On-the-Fly Full Hessian Kernel Calculations Based upon Seismic-Wave Simulations

Author(s):  
Yujiang Xie ◽  
Catherine A. Rychert ◽  
Nicholas Harmon ◽  
Qinya Liu ◽  
Dirk Gajewski

Abstract Full waveform inversion or adjoint tomography has routinely been performed to image the internal structure of the Earth at high resolution. This is typically done using the Fréchet kernels and the approximate Hessian or the approximate inverse Hessian because of the high-computational cost of computing and storing the full Hessian. Alternatively, the full Hessian kernels can be used to improve inversion resolutions and convergence rates, as well as possibly to mitigate interparameter trade-offs. The storage requirements of the full Hessian kernel calculations can be reduced by compression methods, but often at a price of accuracy depending on the compression factor. Here, we present open-source codes to compute both Fréchet and full Hessian kernels on the fly in the computer random access memory (RAM) through simultaneously solving four wave equations, which we call Quad Spectral-Element Method (QuadSEM). By recomputing two forward fields at the same time that two adjoint fields are calculated during the adjoint simulation, QuadSEM constructs the full Hessian kernels using the exact forward and adjoint fields. In addition, we also implement an alternative approach based on the classical wavefield storage method (WSM), which stores forward wavefields every kth (k≥1) timestep during the forward simulation and reads required fields back into memory during the adjoint simulation for kernel construction. Both Fréchet and full Hessian kernels can be computed simultaneously through the QuadSEM or the WSM code, only doubling the computational cost compared with the computation of Fréchet kernels alone. Compared with WSM, QuadSEM can reduce the disk space and input/output cost by three orders of magnitude in the presented examples that use 15,000 timesteps. Numerical examples are presented to demonstrate the functionality of the methods, and the computer codes are provided with this contribution.

2020 ◽  
Vol 221 (3) ◽  
pp. 1591-1604 ◽  
Author(s):  
Solvi Thrastarson ◽  
Martin van Driel ◽  
Lion Krischer ◽  
Christian Boehm ◽  
Michael Afanasiev ◽  
...  

SUMMARY We present a novel full-waveform inversion (FWI) approach which can reduce the computational cost by up to an order of magnitude compared to conventional approaches, provided that variations in medium properties are sufficiently smooth. Our method is based on the usage of wavefield adapted meshes which accelerate the forward and adjoint wavefield simulations. By adapting the mesh to the expected complexity and smoothness of the wavefield, the number of elements needed to discretize the wave equation can be greatly reduced. This leads to spectral-element meshes which are optimally tailored to source locations and medium complexity. We demonstrate a workflow which opens up the possibility to use these meshes in FWI and show the computational advantages of the approach. We provide examples in 2-D and 3-D to illustrate the concept, describe how the new workflow deviates from the standard FWI workflow, and explain the additional steps in detail.


Geophysics ◽  
2017 ◽  
Vol 82 (5) ◽  
pp. A43-A49 ◽  
Author(s):  
Zhiguang Xue ◽  
Hejun Zhu ◽  
Sergey Fomel

Because 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 synthetic Marmousi model demonstrate that seislet regularization can greatly improve the robustness of FWI by recovering high-resolution velocity models, particularly in the presence of strong crosstalk artifacts from simultaneous sources or strong random noise in the data.


2019 ◽  
Vol 219 (2) ◽  
pp. 1225-1236 ◽  
Author(s):  
Youyi Ruan ◽  
Wenjie Lei ◽  
Ryan Modrak ◽  
Rıdvan Örsvuran ◽  
Ebru Bozdağ ◽  
...  

SUMMARY The uneven distribution of earthquakes and stations in seismic tomography leads to slower convergence of nonlinear inversions and spatial bias in inversion results. Including dense regional arrays, such as USArray or Hi-Net, in global tomography causes severe convergence and spatial bias problems, against which conventional pre-conditioning schemes are ineffective. To save computational cost and reduce model bias, we propose a new strategy based on a geographical weighting of sources and receivers. Unlike approaches based on ray density or the Voronoi tessellation, this method scales to large full-waveform inversion problems and avoids instabilities at the edges of dense receiver or source clusters. We validate our strategy using a 2-D global waveform inversion test and show that the new weighting scheme leads to a nearly twofold reduction in model error and much faster convergence relative to a conventionally pre-conditioned inversion. We implement this geographical weighting strategy for global adjoint tomography.


Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. R333-R340 ◽  
Author(s):  
Gian Matharu ◽  
Mauricio Sacchi

Accounting for the Hessian in full-waveform inversion (FWI) can lead to higher convergence rates, improved resolution, and better mitigation of parameter trade-off in multiparameter problems. In spite of these advantages, the adoption of second-order optimization methods (e.g., truncated Newton [TN]) has been precluded by their high computational cost. We propose a subsampled TN (STN) algorithm for time-domain FWI with applications presented for the elastic isotropic case. By using uniform or nonuniform source subsampling during the computation of Hessian-vector products, we reduce the number of partial differential equation solves required per iteration when compared to the conventional TN algorithm. We evaluate the performance of STN through synthetic inversions on the Marmousi II and BP 2.5D models, using the limited-memory Broyden–Fletcher–Goldfarb–Shanno and TN algorithms as benchmarks. We determine that STN reaches a target misfit reduction at an overall cost comparable to first-order gradient methods, while retaining favorable convergence properties of TN methods. Furthermore, we evaluate an example in which nonuniform sampling outperforms uniform sampling in STN due to highly nonuniform source contributions to the Hessian.


Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. C179-C193 ◽  
Author(s):  
Nabil Masmoudi ◽  
Tariq Alkhalifah

Full-waveform inversion (FWI) in anisotropic media is challenging, mainly because of the large computational cost, especially in 3D, and the potential trade-offs between the model parameters needed to describe such media. By analyzing the trade-offs and understanding the resolution limits of the inversion, we can constrain FWI to focus on the main parameters the data are sensitive to and push the inversion toward more reliable models of the subsurface. Orthorhombic anisotropy is one of the most practical approximations of the earth subsurface that takes into account the natural horizontal layering and the vertical fracture network. We investigate the feasibility of a multiparameter FWI for an acoustic orthorhombic model described by six parameters. We rely on a suitable parameterization based on the horizontal velocity and five dimensionless anisotropy parameters. This particular parameterization allows a multistage model inversion strategy in which the isotropic, then, the vertical transverse isotropic, and finally the orthorhombic model can be successively updated. We applied our acoustic orthorhombic inversion on the SEG-EAGE overthrust synthetic model. The observed data used in the inversion are obtained from an elastic variable density version of the model. The quality of the inverted model suggests that we may recover only four parameters, with different resolution scales depending on the scattering potential of these parameters. Therefore, these results give useful insights on the expected resolution of the inverted parameters and the potential constraints that could be applied to an orthorhombic model inversion. We determine the efficiency of the inversion approach on real data from the North Sea. The inverted model is in agreement with the geologic structures and well-log information.


Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. R553-R563
Author(s):  
Sagar Singh ◽  
Ilya Tsvankin ◽  
Ehsan Zabihi Naeini

The nonlinearity of full-waveform inversion (FWI) and parameter trade-offs can prevent convergence toward the actual model, especially for elastic anisotropic media. The problems with parameter updating become particularly severe if ultra-low-frequency seismic data are unavailable, and the initial model is not sufficiently accurate. We introduce a robust way to constrain the inversion workflow using borehole information obtained from well logs. These constraints are included in the form of rock-physics relationships for different geologic facies (e.g., shale, sand, salt, and limestone). We develop a multiscale FWI algorithm for transversely isotropic media with a vertical symmetry axis (VTI media) that incorporates facies information through a regularization term in the objective function. That term is updated during the inversion by using the models obtained at the previous inversion stage. To account for lateral heterogeneity between sparse borehole locations, we use an image-guided smoothing algorithm. Numerical testing for structurally complex anisotropic media demonstrates that the facies-based constraints may ensure the convergence of the objective function towards the global minimum in the absence of ultra-low-frequency data and for simple (even 1D) initial models. We test the algorithm on clean data and on surface records contaminated by Gaussian noise. The algorithm also produces a high-resolution facies model, which should be instrumental in reservoir characterization.


2018 ◽  
Vol 37 (2) ◽  
pp. 142-145 ◽  
Author(s):  
Philipp Witte ◽  
Mathias Louboutin ◽  
Keegan Lensink ◽  
Michael Lange ◽  
Navjot Kukreja ◽  
...  

This tutorial is the third part of a full-waveform inversion (FWI) tutorial series with a step-by-step walkthrough of setting up forward and adjoint wave equations and building a basic FWI inversion framework. For discretizing and solving wave equations, we use Devito ( http://www.opesci.org/devito-public ), a Python-based domain-specific language for automated generation of finite-difference code ( Lange et al., 2016 ). The first two parts of this tutorial ( Louboutin et al., 2017 , 2018 ) demonstrated how to solve the acoustic wave equation for modeling seismic shot records and how to compute the gradient of the FWI objective function using the adjoint-state method. With these two key ingredients, we will now build an inversion framework that can be used to minimize the FWI least-squares objective function.


2016 ◽  
Vol 206 (3) ◽  
pp. 1467-1478 ◽  
Author(s):  
Dimitri Komatitsch ◽  
Zhinan Xie ◽  
Ebru Bozdağ ◽  
Elliott Sales de Andrade ◽  
Daniel Peter ◽  
...  

2018 ◽  
Vol 37 (1) ◽  
pp. 69-72 ◽  
Author(s):  
Mathias Louboutin ◽  
Philipp Witte ◽  
Michael Lange ◽  
Navjot Kukreja ◽  
Fabio Luporini ◽  
...  

This is the second part of a three-part tutorial series on full-waveform inversion (FWI) in which we provide a step-by-step walk through of setting up forward and adjoint wave equation solvers and an optimization framework for inversion. In Part 1 ( Louboutin et al., 2017 ), we showed how to use Devito ( http://www.opesci.org/devito-public ) to set up and solve acoustic wave equations with (impulsive) seismic sources and sample wavefields at the receiver locations to forward model shot records. Here in Part 2, we will discuss how to set up and solve adjoint wave equations with Devito and, from that, how we can calculate gradients and function values of the FWI objective function.


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