Abstract
Full-waveform inversion for borehole seismic data is an ill-posed problem and constraining the problem is crucial. Constraints can be imposed on the data and model space through covariance matrices. Usually, they are set to a diagonal matrix. For the data space, signal polarization information can be used to evaluate the data uncertainties. The inversion forces the synthetic data to fit the polarization of observed data. A synthetic inversion for a 2D-2C data estimating a 1D elastic model shows a clear improvement, especially at the level of the receivers. For the model space, horizontal and vertical spatial correlations using a Laplace distribution can be used to fill the model space covariance matrix. This approach reduces the degree of freedom of the inverse problem, which can be quantitatively evaluated. Strong horizontal spatial correlation distances favor a tabular geological model whenever it does not contradict the data. The relaxation of the spatial correlation distances from large to small during the iterative inversion process allows the recovery of geological objects of the same size, which regularizes the inverse problem. Synthetic constrained and unconstrained inversions for 2D-2C crosswell data show the clear improvement of the inversion results when constraints are used.
Highlights
Full-waveform inversion of seismic data allows one to obtain an image of the subsurface through the determination of a certain number of physical parameters
The horizontal and vertical spatial correlations using Laplace distribution are used to fill the model space covariance matrix in order to introduce an a priori solution, favoring a tabular medium whenever it doesn’t contradict the information contained in the data
Constraining the seismic inverse problem in the model space can be achieved by defining a priori model parameters and evaluating the model uncertainties associated with this model
Summary
Full-waveform inversion of seismic data allows one to obtain an image of the subsurface through the determination of a certain number of physical parameters. Full-waveform inversion is an ill-posed problem, in the sense that an infinite number of models match the data [1]. It is classically solved with local optimization schemes and is strongly dependent on the starting model definition. Regularization can be expressed in the curvelet or wavelet domains [9,10] In such domains, the norm minimization is generally preferred for the model term penalty because it ensures sparsity in the model space. We will illustrate the benefit of constraining the seismic inverse problem on the model space by performing a crosswell synthetic experiment. The horizontal and vertical spatial correlations using Laplace distribution are used to fill the model space covariance matrix in order to introduce an a priori solution, favoring a tabular medium whenever it doesn’t contradict the information contained in the data
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