data domain and image domain wavefield tomography waveform inversion fwi requires a good starting model and or data at very low frequency 1 hz for convergence however this is not a necessary condition but an artifact of the objective function defined using differences of observed and simulated data image domain tomographic methods using the same wavefields and wave equations can converge to a reasonable solution from poor starting models and without long offset and or low frequency data cascading image domain and data domain wavefield tomography eliminates the need for extremely low frequency in the acquired data

multiscattering inversion for low model wavenumbers abstracta successful full waveform inversion implementation updates the low wavenumber model components first for a proper description of the wavefield propagation and slowly adds the high wavenumber potentially scattering parts of the model the low wavenumber components can be extracted from the transmission parts of the recorded wavefield emanating directly from the source or the transmission parts from the single or double scattered wavefield computed from a predicted scatter field acting as secondary sources we use a combined inversion of data modeled from the source and those corresponding to single and double scattering to update the velocity model and the component of the velocity perturbation responsible for the single and double scattering the combined inversion helps us access most of the potential model wavenumber information that may be embedded in the data a scattering angle filter is used to divide the gradient of the combined inversion so initially the high wavenumber low scatterin

adaptive subspace detectors we use the theory of generalized likelihood ratio tests glrts to adapt the matched subspace detectors msds of scharf 1991 and of scharf and frielander 1994 to unknown noise covariance matrices in so doing we produce adaptive msds that may be applied to signal detection for radar sonar and data communication we call the resulting detectors adaptive subspace detectors asds these include kelly s 1987 glrt and the adaptive cosine estimator ace of kaurt and scharh see ibid vol 47 p 2538 41 1999 and of scharf and mcwhorter see proc 30th asilomar conf signals syst comput pacific grove ca 1996 for scenarios in which the scaling of the test data may deviate from that of the training data we then present a unified analysis of the statistical behavior of the entire class of asds obtaining statistically identical decompositions in which each asd is simply decomposed into the nonadaptive matched filter the nonadaptive cosine or t statistic and three other statistically independent random variables that account for the performance degrading effects of limited training data

detection of multivariate cyclostationarity this paper derives an asymptotic generalized likelihood ratio test glrt and an asymptotic locally most powerful invariant test lmpit for two hypothesis testing problems 1 is a vector valued random process cyclostationary cs or is it wide sense stationary wss 2 is a vector valued random process cs or is it nonstationary our approach uses the relationship between a scalar valued cs time series and a vector valued wss time series for which the knowledge of the cycle period is required this relationship allows us to formulate the problem as a test for the covariance structure of the observations the covariance matrix of the observations has a block toeplitz structure for cs and wss processes by considering the asymptotic case where the covariance matrix becomes block circulant we are able to derive its maximum likelihood ml estimate and thus an asymptotic glrt moreover using wijsman s theorem we also obtain an asymptotic lmpit these detectors may be expressed in terms of the loeve spectrum the cyclic spectrum and the power spectral density establishing how to fuse the information in these spectra for an asymptotic glrt and lmpit this goes beyond the state of the art where it is common practice to build detectors of cyclostationarity from ad hoc functions of these spectra

migration velocity analysis by wave field extrapolation velocity information is essential to both common midpoint cmp stacking and migration cmp stacking provides the basis for conventional velocity estimation techniques in that for a number of trial velocities the stack response of a cmp gather is computed and displayed in the form of a velocity table an alternative approach to velocity estimation makes use of the basic ingredients of migration downward extrapolation and imaging of seismic wave fields the procedure involves migration of a cmp gather with a number of trial velocities and collection of the zero offset information again in the form of a velocity table operating on a cmp gather the migration based approach produces results similar to those of the conventional method analyses of synthetic cmp gathers using both methods show essentially equivalent treatments of seismic signal and similar dependence of accuracy and resolving power on recording geometry we have extended the migration based approach to include more than one cmp gather in

inversion of seismic reflection data in the acoustic approximation the nonlinear inverse problem for seismic reflection data is solved in the acoustic approximation the method is based on the generalized least squares criterion and it can handle errors in the data set and a priori information on the model multiply reflected energy is naturally taken into account as well as refracted energy or surface waves the inverse problem can be solved using an iterative algorithm which gives at each iteration updated values of bulk modulus density and time source function each step of the iterative algorithm essentially consists of a forward propagation of the actual sources in the current model and a forward propagation backward in time of the data residuals the correlation at each point of the space of the two fields thus obtained yields the corrections of the bulk modulus and density models this shows in particular that the general solution of the inverse problem can be attained by methods strongly related to the methods of migration of unstacked data and commerc

