prospecting 46 (1998), S. 0 
    Details
    ISSN: 1365-2478
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Geosciences , Physics
    Notes: We show that it is possible to estimate the background velocity for prestack depth migration in 2D laterally varying media using a non-linear optimization technique called very fast simulated annealing (VFSA). We use cubic splines in the velocity model parametrization and make use of either successive pairs of shot gathers or several constant-offset sections as input data for the inversion. A Kirchhoff summation scheme based on first-arrival traveltimes is used to migrate/model the input data during the velocity analysis. We evaluate and compare two different measures of error. The first is defined in the recorded data or (x,t) domain and is based on a reflection-tomography criterion. The second is defined in the migrated data or (x,z) domain and is based on a migration-misfit criterion. Depth relaxation is used to improve the convergence and quality of the velocity analysis while simultaneously reducing the computational cost. Further, we show that by coarse sampling in the offset domain the method is still robust.Our non-linear optimization approach to migration velocity analysis is evaluated for both synthetic and real seismic data. For the velocity-analysis method based on the reflection-tomography criterion, traveltimes do not have to be picked. Similarly, the migration-misfit criterion does not require that depth images be manually compared. Interpreter intervention is required only to restrict the search space used in the velocity-analysis problem. Extension of the proposed schemes to 3D models is straightforward but practical only for the fastest available computers.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1046/j.1365-2478.1998.720308.x
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  • 8
    Electronic Resource
    Electronic Resource
    Use of VFSA for resolution, sensitivity and uncertainty analysis in 1D DC resistivity and IP inversion (2003)
    PO Box 1354, 9600 Garsington Road , Oxford OX4 2XG , UK . : Blackwell Publishing Ltd
    Geophysical prospecting 51 (2003), S. 0 
    Details
    ISSN: 1365-2478
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Geosciences , Physics
    Notes: We present results from the resolution and sensitivity analysis of 1D DC resistivity and IP sounding data using a non-linear inversion. The inversion scheme uses a theoretically correct Metropolis–Gibbs' sampling technique and an approximate method using numerous models sampled by a global optimization algorithm called very fast simulated annealing (VFSA). VFSA has recently been found to be computationally efficient in several geophysical parameter estimation problems. Unlike conventional simulated annealing (SA), in VFSA the perturbations are generated from the model parameters according to a Cauchy-like distribution whose shape changes with each iteration. This results in an algorithm that converges much faster than a standard SA. In the course of finding the optimal solution, VFSA samples several models from the search space. All these models can be used to obtain estimates of uncertainty in the derived solution. This method makes no assumptions about the shape of an a posteriori probability density function in the model space. Here, we carry out a VFSA-based sensitivity analysis with several synthetic and field sounding data sets for resistivity and IP. The resolution capability of the VFSA algorithm as seen from the sensitivity analysis is satisfactory. The interpretation of VES and IP sounding data by VFSA, incorporating resolution, sensitivity and uncertainty of layer parameters, would generally be more useful than the conventional best-fit techniques.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1046/j.1365-2478.2003.00379.x
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  • 9
    Electronic Resource
    Electronic Resource
    Artificial neural networks for parameter estimation in geophysics [ Received ] (2000)
    Oxford BSL : Blackwell Science Ltd
    Geophysical prospecting 48 (2000), S. 0 
    Details
    ISSN: 1365-2478
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Geosciences , Physics
    Notes: Artificial neural systems have been used in a variety of problems in the fields of science and engineering. Here we describe a study of the applicability of neural networks to solving some geophysical inverse problems. In particular, we study the problem of obtaining formation resistivities and layer thicknesses from vertical electrical sounding (VES) data and that of obtaining 1D velocity models from seismic waveform data. We use a two-layer feedforward neural network (FNN) that is trained to predict earth models from measured data. Part of the interest in using FNNs for geophysical inversion is that they are adaptive systems that perform a non-linear mapping between two sets of data from a given domain. In both of our applications, we train FNNs using synthetic data as input to the networks and a layer parametrization of the models as the network output. The earth models used for network training are drawn from an ensemble of random models within some prespecified parameter limits. For network training we use the back-propagation algorithm and a hybrid back-propagation–simulated-annealing method for the VES and seismic inverse problems, respectively. Other fundamental issues for obtaining accurate model parameter estimates using trained FNNs are the size of the training data, the network configuration, the description of the data and the model parametrization. Our simulations indicate that FNNs, if adequately trained, produce reasonably accurate earth models when observed data are input to the FNNs.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1046/j.1365-2478.2000.00171.x
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  • 10
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    Waveform modelling of teleseismicS, Sp, SsPmP, and shear-coupled PL waves for crust- and upper-mantle velocity structure beneath Africa (2007)
    Oxford University Press
    Details
    Publication Date: 2007-09-01
    Print ISSN: 0956-540X
    Electronic ISSN: 1365-246X
    Topics: Geosciences
    Published by Oxford University Press on behalf of The Royal Astronomical Society.
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