Automating seismic waveform analysis for full 3-D waveform inversions

Lee, En‐Jui; Chen, Po

doi:10.1093/gji/ggt124

Cited by 17 publications

(4 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2) 3-D background models are used to compute Fréchet derivatives, thereby accommodating nonlinearities due to structure; (3) data may be assimilated based on automated measurement window-selection algorithms (Maggi et al 2009;Lee & Chen 2013); (4) as a result of (1)-(3), the amount of usable data steadily increases from iteration to iteration, thus enabling the extraction of more information from seismograms, ultimately culminating in global 'full-waveform inversion ' (FWI), that is, the use of entire three-component seismograms; and (5) the crust and mantle are inverted jointly, thereby eliminating the need for crustal corrections. The goal of this study is to harness 3-D simulations of seismic wave propagation in combination with adjoint-state methods to image the crust and mantle.…”

mentioning

confidence: 99%

Global adjoint tomography: first-generation model

Bozdag¹,

et al. 2016

View full text Add to dashboard Cite

We present the first-generation global tomographic model constructed based on adjoint tomography, an iterative full-waveform inversion technique. Synthetic seismograms were calculated using GPU-accelerated spectral-element simulations of global seismic wave propagation, accommodating effects due to 3-D anelastic crust & mantle structure, topography & bathymetry, the ocean load, ellipticity, rotation, and self-gravitation. Fréchet derivatives were calculated in 3-D anelastic models based on an adjoint-state method. The simulations were performed on the Cray XK7 named 'Titan', a computer with 18 688 GPU accelerators housed at Oak Ridge National Laboratory. The transversely isotropic global model is the result of 15 tomographic iterations, which systematically reduced differences between observed and simulated three-component seismograms. Our starting model combined 3-D mantle model S362ANI with 3-D crustal model Crust2.0. We simultaneously inverted for structure in the crust and mantle, thereby eliminating the need for widely used 'crustal corrections'. We used data from 253 earthquakes in the magnitude range 5.8 ≤ M w ≤ 7.0. We started inversions by combining ∼30 s body-wave data with ∼60 s surface-wave data. The shortest period of the surface waves was gradually decreased, and in the last three iterations we combined ∼17 s body waves with ∼45 s surface waves. We started using 180 min long seismograms after the 12th iteration and assimilated minor-and major-arc body and surface waves. The 15th iteration model features enhancements of well-known slabs, an enhanced image of the Samoa/Tahiti plume, as well as various other plumes and hotspots, such as Caroline, Galapagos, Yellowstone and Erebus. Furthermore, we see clear improvements in slab resolution along the Hellenic and Japan Arcs, as well as subduction along the East of Scotia Plate, which does not exist in the starting model. Point-spread function tests demonstrate that we are approaching the resolution of continentalscale studies in some areas, for example, underneath Yellowstone. This is a consequence of our multiscale smoothing strategy in which we define our smoothing operator as a function of the approximate Hessian kernel, thereby smoothing gradients less wherever we have good ray coverage, such as underneath North America.

show abstract

mentioning

confidence: 99%

Global adjoint tomography: first-generation model

Bozdag¹,

et al. 2016

View full text Add to dashboard Cite

show abstract

“…The choice of the misfit function is a key step that affects the success and the convergence of FWI (e.g., Modrak & Tromp, 2016). It is common to split seismic traces into smaller measurement windows using window‐selection algorithms (e.g., Chen et al., 2017; Lee & Chen, 2013; Maggi et al., 2009) to select high‐quality portions of seismograms and maximize the information extracted from each time series. Both time‐domain cross‐correlation (Dahlen et al., 2000; Luo & Schuster, 1991; Marquering et al., 1999; Tanimoto, 1995; Tromp et al., 2005) and frequency‐dependent multitaper cross‐correlation (Tape et al., 2009; Zhou et al., 2004, 2005) measurements tend to highlight the maximum amplitude signals in measurement windows where scattered waves, which provide valuable constraints on the structure of the medium they propagate through, are generally suppressed (e.g., Rickers et al., 2012).…”

Section: Adjoint Tomographymentioning

confidence: 99%

Adjoint Waveform Tomography of South America

et al. 2022

View full text Add to dashboard Cite

The seismic structure of the South American mantle, especially beneath the stable continental interior, has not been studied on a regional scale, with limitations in heterogeneous resolving power caused mainly by a dearth of seismographic stations outside the Andes Mountains. The continent is composed of three main tectonic domains (Figure 1: the Archean-Proterozoic South American Platform, the Phanerozoic Patagonian Platform, and the active Andean Belt. The stable platforms are composed of several cratons and cratonic blocks (Figure 1) amalgamated in the Neoproterozoic to form the Gondwana supercontinent. In this stable interior, several regional scale tomographic studies using teleseismic

show abstract

“…Nolet (1987) discussed the statistics of picking travel times in seismology when their probability distribution is away from Gaussian one. Using a data-driven strategy, Maggi et al (2009) had performed time window detection either on observed seismograms or on synthetic seismograms while Lee and Chen (2013) have developed a frequency-time window detection. We may consider data-driven strategies where only observed seismograms are analyzed (Chevrot, 2002;Gentili and Michelini, 2006;Rowe et al, 2002;Zhang et al, 2003) and model-driven strategies where observed seismograms are compared with synthetic ones computed in a reference model.…”

Section: Travel-time Estimation a Data Mining Issuementioning

confidence: 99%