Transdimensional Love-wave tomography of the British Isles and shear-velocity structure of the East Irish Sea Basin from ambient-noise interferometry

Galetti, Erica; Curtis, Andrew; Baptie, Brian; Jenkins, D. H. R.; Nicolson, Heather

doi:10.1093/gji/ggw286

Cited by 69 publications

(161 citation statements)

References 67 publications

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“…Our results also explain the poor signal‐to‐noise ratio in ambient noise interferometry‐derived signals by Nicolson et al () and Galetti et al () for signals crossing the North Sea. If the Q values we observe are representative across the North Sea basin, then all ambient noise traversing from one side of the sea to the other will be attenuated to nearly 0.…”

Section: Discussionsupporting

confidence: 83%

“…This acceptance ratio is defined as The acceptance ratio is a Bayesian quantity which promotes natural parsimony, meaning that if m and m 0 are identical in all but the number of parameters then the model with the fewest parameters will be chosen and ensures that samples are distributed according to the posterior probability density (Galetti & Curtis, 2018). This algorithm generates samples according the posterior probability density (Galetti et al, 2016) and does so in an ordered chain.…”

Section: Depth Inversionmentioning

confidence: 99%

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Seismic Attenuation From Ambient Noise Across the North Sea Ekofisk Permanent Array

et al. 2018

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Quality factor (Q) or equivalently attenuation α=1Q describes the amount of energy lost per cycle as a wave travels through a medium. This is important to correct seismic data amplitudes for near‐surface effects, to locate subsurface voids or porosity, to aid seismic interpretation, or for characterizing other rock and fluid properties. Seismic attenuation can be variable even when there are no discernible changes in seismic velocity or density (Yıldırım et al., 2017, https://doi.org/10.1016/j.jappgeo.2016.11.010) and so provides independent information about subsurface heterogeneity. This study uses ambient noise recordings made on the Ekofisk Life of Field Seismic array to estimate Q structure in the near surface. We employ the method of X. Liu et al. (2015, https://doi.org/10.1093/gji/ggv357), which uses linear triplets of receivers to estimate Q—ours is the first known application of the method to estimate the Q structure tomographically. Estimating Q requires an estimate of phase velocity which we obtain using the method of Bloch and Hales (1968, https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/58/3/1021/116607/) followed by traveltime tomography. The Q structure at Ekofisk has features which can be related to local geology, showing that surface ambient noise recordings may provide a new and robust method to image Q. Our results suggest that there is a nonlinear relationship between Q and compression. They also may explain why it has been found that in the period range of 1 to 2 s considered here, ambient noise cross correlations along paths that span the North Sea Basin are unreliable: Such Q values would attenuate almost all ambient seismic energy during such a traverse.

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Section: Discussionsupporting

confidence: 83%

Section: Depth Inversionmentioning

confidence: 99%

Seismic Attenuation From Ambient Noise Across the North Sea Ekofisk Permanent Array

et al. 2018

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“…The often repeated claim of trans‐dimensional inversion is that it results in a parsimonious solution, that is, the resulting Markov chain ensemble will converge toward models with an efficient number of parameters required to predict the observations within noise levels (neither underparameterized nor overparameterized models). This general approach has been utilized in a number of geophysical inverse problems across various disciplines (Bodin & Sambridge, ; Bodin et al, ; Burdick & Lekić, ; Dettmer et al, ; Dettmer et al, ; Dettmer et al, ; Galetti et al, ; Hawkins et al, ; Malinverno, ; Olugboji et al, ; Piana Agostinetti & Malinverno, ; Piana Agostinetti et al, ; Saygin et al, ). Its general advantage over other approaches is that it produces parsimonious inference that results in better estimates of uncertainties as shown in comparisons with more traditional fixed dimensional inversions (Dettmer et al, ; Olugboji et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…The most common parameterization used in these trans-dimensional inversions is the Voronoi cell (Bodin & Sambridge, 2009;Burdick & Lekić, 2017;Galetti et al, 2016;Saygin et al, 2016). When using Voronoi cells, a 2-D or 3-D region is parameterized as a collection of cell centers with associated Earth model parameters.…”

