Assessing seismic uncertainty via geostatistical velocity-model perturbation and image registration: An application to subsalt imaging

Li, Lewis; Caers, Jef; Sava, Paul

doi:10.1190/tle34091064.1

Cited by 12 publications

(10 citation statements)

References 12 publications

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“…Hence, another way to look at variability is to quantify the change from one snapshot to the next one; this has the added appeal that pattern change is also critical in how surface patterns are recorded as stratigraphy. We propose to study local changes in time in the snapshots using the demon algorithm [Thirion, 1996;Li et al, 2015]. The demon algorithm is a gradient-based algorithm that determines "forces" that are required to deform a given image into a target image (Figure 5).…”

Section: Quantifying Change Between Images: the Demon Algorithmmentioning

confidence: 99%

Quantifying natural delta variability using a multiple-point geostatistics prior uncertainty model

Scheidt¹,

Fernandes²,

Paola³

et al. 2018

Preprint

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We address the question of quantifying uncertainty associated with autogenic pattern variability in a channelized transport system by means of a modern geostatistical method. This question has considerable relevance for practical subsurface applications as well, particularly those related to uncertainty quantification relying on Bayesian approaches. Specifically, we show how the autogenic variability in a laboratory experiment can be represented and reproduced by a multiple-point geostatistical prior uncertainty model. The latter geostatistical method requires selection of a limited set of training images from which a possibly infinite set of geostatistical model realizations, mimicking the training image patterns, can be generated. To that end, we investigate two methods to determine how many training images and what training should be provided to reproduce natural autogenic variability. The first method relies on distance-based clustering of overhead snapshots of the experiment; the second method relies on a rate of change quantification by means of a computer vision algorithm termed the demon algorithm. We show quantitatively that, with either training image selection method, we can statistically reproduce the natural variability of the delta formed in the experiment. In addition, we study the nature of the patterns represented in the set of training images as a representation of the "eigen-patterns‟ of the natural system. The eigen-pattern in the training image sets display patterns consistent with previous physical interpretations of the fundamental modes of this type of delta system: a highly channelized, incisional mode; a poorly channelized, depositional mode; and an intermediate mode between the two.

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Section: Quantifying Change Between Images: the Demon Algorithmmentioning

confidence: 99%

Quantifying natural delta variability using a multiple-point geostatistics prior uncertainty model

Scheidt¹,

Fernandes²,

Paola³

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hence, another way to look at variability is to quantify the change from one snapshot to the next one; this has the added appeal that pattern change is also critical in how surface patterns are recorded as stratigraphy. We propose to study local changes in time in the snapshots using the demon algorithm [ Thirion , ; Li et al ., ]. The demon algorithm is a gradient‐based algorithm that determines “forces” that are required to deform a given image into a target image (Figure ).…”

Section: Data Scientific Toolsmentioning

confidence: 99%

Quantifying natural delta variability using a multiple-point geostatistics prior uncertainty model

Scheidt

Fernandes

Paola

et al. 2016

J. Geophys. Res. Earth Surf.

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We address the question of quantifying uncertainty associated with autogenic pattern variability in a channelized transport system by means of a modern geostatistical method. This question has considerable relevance for practical subsurface applications as well, particularly those related to uncertainty quantification relying on Bayesian approaches. Specifically, we show how the autogenic variability in a laboratory experiment can be represented and reproduced by a multiple‐point geostatistical prior uncertainty model. The latter geostatistical method requires selection of a limited set of training images from which a possibly infinite set of geostatistical model realizations, mimicking the training image patterns, can be generated. To that end, we investigate two methods to determine how many training images and what training images should be provided to reproduce natural autogenic variability. The first method relies on distance‐based clustering of overhead snapshots of the experiment; the second method relies on a rate of change quantification by means of a computer vision algorithm termed the demon algorithm. We show quantitatively that with either training image selection method, we can statistically reproduce the natural variability of the delta formed in the experiment. In addition, we study the nature of the patterns represented in the set of training images as a representation of the “eigenpatterns” of the natural system. The eigenpattern in the training image sets display patterns consistent with previous physical interpretations of the fundamental modes of this type of delta system: a highly channelized, incisional mode; a poorly channelized, depositional mode; and an intermediate mode between the two.

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“…Geological realizations obtained by such perturbations of a reference structural model often underestimate structural uncertainties, since interpretation uncertainties are not taken into account. Interpretation uncertainties arise where reflectors cannot be tracked deterministically; this can be a result of an inaccurate imaging velocity model (Li et al, 2015), poor illumination and/or poor resolution (Lecomte et al, 2016). In these situations, multiple geologically possible structural models can be interpreted from the same seismic image (Bond et al, 2007;Bond, 2015;Alcalde et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Appraising structural interpretations using seismic data — Theoretical elements

et al. 2019

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Structural interpretation of seismic images can be highly subjective, especially in complex geologic settings. A single seismic image will often support multiple geologically valid interpretations. However, it is usually difficult to determine which of those interpretations are more likely than others. We have referred to this problem as structural model appraisal. We have developed the use of misfit functions to rank and appraise multiple interpretations of a given seismic image. Given a set of possible interpretations, we compute synthetic data for each structural interpretation, and then we compare these synthetic data against observed seismic data; this allows us to assign a data-misfit value to each structural interpretation. Our aim is to find data-misfit functions that enable a ranking of interpretations. To do so, we formalize the problem of appraising structural interpretations using seismic data and we derive a set of conditions to be satisfied by the data-misfit function for a successful appraisal. We investigate vertical seismic profiling (VSP) and surface seismic configurations. An application of the proposed method to a realistic synthetic model shows promising results for appraising structural interpretations using VSP data, provided that the target region is well-illuminated. However, we find appraising structural interpretations using surface seismic data to be more challenging, mainly due to the difficulty of computing phase-shift data misfits.

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Assessing seismic uncertainty via geostatistical velocity-model perturbation and image registration: An application to subsalt imaging

Cited by 12 publications

References 12 publications

Quantifying natural delta variability using a multiple-point geostatistics prior uncertainty model

Quantifying natural delta variability using a multiple-point geostatistics prior uncertainty model

Quantifying natural delta variability using a multiple-point geostatistics prior uncertainty model

Appraising structural interpretations using seismic data — Theoretical elements

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