a novel approach for the efficient computation of 1 d and 2 d summations a novel computational method is proposed to evaluate 1 d and 2 d summations and integrals which are relatively difficult to compute numerically the method is based on applying a subspace algorithm to the samples of partial sums and approximating them in terms of complex exponentials for a convergent summation the residue of the exponential term with zero complex pole of this approximation corresponds to the result of the summation since the procedure requires the evaluation of relatively small number of terms the computation time for the evaluation of the summation is reduced significantly in addition by using the proposed method very accurate and convergent results are obtained for the summations which are not even absolutely convergent the efficiency and accuracy of the method are verified by evaluating some challenging 1 d and 2 d summations and integrals

estimation of acoustic macro models using a genetic full waveform inversion applications to the marmousi model abstractwe have developed a stochastic full waveform inversion that uses genetic algorithms ga fwi to estimate acoustic macro models of the p wave velocity field stochastic methods such as ga severely suffer the curse of dimensionality meaning that they require unaffordable computer resources for inverse problems with many unknowns and expensive forward modeling to mitigate this issue we have proposed a two grid technique with a coarse grid to represent the subsurface for the ga inversion and a finer grid for the forward modeling we have applied this procedure to invert synthetic acoustic data of the marmousi model and we have developed three different tests the first two tests use a velocity model derived from standard stacking velocity analysis as prior information and differ only for the parameterization of the coarse grid their comparison indicates that a smart parameterization of the coarse grid may significantly improve the final result the third test uses a linearly increasing 1d velocit

separating function estimation tests a new perspective on binary composite hypothesis testing in this paper we study some relationships between the detection and estimation theories for a binary composite hypothesis test h0 against h1 and a related estimation problem we start with a one dimensional 1d space for the unknown parameter space and one sided hypothesis problems and then extend out results into more general cases for one sided tests we show that the uniformly most powerful ump test is achieved by comparing the minimum variance and unbiased estimator mvue of the unknown parameter with a threshold thus for the case where the ump test does not exist the mvue of the unknown parameter does not exist either therefore for such cases a good estimator of the unknown parameter is deemed as a good decision statistic for the test for a more general class of composite testing with multiple unknown parameters we prove that the mvue of a separating function sf can serve as the optimal decision statistic for the ump unbiased test where the sf is continuous differentiable positive for all parameters under h1 and is negative for the parameters under h0 we then prove that the ump unbiased statistic is equal to the mvue of an sf in many problems with multiple unknown parameters the ump test does not exist for such cases we show that if one detector between two detectors has a better receiver operating characteristic roc curve then using its decision statistic we can estimate the sf more e accurately in probability for example the sf is the signal to noise ratio snr in some problems these results motivate us to introduce new suboptimal sf estimator tests sfets which are easy to derive for many problems finally we provide some practical examples to study the relationship between the decision statistic of a test and the estimator of its corresponding sf

preconditioning techniques for large linear systems a survey this article surveys preconditioning techniques for the iterative solution of large linear systems with a focus on algebraic methods suitable for general sparse matrices covered topics include progress in incomplete factorization methods sparse approximate inverses reorderings parallelization issues and block and multilevel extensions some of the challenges ahead are also discussed an extensive bibliography completes the paper

ir uwb transmitted reference systems with partial channel knowledge a receiver design based on the statistical invariance principle this paper deals with detection algorithms for impulse radio ultrawideband transmitted reference systems for binary modulation schemes we propose a novel detector that operates on the partial autocorrelations of the received signal as computed over time intervals bins of size less than the symbol period assuming that the receiver has knowledge of the partial autocorrelations of the channel response over those bins we derive the minimum error probability detector mepd in doing so we exploit some invariance properties of the detection problem under consideration it turns out that the receiver adopting such a strategy is superior to a conventional tr system the more so the smaller the bin size is also we address the problem of estimating the partial autocorrelations of the channel response and propose a simple solution by exploiting a training sequence the impact of the estimation errors on the receiver performance is shown to be marginal