Section: Introductionmentioning

confidence: 99%

Trans‐Dimensional Surface Reconstruction With Different Classes of Parameterization

Hawkins

Bodin

Sambridge

et al. 2019

Geochem Geophys Geosyst

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The use of Bayesian trans‐dimensional sampling in 2‐D and 3‐D imaging problems has recently become widespread in geophysical inversion. Its benefits include its spatial adaptability to the level of information present in the data and the ability to produce uncertainty estimates. The most used parameterization in Bayesian trans‐dimensional inversions is Voronoi cells. Here we introduce a general software, TransTessellate2D, that allows 2‐D trans‐dimensional inference with Voronoi cells and two alternative underlying parameterizations, Delaunay triangulation with linear interpolation and Clough‐Tocher interpolation, which utilize the same algorithm but result in either C0 or C1 continuity. We demonstrate that these alternatives are better suited to the recovery of smooth models, and show that the posterior probability solution is less susceptible to multimodalities which can complicate the interpretation of model parameter uncertainties.

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“…4 shows the distribution of posterior values for the slowness, the number of cells and the inverted data noise of the well-fitting models (in the post burn-in phase).These models can be converted into a regular grid with a grid-specific spacing (see Section 3). From these gridded models, statistical properties like the average, standard deviation, median, etc., can be constructed locally at every model position (following Bodin & Sambridge 2009;Bodin et al 2012a;Young et al 2013a,b;Burdick & Lekić 2017;Galetti et al 2017). Typically, the average model is treated as the reference solution and the standard deviation is interpreted as a measure of the model error (uncertainty and resolution).…”

Section: R E F E R E N C E S O L U T I O N a N D E R Ro R M A Pmentioning

confidence: 99%

Bayesian inversion of refraction seismic traveltime data

Ryberg

Haberland

2017

Geophysical Journal International

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S U M M A R YWe apply a Bayesian Markov chain Monte Carlo (McMC) formalism to the inversion of refraction seismic, traveltime data sets to derive 2-D velocity models below linear arrays (i.e. profiles) of sources and seismic receivers. Typical refraction data sets, especially when using the far-offset observations, are known as having experimental geometries which are very poor, highly ill-posed and far from being ideal. As a consequence, the structural resolution quickly degrades with depth. Conventional inversion techniques, based on regularization, potentially suffer from the choice of appropriate inversion parameters (i.e. number and distribution of cells, starting velocity models, damping and smoothing constraints, data noise level, etc.) and only local model space exploration. McMC techniques are used for exhaustive sampling of the model space without the need of prior knowledge (or assumptions) of inversion parameters, resulting in a large number of models fitting the observations. Statistical analysis of these models allows to derive an average (reference) solution and its standard deviation, thus providing uncertainty estimates of the inversion result. The highly non-linear character of the inversion problem, mainly caused by the experiment geometry, does not allow to derive a reference solution and error map by a simply averaging procedure. We present a modified averaging technique, which excludes parts of the prior distribution in the posterior values due to poor ray coverage, thus providing reliable estimates of inversion model properties even in those parts of the models. The model is discretized by a set of Voronoi polygons (with constant slowness cells) or a triangulated mesh (with interpolation within the triangles). Forward traveltime calculations are performed by a fast, finite-difference-based eikonal solver. The method is applied to a data set from a refraction seismic survey from Northern Namibia and compared to conventional tomography. An inversion test for a synthetic data set from a known model is also presented.

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Transdimensional Love-wave tomography of the British Isles and shear-velocity structure of the East Irish Sea Basin from ambient-noise interferometry

Cited by 69 publications

References 67 publications

Seismic Attenuation From Ambient Noise Across the North Sea Ekofisk Permanent Array

Seismic Attenuation From Ambient Noise Across the North Sea Ekofisk Permanent Array

Trans‐Dimensional Surface Reconstruction With Different Classes of Parameterization

Bayesian inversion of refraction seismic traveltime data

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