The problem of inverting for multiple physical parameters in the subsurface using seismic full-waveform inversion (FWI) is complicated by interparameter trade-off arising from inherent ambiguities between different physical parameters. Parameter resolution is often characterized using scattering radiation patterns, but these neglect some important aspects of interparameter trade-off. More general analysis and mitigation of interparameter trade-off in isotropic-elastic FWI is possible through judiciously chosen multiparameter Hessian matrix-vector products. We show that products of multiparameter Hessian off-diagonal blocks with model perturbation vectors, referred to as interparameter contamination kernels, are central to the approach. We apply the multiparameter Hessian to various vectors designed to provide information regarding the strengths and characteristics of interparameter contamination, both locally and within the whole volume. With numerical experiments, we observe that S-wave velocity perturbations introduce strong contaminations into density and phase-reversed contaminations into P-wave velocity, but themselves experience only limited contaminations from other parameters. Based on these findings, we introduce a novel strategy to mitigate the influence of interparameter trade-off with approximate contamination kernels. Furthermore, we recommend that the local spatial and interparameter trade-off of the inverted models be quantified using extended multiparameter point spread functions (EMPSFs) obtained with pre-conditioned conjugate-gradient algorithm. Compared to traditional point spread functions, the EMPSFs appear to provide more accurate measurements for resolution analysis, by de-blurring the estimations, scaling magnitudes and mitigating interparameter contamination. Approximate eigenvalue volumes constructed with stochastic probing approach are proposed to evaluate the resolution of the inverted models within the whole model. With a synthetic Marmousi model example and a land seismic field data set from Hussar, Alberta, Canada, we confirm that the new inversion strategy suppresses the interparameter contamination effectively and provides more reliable density estimations in isotropic-elastic FWI as compared to standard simultaneous inversion approach.

Full-waveform inversion (FWI) has emerged as a powerful strategy for estimating subsurface model parameters by iteratively minimizing the difference between synthetic data and observed data. The Hessian-free (HF) optimization method represents an attractive alternative to Newton-type and gradient-based optimization methods. At each iteration, the HF approach obtains the search direction by approximately solving the Newton linear system using a matrix-free conjugate-gradient (CG) algorithm. The main drawback with HF optimization is that the CG algorithm requires many iterations. In our research, we develop and compare different preconditioning schemes for the CG algorithm to accelerate the HF Gauss-Newton (GN) method. Traditionally, preconditioners are designed as diagonal Hessian approximations. We additionally use a new pseudo diagonal GN Hessian as a preconditioner, making use of the reciprocal property of Green’s function. Furthermore, we have developed an $l$-BFGS inverse Hessian preconditioning strategy with the diagonal Hessian approximations as an initial guess. Several numerical examples are carried out. We determine that the quasi-Newton $l$-BFGS preconditioning scheme with the pseudo diagonal GN Hessian as the initial guess is most effective in speeding up the HF GN FWI. We examine the sensitivity of this preconditioning strategy to random noise with numerical examples. Finally, in the case of multiparameter acoustic FWI, we find that the $l$-BFGS preconditioned HF GN method can reconstruct velocity and density models better and more efficiently compared with the nonpreconditioned method.

Simultaneous determination of multiple physical parameters using full-waveform inversion (FWI) suffers from interparameter trade-off difficulties. Analyzing the interparameter trade-offs in different model parameterizations of isotropic-elastic FWI, and thus determining the appropriate model parameterization, are critical for efficient inversion and obtaining reliable inverted models. Five different model parameterizations are considered and compared including velocity-density, modulus-density, impedance-density, and two velocity-impedance parameterizations. The scattering radiation patterns are first used for interparameter trade-off analysis. Furthermore, a new framework is developed to evaluate the interparameter trade-off based upon multiparameter Hessian-vector products: Multiparameter point spread functions (MPSFs) and interparameter contamination sensitivity kernels (ICSKs), which provide quantitative, second-order measurements of the interparameter contaminations. In the numerical experiments, the interparameter trade-offs in various model parameterizations are evaluated using the MPSFs and ICSKs. Inversion experiments are carried out with simple Gaussian-anomaly models and a complex Marmousi model. Overall, the parameterization of the P-wave velocity, S-wave velocity, and density, and the parameterization of the P-wave velocity, S-wave velocity, and S-wave impedance perform best for reconstructing all of the physical parameters. Isotropic-elastic FWI of the Hussar low-frequency data set with various model parameterizations verifies our conclusions.