multiscale seismic waveform inversion iterative inversion methods have been unsuccessful at inverting seismic data obtained from complicated earth models e g the marmousi model the primary difficulty being the presence of numerous local minima in the objective function the presence of local minima at all scales in the seismic inversion problem prevent iterative methods of inversion from attaining a reasonable degree of convergence to the neighborhood of the global minimum the multigrid method is a technique that improves the performance of iterative inversion by decomposing the problem by scale at long scales there are fewer local minima and those that remain are further apart from each other thus at long scales iterative methods can get closer to the neighborhood of the global minimum we apply the multigrid method to a subsampled low frequency version of the marmousi data set although issues of source estimation source bandwidth and noise are not treated results show that iterative inversion methods perform much better when employed with a decomposition by scale furthermore the method greatly reduces the computational burden of the inversion that will be of importance for 3 d extensions to the method

viscoacoustic waveform inversion of velocity structures in the time domain abstractbecause of the conversion of elastic energy into heat seismic waves are attenuated and dispersed as they propagate the attenuation effects can reduce the resolution of velocity models obtained from waveform inversion or even cause the inversion to produce incorrect results using a viscoacoustic model consisting of a single standard linear solid we discovered a theoretical framework of viscoacoustic waveform inversion in the time domain for velocity estimation we derived and found the viscoacoustic wave equations for forward modeling and their adjoint to compensate for the attenuation effects in viscoacoustic waveform inversion the wave equations were numerically solved by high order finite difference methods on centered grids to extrapolate seismic wavefields the finite difference methods were implemented satisfying stability conditions which are also presented numerical examples proved that the forward viscoacoustic wave equation can simulate attenuative behaviors very well in amplitude

multifrequency viscoacoustic modeling and inversion modeling and inversion for seismic wavefields that include the attenuation and phase dispersion effects of q can be implemented in the space frequency domain the viscoacoustic wave equation is solved by the moment method absorbing boundary conditions are implemented by reducing q and adjusting the complex velocity to reduce q dependent reflectivity in a zone around the edges of the model grid nonlinear inversion is performed using iterative linearized inversions the residual wavefield at a single frequency is back projected using an anticausal green s function along the viscoacoustic wavepath in an estimate of the model to get updated velocity and q distributions the model obtained from data at one frequency becomes input to inversion at the next higher frequency velocity and q are inverted simultaneously as they are interdependent both modeling and inversion algorithms are successfully tested with synthetic examples data at two or three frequencies are sufficient to produce reliable images from noise free synthetic data

rational modeling by pencil of functions method pole zero modeling of signals such as an electromagnetic scatterer response is considered in this paper it is shown by use of the pencil of functions theorem that a the true parameters can be recovered in the ideal case where the signal is the impulse reponse of a rational function h z and b the parameters are optimal in the functional dependence sense when the observed data are corrupted by additive noise or by systematic error although the computations are more involved than in all pole modeling they are considerably less than those required in iterative schemes of pole zero modeling the advantages of the method are demonstrated by a simulation example and through application to the electromagnetic response of a scatterer the paper also includes very recent and promising results on a new approach to noise correction in contradistinction with spectral subtraction techniques where only amplitude information is emphasized and phase is ignored we propose a method that a estimates the noise spectral density for the data frame and then b performs the subtraction of the noise correlation matrix from the gram matrix of the signal

quasi newton full waveform inversion with a projected hessian matrix abstractwe present a method in realistic size full waveform inversion fwi to explicitly construct a projected hessian matrix and its inverse matrix which we subsequently used to solve fwi with a quasi newton method newton s method is practically unfeasible in solving realistic size fwi problems because of the prohibitive cost computing time and memory consumption of calculating the hessian matrix and the inverse hessian therefore the gauss newton method and various quasi newton methods are proposed to approximate a hessian matrix particularly current quasi newton fwi qnfwi commonly uses the limited memory bfgs l bfgs method which however only implicitly approximates an inverse hessian we repose fwi as a sparse optimization problem in a sparse model space which contains substantially fewer model parameters that are constrained by structures of the model with respect to fewer parameters in the sparse model we can avoid the limited memory approximation and are able to explicitly comp

