An Adaptable Seismic Data Format
Lion Krischer,James A Smith,W. Lei,Matthieu Lefebvre,Y. Ruan,Elliott Sales de Andrade,Norbert Podhorszki,Ebru Bozdag,Jeroen Tromp +8 more
TL;DR: ASDF stores any number of synthetic, processed or unaltered waveforms in a single file and includes comprehensive meta information, such as event or station information, in the same file, thereby enhancing reproducibility and accountability.
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Abstract: We present ASDF, the Adaptable Seismic Data Format, a modern and practical data format for all branches of seismology and beyond. The growing volume of freely available data coupled with ever expanding computational power opens avenues to tackle larger and more complex problems. Current bottlenecks include inefficient resource usage and insufficient data organization. Properly scaling a problem requires the resolution of both these challenges, and existing data formats are no longer up to the task. ASDF stores any number of synthetic, processed or unaltered waveforms in a single file. A key improvement compared to existing formats is the inclusion of comprehensive meta information, such as event or station information, in the same file. Additionally, it is also usable for any non-waveform data, for example, cross-correlations, adjoint sources or receiver functions. Last but not least, full provenance information can be stored alongside each item of data, thereby enhancing reproducibility and accountability. Any data set in our proposed format is self-describing and can be readily exchanged with others, facilitating collaboration. The utilization of the HDF5 container format grants efficient and parallel I/O operations, integrated compression algorithms and check sums to guard against data corruption. To not reinvent the wheel and to build upon past developments, we use existing standards like QuakeML, StationXML, W3C PROV and HDF5 wherever feasible. Usability and tool support are crucial for any new format to gain acceptance. We developed mature C/Fortran and Python based APIs coupling ASDF to the widely used SPECFEM3D_GLOBE and ObsPy toolkits.
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Citations
Global adjoint tomography: first-generation model
Ebru Bozdag,Daniel Peter,Matthieu Lefebvre,Dimitri Komatitsch,Jeroen Tromp,Judith Hill,Norbert Podhorszki,David Pugmire +7 more
TL;DR: The first generation global tomographic model constructed based on adjoint tomography, an iterative full-wave-form inversion technique, was presented in this paper, where synthetic seismograms were calculated using GPU-accelerated spectral-element simulations of global seismic wave propagation, accommodating effects due to 3D anelastic crust & mantle structure, topography & bathymetry, the ocean load, ellipticity, rotation, and self-gravitation.
Global adjoint tomography—model GLAD-M25
W. Lei,Y. Ruan,Y. Ruan,Ebru Bozdag,Daniel Peter,Matthieu Lefebvre,Dimitri Komatitsch,Jeroen Tromp,Judith Hill,Norbert Podhorszki,David Pugmire +10 more
TL;DR: In this paper, a transversely isotropic global adjoint tomography model (GLAD-M25) is presented, which is the result of 10 quasi-Newton tomographic iterations with an earthquake database consisting of 1480 events.
Seismic wavefield imaging of Earth’s interior across scales
Jeroen Tromp
- 01 Jan 2020
TL;DR: The use of full waveform inversion (FWI) for imaging Earth's interior was introduced in the late 1970s and has become feasible for a wide range of applications and is currently used across nine orders of magnitude in frequency and wavelength.
154
Automated Large-Scale Full Seismic Waveform Inversion for North America and the North Atlantic
TL;DR: In this paper, a multiscale full seismic waveform inversion for crustal and upper mantle structure from the western edge of North America across the North Atlantic and into Europe is presented.
67
Accelerated full-waveform inversion using dynamic mini-batches
Dirk Philip van Herwaarden,Christian Boehm,Michael Afanasiev,Solvi Thrastarson,Lion Krischer,Jeannot Trampert,Andreas Fichtner +6 more
TL;DR: In this paper, the authors proposed a dynamic mini-batch approach to accelerate full-waveform inversion by selecting quasi-random subsets of sources, used to approximate the misfit and the gradient of the complete data set.
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