Ensemble-based uncertainty estimation in Full Waveform Inversion

Thurin, Julien; Brossier, Romain; Métivier, Ludovic

doi:10.1093/gji/ggz384

Cited by 21 publications

(10 citation statements)

References 91 publications

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“…Trialing other misfit functions that capture traveltime differences of multiple events, such as the local traveltime inversion method proposed by Hu et al (2020) could also be advantageous. Another promising branch of research is uncertainty quantification for FWT in the CZ, as these methods may help researchers to identify and avoid interpreting inversion artifacts (e.g., Thurin et al, 2019). Additional improvements could be made via the introduction of horizontal component data and sources, as these may be particularly useful in better resolving Vs (e.g., J.…”

Section: Limitations Uncertainties and Outlook On Future Workmentioning

confidence: 99%

2D Near‐Surface Full‐Waveform Tomography Reveals Bedrock Controls on Critical Zone Architecture

Eppinger,

Holbrook,

Liu

et al. 2024

Earth and Space Science

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For decades, seismic imaging methods have been used to study the critical zone, Earth's thin, life‐supporting skin. The vast majority of critical zone seismic studies use traveltime tomography, which poorly resolves heterogeneity at many scales relevant to near‐surface processes, therefore limiting progress in critical zone science. Full‐waveform tomography can overcome this limitation by leveraging more seismic data and enhancing the resolution of geophysical imaging. In this study, we apply 2D full‐waveform tomography to match the phases of observed seismograms and elucidate previously undetected heterogeneity in the critical zone at a well‐studied catchment in the Laramie Range, Wyoming. In contrast to traveltime tomograms from the same data set, our results show variations in depth to bedrock ranging from 5 to 60 m over lateral scales of just tens of meters and image steep low‐velocity anomalies suggesting hydrologic pathways into the deep critical zone. Our results also show that areas with thick fractured bedrock layers correspond to zones of slightly lower velocities in the deep bedrock, while zones of high bedrock velocity correspond to sharp vertical transitions from bedrock to saprolite. By corroborating these findings with borehole imagery, we hypothesize that lateral changes in bedrock fracture density majorly impact critical zone architecture. Borehole data also show that our full‐waveform tomography results agree significantly better with velocity logs than previously published traveltime tomography models. Full‐waveform tomography thus appears unprecedentedly capable of imaging the spatially complex porosity structure crucial to critical zone hydrology and processes.

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Section: Limitations Uncertainties and Outlook On Future Workmentioning

confidence: 99%

2D Near‐Surface Full‐Waveform Tomography Reveals Bedrock Controls on Critical Zone Architecture

Eppinger,

Holbrook,

Liu

et al. 2024

Earth and Space Science

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“…Others have tried related ensemble-based approaches to seismic inversion: Liu and Grana (2018) apply an ensemble-based methods to seismic AVO inversion, but they do not use the local approach outlined here. Thurin et al (2019) and Gineste et al (2020) use the ensemble subspace version that we rely on here for ensemblebased iterative inversion of seismic waveform data in a layer-based depth section. The problem of full waveform inversion is a much more non-linear problem than that of AVO inversion.…”

Section: Introductionmentioning

confidence: 99%

Bayesian rock-physics inversion using a localized ensemble-based approach — With an application to the Alvheim field

Spremić,

Eidsvik,

Avseth

2024

GEOPHYSICS

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We describe, implement and show results of a localized ensemble-based approach for seismic AVO inversion with uncertainty quantification. Ensembles are simulated from continuous prior probability distributions for fluid saturations and clay content. Starting with continuous saturations and clay content variables, we use depth-varying models for cementation and grain contact theory, Gassmann fluid substitution with mixed saturations, and approximations to the Zoeppritz equations for the AVO attributes at the top-reservoir. The local conditioning to seismic AVO observations relies on i) the misfit between ensemble simulated seismic AVO data and the field observations in a local partition of the grid - local patch, of inlines/crosslines around the locations where we aim to predict, ii) correlations between the simulated reservoir properties and the data in local patches, iii) local assessment to avoid unrealistic updates based on spurious correlations in the ensembles. Data from the Alvheim field in the North Sea are used to demonstrate the approach. The influence of the prior information from the well logs in combination with the seismic reflection data indicates presence of higher oil and gas saturation in the lobe structures of the field and increased clay content at their edges.

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“…In this context, objective functions alternative to the classical L2 misfit (Metivier et al, 2018;Sun & Alkhalifah, 2018;Warner & Guasch, 2014), and also global optimization schemes (Datta & Sen, 2016;Sajeva et al, 2016), have been proposed to mitigate the cycle-skipping issue. On the other hand, some probabilistic approaches have been proposed over the last years, but many of these offer an approximate uncertainty estimation (Thurin et al, 2019), are limited to be applied to analytical priors (Gebraad et al, 2020) or are characterized by huge computational effort (Sajeva et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

A computationally efficient Bayesian approach to full‐waveform inversion

Berti,

Aleardi,

Stucchi

2023

Geophysical Prospecting

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Conventional methods solve the full‐waveform inversion making use of gradient‐based algorithms to minimize an error function, which commonly measure the Euclidean distance between observed and predicted waveforms. This deterministic approach only provides a ‘best‐fitting’ model and cannot account for the uncertainties affecting the predicted solution. Local methods are also usually prone to get trapped into local minima of the error function. On the other hand, casting this inverse problem into a probabilistic framework has to deal with the formidable computational effort of the Bayesian approach when applied to non‐linear problems with expensive forward evaluations and large model spaces. We present a gradient‐based Markov Chain Monte Carlo full‐waveform inversion in which the posterior sampling is accelerated by compressing the data and model spaces through the discrete cosine transform, and by also defining a proposal that is a local, Gaussian approximation of the target posterior probability density. This proposal is constructed using the local Hessian and gradient informations of the log posterior, which are made computationally manageable thanks to the compression of the data and model spaces. We demonstrate the applicability of the approach by performing two synthetic inversion tests on portions of the Marmousi and BP acoustic model. In these examples, the forward modelling is performed using Devito, a finite difference domain‐specific language that solves the discretized wave equation on a Cartesian grid. For both examples, the results obtained by the implemented method are also validated against those obtained using a classic deterministic approach. Our tests illustrate the efficiency of the proposed probabilistic method, which seems quite robust against cycle‐skipping issues and also characterized by a computational cost comparable to that of the local inversion. The outcomes of the proposed probabilistic inversion can also play the role of starting models for a subsequent local inversion step aimed at improving the spatial resolution of the probabilistic result, which was limited by the model compression.

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Ensemble-based uncertainty estimation in Full Waveform Inversion

Cited by 21 publications

References 91 publications

2D Near‐Surface Full‐Waveform Tomography Reveals Bedrock Controls on Critical Zone Architecture

2D Near‐Surface Full‐Waveform Tomography Reveals Bedrock Controls on Critical Zone Architecture

Bayesian rock-physics inversion using a localized ensemble-based approach — With an application to the Alvheim field

A computationally efficient Bayesian approach to full‐waveform inversion

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