fast and accurate mom analysis of periodic arrays of multilayered stacked rectangular patches with application to the design of reflectarray antennas the scattering of plane waves by periodic arrays of stacked rectangular patches in multilayered substrates is a problem that has to be solved many times when designing reflectarray antennas made of those patches under the local periodicity assumption the solution to the periodic multilayered problem has been traditionally carried out by means of the galerkin s version of the method of moments mom in the spectral domain this approach involves the computation of double infinite summations and whereas some of these summations converge very fast some other converge very slowly in this paper the slowly convergent summations are computed by making use of an enhanced mixed potential integral equation mpie formulation of the mom in the spatial domain this enhanced formulation is based on the interpolation of the multilayered periodic green s functions and on the efficient computation of the four dimensional 4 d integrals leading to the mom matrix entries both the novel hybrid spectral spatial mom code and the standard spectral domain mom code have been used for the design of a contoured beam reflectarray antenna it has been verified that the spectral spatial mom code requires cpu times that are typically 30 times smaller than those required by the pure spectral domain mom code

full waveform inversion using seislet regularization abstractbecause of inaccurate incomplete and inconsistent waveform records full waveform inversion fwi in the framework of a local optimization approach may not have a unique solution and thus it remains an ill posed inverse problem to improve the robustness of fwi we have developed a new model regularization approach that enforced the sparsity of solutions in the seislet domain the construction of seislet basis functions requires structural information that can be estimated iteratively from migration images we implement fwi with seislet regularization using nonlinear shaping regularization and impose sparseness by applying soft thresholding on the updated model in the seislet domain at each iteration of the data fitting process the main extra computational cost of the method relative to standard fwi is the cost of applying forward and inverse seislet transforms at each iteration this cost is almost negligible compared with the cost of solving wave equations numerical tests using the syntheti

generalized pencil of function method for extracting poles of an em system from its transient response a generalized pencil of function gpof method is developed for extracting the poles of an electromagnetic system from its transient response the gpof method needs the solution of a generalized eigenvalue problem to find the poles subspace decomposition is also used to optimize the performance of the gpof method the gpof method has advantages over the prony method in both computation and noise sensitivity and approaches the cramer rao bound when the signal to noise ratio snr is above threshold an application of the gpof method to a thin wire target is presented

an improved pencil of functions method and comparisons with traditional methods of pole extraction prony s method is usually used for the determination of poles from experimental data in the singularity expansion method sem literature it is the aim here to show that this is not the best method and that jain s pencil of functions pof method can be generalized to provide a better method an example of such a method is given

image guided sparse model full waveform inversion multiple problems including high computational cost spurious local minima and solutions with no geologic sense have prevented widespread application of full waveform inversion fwi especially fwi of seismic reflections these problems are fundamentally related to a large number of model parameters and to the absence of low frequencies in recorded seismograms instead of inverting for all the parameters in a dense model image guided full waveform inversion inverts for a sparse model space that contains far fewer parameters we represent a model with a sparse set of values and from these values we use image guided interpolation igi and its adjoint operator to compute finely and uniformly sampled models that can fit recorded data in fwi because of this sparse representation image guided fwi updates more blocky models and this blockiness in the model space mitigates the absence of low frequencies in recorded data moreover igi honors imaged structures so image guided fwi built in this way yields models that are geologically sensible

on the relationship between the glrt and umpi tests for the detection of signals with unknown parameters the generalized likelihood ratio test glrt is widely used in signal processing applications such as image processing wireless communications medical imaging classification and signal detection however the glrt does not have many known properties other than that it is invariant uniformly most powerful invariant umpi for problems that fit the linear model and asymptotically n spl rarr spl infin umpi in general since it is invariant it belongs to the class of tests for which the umpi test is optimal in this paper we consider a general class of detection problems in which unknown signal parameters imply a problem invariance that can be described analytically by orthogonal subgroups this invariance is natural for problems with unknown signal parameters and for example include those of the matched subspace detectors of scharf and friedlander we derive the glrt and umpi detectors for this general signal class for the case of gaussian noise an expression is found that relates the two test statistics showing the umpi statistic to be the sum of two terms one of which is the glrt using this we find that the glrt and umpi tests are asymptotically equivalent as signal to noise ratio snr approaches infinity or as probability of false alarm approaches zero these results are illustrated by extending an example given by nicolls and de jager to show the analytic relationship between the glrt and umpi tests the results indicate that the performance between the tests becomes close at signal to noise ratios snrs associated with operating points of the receiver operating curve that are typically of interest in signal detection applications

perturbation analysis of tk method for harmonic retrieval problems the authors present a first order perturbation analysis of the tufts kumaresan tk 1980 method used to estimate frequencies of complex sinusoids in small additive noise several fundamental properties are presented and proved further illustrations are provided through numerical examples