Distributed acoustic sensing (DAS) technology is, increasingly, the seismic acquisition mode of choice for its high spatial sampling rate, low cost, and nonintrusive deployability. It is being widely evaluated as an enabler of seismic monitoring for CO2 sequestration in building subsurface time-lapse images and in characterizing near-surface environments. To advance this evaluation, field seismic surveys with optical fibers have been conducted at the Containment and Monitoring Institute’s Field Research Station (CaMI.FRS) in Newell County, Alberta, Canada. In comparison to the standard geophones, optical fibers deployed in surface trenches at CaMI.FRS have recorded high-quality surface waves, rich in low frequencies and exhibiting limited spatial aliasing. These benefits have motivated us to apply the full-waveform inversion (FWI) approach to image the S-wave velocity (VS) and attenuation (quality factor QS) models at shallow site using the surface waves recorded by optical fibers. Compared to the conventional surface-wave dispersion approach, FWI can intrinsically incorporate fundamental and high-order modes and produce VS model with high spatial resolution that resolves horizontal variations. The low-frequency components below 10 Hz measured in the DAS recordings are helpful to overcome the cycle-skipping problem of FWI. Following the adjoint-state method, QS sensitivity kernel can be calculated efficiently with memory strain variables. The QS model is iteratively estimated with a new misfit function measuring root-mean-square amplitude differences, which helps to reduce the trade-off artifacts. The synthetic data obtained from the inverted models are consistent with the observed data in amplitude and phase. The inversion results provide valuable information to characterize the near-surface environments at CaMI.FRS and are expected to support seismic imaging in deeper CO2 injection zones.

SUMMARY: Estimating subsurface attenuation distribution is essential to compensate the amplitude and phase distortions in seismic imaging and characterize attenuative reservoirs. Full-waveform inversion (FWI) methods represent promising techniques to invert for both velocity and attenuation models with arbitrary spatial distributions. However, simultaneously determining velocity and attenuation properties introduces the problem of interparameter trade-off in viscoelastic FWI. Ignoring attenuation effects can result in inaccurate velocity estimations. Velocity errors may produce significant parameter crosstalk artefacts in the inverted attenuation models. An appropriate misfit function measuring specific seismic attribute is essential to capture the influence of attenuation on the seismic data and thus is expected to reduce the influences of velocity errors for attenuation estimation. In this study, we evaluate the performances of different misfit functions for attenuation estimation in viscoelastic FWI accompanied with a two-stage sequential inversion strategy. Synthetic examples with different acquisition surveys are given to show that in the presence of strong velocity errors, the amplitude-based misfit functions, including envelope-difference, root-mean-square amplitude-ratio and spectral amplitude-ratio, can invert for the attenuation models more reliably, compared to the waveform-difference and instantaneous phase misfit functions. With the two-stage inversion approach, more reliable velocity and attenuation models can be obtained using viscoelastic FWI.

Elastic full-waveform inversion (FWI) in transversely isotropic media with a vertical symmetry axis (VTI) is applied to field walk-away vertical seismic profile (W-VSP) data acquired in Western Canada. The performance of VTI-elastic FWI is significantly influenced by the model parameterization choice. Synthetic analysis based on specific field survey configuration is carried out to evaluate three different VTI-elastic model parameterizations. Interparameter trade-offs are quantified using the recently introduced interparameter contamination sensitivity kernel approach. Synthetic results suggest that neglecting anisotropy leads to inaccurate velocity estimations. For the conventional vertical velocity-Thomsen’s parameter parameterization (i.e., vertical P-wave velocity, vertical S-wave velocity, Thomsen’s parameters ε and δ), a sequential inversion strategy is designed to reduce strong natural interparameter trade-offs. The model parameterizations of elastic-constant (c11, c13, c33, and c55) and velocity-based (vertical, horizontal, and normal move-out P-wave velocities and vertical S-wave velocity) models appear to suffer from fewer interparameter trade-offs, providing more reliable velocity and anisotropy models. Results derived from application of VTI-elastic FWI to the field W-VSP data set tend to support the synthetic conclusions. Multiparameter point spread functions are calculated to quantify the local interparameter trade-offs of the inverted models. The output inversion results are interpreted to provide valuable references regarding the target hydrocarbon reservoir.