Albert Tarantola

Inverse Problem Theory and Methods for Model Parameter Estimation

1. The general discrete inverse problem 2. Monte Carol methods 3. The least-squares criterion 4. Least-absolute values criterion and minimax criterion 5. Functional inverse problems 6. Appendices 7. Problems References Index.

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...

Monte Carlo sampling of solutions to inverse problems

Probabilistic formulation of inverse problems leads to the definition of a probability distribution in the model space. This probability distribution combines a priori information with new information obtained by measuring some observable parameters (data). As, in the general case, the theory linking data with model parameters is nonlinear, the a posteriori probability in the model space may not be easy to describe (it may be multimodal, some moments may not be defined, etc.). When analyzing an inverse problem, obtaining a maximum likelihood model is usually not sucient, as we normally also wish to have infor

A strategy for nonlinear elastic inversion of seismic reflection data

The problem of interpretation of seismic reflection data can be posed with sufficient generality using the concepts of inverse theory. In its roughest formulation, the inverse problem consists of obtaining the Earth model for which the predicted data best fit the observed data. If an adequate forward model is used, this best model will give the best images of the Earth’s interior. Three parameters are needed for describing a perfectly elastic, isotropic, Earth: the density ρ(x) and the Lame parameters λ(x) and μ(x), or the density ρ(x) and the P-wave and S-wave velocities α(x) and β(x). The choice of parameters is not neutral, in the sense that although theoretically equivalent, if they are not adequately chosen the numerical algorithms in the inversion can be inefficient. In the long (spatial) wavelengths of the model, adequate parameters are the P-wave and S-wave velocities, while in the short (spatial) wavelengths, P-wave impedance, S-wave impedance, and density are adequate. The problem of inversion o...

Two-dimensional nonlinear inversion of seismic waveforms; numerical results

The nonlinear problem of inversion of seismic waveforms can be set up using least‐squares methods. The inverse problem is then reduced to the problem of minimizing a lp;nonquadratic) function in a space of many (104to106) variables. Using gradient methods leads to iterative algorithms, each iteration implying a forward propagation generated by the actual sources, a backward propagation generated by the data residuals (acting as if they were sources), and a correlation at each point of the space of the two fields thus obtained, which gives the updated model. The quality of the results of any inverse method depends heavily on the realism of the forward modeling. Finite‐difference schemes are a good choice relative to realism because, although they are time‐consuming, they give excellent results. Numerical tests performed with multioffset synthetic data from a two‐dimensional model prove the feasibility of the approach. If only surface‐recorded reflections are used, the high spatial frequency content of the ...

Nonliner inversion of seismic reflection data in a laterally invariant medium

Interpretation of seismic waveforms can be expressed as an optimization problem based on a non‐linear least‐squares criterion to find the model which best explains the data. An initial model is corrected iteratively using a gradient method (conjugate gradient). At each iteration, computation of the direction of the model perturbation requires the forward propagation of the actual sources and the reverse‐time propagation of the residuals (misfit between the data and the synthetics); the two wave fields thus obtained are then correlated. An extra forward propagation is required to compute the amplitude of the perturbation along the conjugate‐gradient direction. The number of propagations to be simulated numerically in each iteration equals three times the number of shots. Since a 2-D finite‐difference code is employed to solve forward‐ and backward‐propagation problems, the method is general and can handle arbitrary 2-D source‐receiver configurations and lateral heterogeneities. Using conventional velocity ...

Wavelengths of Earth structures that can be resolved from seismic reflection data

The aim of inverting seismic waveforms is to obtain the “best” earth model. The best model is defined as the one producing seismograms that best match (usually under a least‐squares criterion) those recorded. Our approach is nonlinear in the sense that we synthesize seismograms without using any linearization of the elastic wave equation. Since we use rather complete data sets without any spatial aliasing, we do not have the problem of secondary minima (Tarantola, 1986). Nevertheless, our gradient methods fail to converge if the starting earth model is far from the true earth (Mora, 1987; Kolb et al., 1986; Pica et al., 1989).

Reference velocity model estimation from prestack waveforms; coherency optimization by simulated annealing

Coherency inversion, which consists of maximizing a semblance function calculated from unstacked seismic waveforms, has the potential of estimating reliable velocity information without requiring traveltime picking on unstacked data. In this work, coherency inversion is based on the assumption that reflectors’ zero‐offset times are known and that the velocity in each layer may vary laterally. The method uses a type of Monte Carlo technique termed the generalized simulated annealing method for updating the velocity field. At each Monte Carlo step, time‐to‐depth conversion is performed. Although this procedure is slow at convergence to the global minimum, it is robust and does not depend on the initial model or topography of the objective function. Applications to both synthetic and field data demonstrate the efficiency of coherency inversion for estimating both lateral velocity variations and interface depth positions.

Linear inverse Gaussian theory and geostatistics

Inverse problems in geophysics require the introduction of complex a priori information and are solved using computationally expensive Monte Carlo techniques (where large portions of the model space are explored). The geostatistical method allows for fast integration of complex a priori information in the form of covariance functions and training images. We combine geostatistical methods and inverse problem theory to generate realizations of the posterior probability density function of any Gaussian linear inverse problem, honoring a priori information in the form of a covariance function describing the spatial connectivity of the model space parameters. This is achieved using sequential Gaussian simulation, a well-known, noniterative geostatisticalmethod for generating samples of a Gaussian random field with a given covariance function. This work is a contribution to both linear inverse problem theory and geostatistics. Our main result is an efficient method to generate realizations, actual solutions rat...

Sensitivity of SS precursors to topography on the upper-mantle 660-km discontinuity

The sensitivity of SS precursors to the presence of topography on the 660-km discontinuity is addressed using an axisymmetric finite difference approximation to the SH wave propagation in the Earth mantle. Numerical experiments are lead to quantify the bias in both wavelength and amplitude committed in estimating depth information on the upper-mantle discontinuities from ray inversion of SdS arrival times. It is further shown that long period S660S arrival-times do not provide information on the deepening of the ‘660‧ in presence of a velocity increase expected as the thermal signature of a subducting slab.