matched subspace detectors we formulate a general class of problems for detecting subspace signals in subspace interference and broadband noise we derive the generalized likelihood ratio glr for each problem in the class we then establish the invariances for the glr and argue that these are the natural invariances for the problem in each case the glr is a maximal invariant statistic and the distribution of the maximal invariant statistic is monotone this means that the glr test glrt is the uniformly most powerful invariant detector we illustrate the utility of this finding by solving a number of problems for detecting subspace signals in subspace interference and broadband noise in each case we give the distribution for the detector and compute performance curves

a maximal invariant framework for adaptive detection with structured and unstructured covariance matrices we introduce a framework for exploring array detection problems in a reduced dimensional space by exploiting the theory of invariance in hypothesis testing this involves calculating a low dimensional basis set of functions called the maximal invariant the statistics of which are often tractable to obtain thereby making analysis feasible and facilitating the search for tests with some optimality property using this approach we obtain a locally most powerful invariant test for the unstructured covariance case and show that all invariant tests can be expressed in terms of the previously published kelly s generalized likelihood ratio glrt and robey s adaptive matched filter amf test statistics applying this framework to structured covariance matrices corresponding to stochastic interferers in a known subspace for which the glrt is unavailable we obtain the maximal invariant and propose several new invariant detectors that are shown to perform as well or better than existing ad hoc detectors these invariant tests are unaffected by most nuisance parameters hence the variation in the level of performance is sharply reduced this framework facilitates the search for such tests even when the usual glrt is unavailable

optimal invariant detection of a sinusoid with unknown parameters a uniformly most powerful test does not exist for the problem of detecting a sinusoid of unknown amplitude phase and frequency in complex white gaussian noise of unknown variance the problem is shown to be invariant to scale and modulation transformations using invariance principles it is found that the uniformly most powerful invariant umpi test does not exist a test is derived that is umpi if it is given that the signal to noise ratio snr is known for snr unknown the derived test can be used as an upper performance bound for any invariant test suboptimal tests are typically implemented such as the generalized likelihood ratio and locally most powerful tests which are also scale and modulation invariant it is shown analytically that as snr approaches zero the locally most powerful invariant test performance approaches the umpi performance bound and as the snr becomes large the generalized likelihood ratio test performance approaches the performance of the umpi bound computer simulation examples indicate that the generalized likelihood ratio test performance is close to the umpi bound particularly in the low probability of false alarm region of the receiver operating characteristic curve

locally most powerful invariant tests for correlation and sphericity of gaussian vectors in this paper we study the existence of locally most powerful invariant tests lmpit for the problem of testing the covariance structure of a set of gaussian random vectors the lmpit is the optimal test for the case of close hypotheses among those satisfying the invariances of the problem and in practical scenarios can provide better performance than the typically used generalized likelihood ratio test glrt the derivation of the lmpit usually requires one to find the maximal invariant statistic for the detection problem and then derive its distribution under both hypotheses which in general is a rather involved procedure as an alternative wijsman s theorem provides the ratio of the maximal invariant densities without even finding an explicit expression for the maximal invariant we first consider the problem of testing whether a set of n dimensional gaussian random vectors are uncorrelated or not and show that the lmpit is given by the frobenius norm of the sample coherence matrix second we study the case in which the vectors under the null hypothesis are uncorrelated and identically distributed that is the sphericity test for gaussian vectors for which we show that the lmpit is given by the frobenius norm of a normalized version of the sample covariance matrix finally some numerical examples illustrate the performance of the proposed tests which provide better results than their glrt counterparts

invariance and optimality of cfar detectors in binary composite hypothesis tests we investigate the relationship between constant false alarm rate cfar and invariant tests we introduce the minimal invariant group mig we show that for a family of distributions the unknown parameters are eliminated from the distribution of the maximal invariant statistic under the mig while the maximum information of the observed signal is preserved we prove that any invariant test with respect to mig is cfar and conversely for any cfar test an invariant statistic exists with respect to an mig under some mild conditions moreover for a given cfar test we propose a systematic method for deriving an enhanced test i e a function of observations exists such that the likelihood ratio lr of the maximal invariant of its mig gives an enhanced test furthermore we introduce the uniformly most powerful cfar ump cfar test as the optimal cfar bound among all cfar tests we then prove that the ump cfar test for the minimally invariant hypothesis testing problem is given by the lr of the maximal invariant under mig for some problems this test mp cfar depends on the unknown parameters of the alternative hypothesis however provides an upper performance bound for all suboptimal cfar tests we also propose three suboptimal novel cfar tests among which one is asymptotically optimal

