Seismic Traveltime Tomography of the Crust and Lithosphere

Rawlinson, Nicholas; Sambridge, Malcolm

doi:10.1016/s0065-2687(03)46002-0

Cited by 197 publications

(186 citation statements)

References 212 publications

(461 reference statements)

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“…We use a synthetic example from Bodin et al [31] to illustrate. It is reasonable to assume that errors in dispersion measurements are independent and identically distributed, in which case the covariance matrix becomes proportional to the identity 4) and the standard deviation parameter, σ 2 s , becomes a new unknown. For the time-correlated receiver functions, we assume an exponential covariance function described by two more unknowns, a variance, σ 2 r , and a correlation parameter, r. In this case, C r becomes a symmetric Toeplitz matrix (C r ) i,j = σ number of layers, n L , and the three noise parameters (σ s , σ r , r).…”

Section: (B) Joint Inversion Of Multiple Data Typesmentioning

confidence: 99%

“…In global seismology, the path that energy travels from source to receiver samples Earth's interior highly unevenly. The usual way of addressing these limitations is to use a tomographic imaging technique and apply some spatial smoothing, norm damping or simply to coarsen the parametrization in ill-constrained volumes of the model [4]. In practice, this usually reduces to the need to solve a large linear system of equations with regularization [4].…”

Section: Introductionmentioning

confidence: 99%

“…The usual way of addressing these limitations is to use a tomographic imaging technique and apply some spatial smoothing, norm damping or simply to coarsen the parametrization in ill-constrained volumes of the model [4]. In practice, this usually reduces to the need to solve a large linear system of equations with regularization [4]. A key point is that regularization is often applied uniformly across the entire model, which raises the possibility that, while the ill-constrained regions are being appropriately damped, the well-constrained regions are also being oversmoothed and hence information may be lost.…”

Section: Introductionmentioning

confidence: 99%

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Transdimensional inference in the geosciences

Sambridge

Bodin

Gallagher

et al. 2013

Phil. Trans. R. Soc. A.

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140

114

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Seismologists construct images of the Earth's interior structure using observations, derived from seismograms, collected at the surface. A common approach to such inverse problems is to build a single ‘best’ Earth model, in some sense. This is despite the fact that the observations by themselves often do not require, or even allow, a single best-fit Earth model to exist. Interpretation of optimal models can be fraught with difficulties, particularly when formal uncertainty estimates become heavily dependent on the regularization imposed. Similar issues occur across the physical sciences with model construction in ill-posed problems. An alternative approach is to embrace the non-uniqueness directly and employ an inference process based on parameter space sampling. Instead of seeking a best model within an optimization framework, one seeks an ensemble of solutions and derives properties of that ensemble for inspection. While this idea has itself been employed for more than 30 years, it is now receiving increasing attention in the geosciences. Recently, it has been shown that transdimensional and hierarchical sampling methods have some considerable benefits for problems involving multiple parameter types, uncertain data errors and/or uncertain model parametrizations, as are common in seismology. Rather than being forced to make decisions on parametrization, the level of data noise and the weights between data types in advance, as is often the case in an optimization framework, the choice can be informed by the data themselves. Despite the relatively high computational burden involved, the number of areas where sampling methods are now feasible is growing rapidly. The intention of this article is to introduce concepts of transdimensional inference to a general readership and illustrate with particular seismological examples. A growing body of references provide necessary detail.

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Section: (B) Joint Inversion Of Multiple Data Typesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Transdimensional inference in the geosciences

Sambridge

Bodin

Gallagher

et al. 2013

Phil. Trans. R. Soc. A.

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140

114

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“…Iterations are repeated until the contribution of the next cycle becomes negligible. In case of our study, six iterations were enough to achieve sufficient convergence [12]. …”

Section: Results and Analysismentioning

confidence: 96%

3D Tomographic Imaging of P Wave Velocity Structure Beneath Java Island using Fast Marching Tomography Method

Chasanah

Santosa

2017

IJOS

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AbstractJava Island one of the best locations for geophysics research, as it's located near the edge of the junction of continental Eurasia Plate and the Indo-Australia Plate with the movement of the plates 6 cm/year. Its needed the accurate imaging of subsurface structure to understanding complex tectonic setting. The first arrival time from local earthquake of Java (6,360-9,150 S and 105,890-115,540 E) with magnitude greater than 4,5 Mw occurred from 2011 until 2013 recorded by local network seismograph has been inverted for three dimensional variation of the depth to the P wave velocity in the Java island. At the same time, earthquake hypocenter location has been corrected simultaneous. This research apply a new tomographic inversion scheme FMTOMO that has been developed by Rawlinson (2004). The results of inversion show that there are three layer in 100 km from the surface at continental plate side (Eurasia). The continental upper crust has P wave velocity variation about 4-5,5 km/s in the depth until 30 km from the surface. There are low velocity anomaly inclined towards the slab which probably have associated with shallow earthquake along the slab.

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“…It is applied at all scales, from the local to the global scale (Romanowicz 2003), using signals from artificial sources and earthquakes (Rawlinson & Sambridge 2003;Rawlinson et al 2010;Liu & Gu 2012). Traditionally, the traveltime values 'picked' from the observed waveform signals (of seismic waves emerging from artificial sources or from earthquakes) are inverted for the distribution of the seismic velocity (or slowness) in the subsurface, either along 2-D profiles or in 3-D volumes (Thurber & Aki 1987).…”

Section: Introductionmentioning

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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Seismic Traveltime Tomography of the Crust and Lithosphere

Cited by 197 publications

References 212 publications

Transdimensional inference in the geosciences

Transdimensional inference in the geosciences

3D Tomographic Imaging of P Wave Velocity Structure Beneath Java Island using Fast Marching Tomography Method

Bayesian inversion of refraction seismic traveltime data

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