a novel analysis of microstrip structures using the gaussian green s function method a novel closed form expression is derived for spatial green s functions of microstrip structures by expanding the spectral green s function into a gaussian series this innovative method is called the gaussian green s function ggf method due to the gaussian form in the closed form green s function representation the main advantage of the ggf method lies in its precision as well as rapid convergence to demonstrate the versatility of this method the current distribution of a microstrip antenna is achieved via the combination of the method of moments mom and ggf method it is shown that this method can be computationally very efficient with less than 1 error compared to the numerical integration of the spectral integral also the results of the ggf method have been compared to the results of the commercial full wave software of agilent ads

generalized cfar property and ump invariance for adaptive signal detection in this paper we consider adaptive detection of a signal embedded in additive disturbance whose multivariate distribution belongs to a very general class including many statistical models commonly adopted for radar disturbance we introduce the concept of generalized constant false alarm rate cfar and show that a class of receivers sharing some invariances complies with the quoted property then we devise the generalized likelihood ratio test glrt and prove that under some mild technical conditions it coincides with that obtained under the gaussian assumption for the observations we also deal with the existence of the uniformly most powerful invariant umpi detector either using the wijsman theorem or directly computing the maximal invariant likelihood ratio lr at the analysis stage we focus on a compound matrix variate model for the disturbance component which is a natural generalization of the spherically invariant random vector sirv in this context we assess the performance of some well known invariant decision rules also in comparison with the most powerful invariant mpi detector the results highlight that some among the analyzed receivers exhibit a performance level very close to the mpi test

wave equation tomography the relation between ray trace and diffraction tomography is usually obscured by formulation of the two methods in different domains the former in space the latter in wavenumber here diffraction tomography is reformulated in the space domain under the title of wave equation tomography with this transformation wave equation tomography projects monochromatic scattered wavefields back over source receiver wavepaths just as ray trace tomography projects traveltime delays back over source receiver raypaths derived under the born approximation these wavepaths are wave theoretic back projection patterns for reflected energy derived under the rytov approximation they are wave theoretic back projection patterns for transmitted energy differences between ray trace and wave equation tomography are examined through comparison of wavepaths and raypaths followed by their application to a transmission geometry synthetic data set rytov wave equation tomography proves superior to ray trace tomography in de

an improved immersed boundary finite difference method for seismic wave propagation modeling with arbitrary surface topography abstractto accurately simulate seismic wave propagation for the purpose of developing modern land data processing tools especially full waveform inversion fwi we have developed an efficient high order finite difference forward modeling algorithm with the capability of handling arbitrarily shaped free surface topography unlike most existing forward modeling algorithms using curvilinear grids to fit irregular surface topography this finite difference algorithm based on an improved immersed boundary method uses a regular cartesian grid system without suffering from staircasing error which is inevitable in a conventional finite difference method in this improved immersed boundary finite difference ibfd algorithm arbitrarily curved surface topography is accounted for by imposing the free surface boundary conditions at exact boundary locations instead of using body conforming grids or refined grids near the boundaries thus greatly reducing the complexity of its preprocessing procedures and the com

fundamentals of statistical signal processing estimation theory minimum variance unbiased estimation cramer rao lower bound linear models general minimum variance unbiased estimation best linear unbiased estimators maximum likelihood estimation least squares method of moments the bayesian philosophy general bayesian estimators linear bayesian estimators kalman filters summary of estimators extension for complex data and parameters

image guided regularization of marine electromagnetic inversion abstractmarine electromagnetic em inverse methods have recently been rapidly developed for offshore exploration however inverted resistivity results with low resolution are provided by the em method to improve this quality of the results we have developed an image guided regularization method for inversion of the marine em data the method incorporates seismic constraints into em inversion information is extracted from seismic geologic images and consists of the metric tensor field and sampling on the geologic structure in addition to the regularization geologic horizons picked from the seismic images and samplings on the structure can be used to generate an irregular sparse mesh compared with an unstructured regular dense mesh a coherence based irregular sparse mesh can reduce computational costs furthermore image guided regularization represents an improvement compared with traditional regularization that are structurally based on seismic images by following geologic features more closely a

a hybrid fast hankel transform algorithm for electromagnetic modeling a hybrid fast hankel transform algorithm has been developed that uses several complementary features of two existing algorithms anderson s digital filtering or fast hankel transform fht algorithm and chave s quadrature and continued fraction algorithm a hybrid fht subprogram called hybfht written in standard fortran 77 provides a simple user interface to call either subalgorithm the hybrid approach is an attempt to combine the best features of the two subalgorithms in order to minimize the user s coding requirements and to provide fast execution and good accuracy for a large class of electromagnetic problems involving various related hankel transform sets with multiple arguments special cases of hankel transforms of double order and double argument are discussed where use of hybfht is shown to be advantageous for oscillatory kernel functions

combining an extrapolation technique with the method of moments for solving large scattering problems involving bodies of revolution the purpose of this work is to combine an extrapolation technique with the method of moments mom to solve scattering problems involving large bodies it has been shown in a previous work that the current induced on the smooth parts of large scatterers may be represented as a series of complex exponential functions with a few terms based on this concept a hybrid set of basis functions is constructed using entire domain functions of complex exponential type on the smooth portion of the scatterer complemented by subdomain basis functions near edges and discontinuities an extrapolation procedure is developed in which the scattering problem is first solved for a portion of the scatterer using the conventional mom next a set of entire domain basis functions whose behaviour could be extrapolated with an increase in the size of the scatterer is extracted from this original solution the procedure outlined has the very desirable feature that the total number of basis functions remains unchanged even as the scatterer size is increased allowing for large scatterers to be handled with a relatively small number of unknowns the extrapolation technique is applied to scattering problems from bodies of revolution bors and numerical results for an open cylinder and a barrel shaped bor are presented

maximal invariants and performance of some invariant hypothesis tests for an adaptive detection problem maximal invariants for adaptive detection of a signal in unknown interference from multiple observations is derived given coherent samples from p sets of observations it is shown that a maximal invariant statistic for the detection problem is a 2p 1 dimensional vector comprising the eigenvalues of two hermitian positive definite matrices obtained from the data set two invariant detectors well known for p 1 are generalized for the case of multiple observations and closed form expressions for the probability of detection and probability of false alarm are derived along with the distributions of the signal to interference plus noise loss factors several novel invariant detectors are constructed from the maximal invariants and the receiver operating characteristics of the detectors compared

locally most powerful invariant tests for the properness of quaternion gaussian vectors previous works have addressed the second order statistical characterization of quaternion random vectors introducing different properness definitions and presenting the generalized likelihood ratio tests glrts for determining the kind of quaternion properness this paper considers the more challenging problem of deriving the locally most powerful invariant tests lmpits which can be obtained even without an explicit expression for the maximal invariants thanks to the wijsman s theorem specifically we consider three binary hypothesis testing problems involving the two main kinds of quaternion properness and show that the lmpit statistics are given by the frobenius norm of three previously defined sample coherence matrices the proposed detectors exhibit interesting connections with the problem of testing for the properness of a complex vector and with the problems of testing for the sphericity of a four dimensional real or two dimensional complex proper vector additionally some numerical examples show that in general the proposed lmpits outperform their glrt counterparts and in some cases the performance gap is very noticeable

random distortion testing and optimality of thresholding tests this paper addresses the problem of testing whether the mahalanobis distance between a random signal and a known deterministic model 0 exceeds some given non negative real number or not when has unknown probability distribution and is observed in additive independent gaussian noise with positive definite covariance matrix when is deterministic unknown we prove the existence of thresholding tests on the mahalanobis distance to 0 that have specified level and maximal constant power mcp the mcp property is a new optimality criterion involving wald s notion of tests with uniformly best constant power ubcp on ellipsoids for testing the mean of a normal distribution when the signal is random with unknown distribution constant power maximality extends to maximal constant conditional power mccp and the thresholding tests on the mahalanobis distance to 0 still verify this novel optimality property our results apply to the detection of signals in independent and additive gaussian noise in particular for a large class of possible model mismatches mccp tests can guarantee a specified false alarm probability in contrast to standard neyman pearson tests that may not respect this constraint

comparison of glr and invariant detectors under structured clutter covariance this paper addresses a target detection problem in radar imaging for which the covariance matrix of unknown gaussian clutter has block diagonal structure this block diagonal structure is the consequence of a target lying along a boundary between two statistically independent clutter regions here we design adaptive detection algorithms using both the generalized likelihood ratio glr and the invariance principles there has been considerable interest in applying invariant hypothesis testing as an alternative to the glr test this interest has been motivated by several attractive properties of invariant tests including exact robustness to variation of nuisance parameters and possible finite sample min max optimality however in our deep hide target detection problem there are regimes for which neither the glr nor the invariant tests uniformly outperforms the other we discuss the relative advantages of glr and invariance procedures in the context of this radar imaging and target detection application

application of the matrix pencil method for estimating the sem singularity expansion method poles of source free transient responses from multiple look directions the matrix pencil method has been utilized for estimating the natural resonances from different transient responses recorded along multiple look directions as a function of time after the incident field has passed the structure the novelty of this article is that a single estimate for all the poles are done utilizing multiple transient waveforms emanating from the structure along multiple look directions the sem poles are independent of the angle at which the transient response is recorded the only difference between the various waveforms are that the residues at the various poles are of different magnitudes some of the residues may even be zero for some of the poles indicating that the contribution from certain sem poles may not be significant along that look direction here all the waveforms are utilized providing a single estimate for the poles without performing an arithmetic mean of the various waveforms

an efficient multiscale method for time domain waveform tomography this efficient multiscale method for time domain waveform tomography incorporates filters that are more efficient than hamming window filters a strategy for choosing optimal frequency bands is proposed to achieve computational efficiency in the time domain a staggered grid explicit finite difference method with fourth order accuracy in space and second order accuracy in time is used for forward modeling and the adjoint calculation the adjoint method is utilized in inverting for an efficient computation of the gradient directions in the multiscale approach multifrequency data and multiple grid sizes are used to overcome somewhat the severe local minima problem of waveform tomography the method is applied successfully to 1d and 2d heterogeneous models it can accurately recover low and high wavenumber components of the velocity models the inversion result for the 2d model demonstrates that the multiscale method is computationally efficient and converges faster than a conventional single scale method

cfar detection of multidimensional signals an invariant approach the paper deals with constant false alarm rate cfar detection of multidimensional signals embedded in gaussian noise with unknown covariance we attack the problem by resorting to the principle of invariance which proves a valuable statistical tool for ensuring a priori namely at the design stage the cfar property in this context we determine a maximal invariant statistic with respect to a proper group of transformations that leave unaltered the hypothesis testing problem under study devise the optimum invariant detector and show that no uniformly most powerful invariant umpi test exists thus we establish the conditions an invariant detector must fulfill in order to ensure the cfar property finally we discuss several suboptimal implementable invariant receivers and remarkably show that the generalized likelihood ratio test glrt detector is a member of this class the performance analysis which has been carried out in the presence of a gaussian signal array shows that the proposed detectors exhibit a quite acceptable loss with respect to the optimum neyman pearson detector

application of dcim on marine controlled source electromagnetic survey the discrete complex image method dcim is used to calculate the mixed potential electric field integral equation the dcim has been used broadly in high frequency domain and now is successfully introduced to the extremely low frequency situation of marine controlled source electromagnetic mcsem survey the integral path in high frequency situation does not hold valid for extremely low frequency situation the reason can be ascribed to the change of wave number formulas a new integral path suitable for the extremely low frequency situation is proposed the appropriate ranges of the parameters used in dcim are given the computation is stable at these considerably large ranges the results of a five layer model calculated with dcim are compared with those with the filter method and the direct integral method the computation time with dcim is comparable to the time with filter method the effects of water depth upper sediment thickness and oil layer thickness are studied with dcim the results show that electric field varies with the change of thickness the effects accord to the physical meanings of mcsem survey it is safe to conclude that the dcim with the new path is valid in mcsem survey

comparative evaluation of absorbing boundary conditions using green s functions for layered media absorbing boundary conditions are comparatively studied using the green s functions of the vector and scalar potentials for multilayer geometries and general sources since the absorbing boundaries are introduced as additional layers with predefined reflection coefficients into the calculation of the green s functions this approach provides an absolute measure of the effectiveness of different absorbing boundaries the green s functions are calculated using different reflection coefficients corresponding to different absorbing boundaries and compared to those obtained with no absorbing boundary it is observed that the perfectly matched layer pml is by far the best among the other absorbing boundary conditions whose reflection coefficients are available