TY  - JOUR
A1  - Heimann, Sebastian
A1  - Vasyura-Bathke, Hannes
A1  - Sudhaus, Henriette
A1  - Isken, Marius Paul
A1  - Kriegerowski, Marius
A1  - Steinberg, Andreas
A1  - Dahm, Torsten
T1  - A Python framework for efficient use of pre-computed Green's functions in seismological and other physical forward and inverse source problems
JF  - Solid earth
N2  - The computation of such synthetic GFs is computationally and operationally demanding. As a consequence, the onthe-fly recalculation of synthetic GFs in each iteration of an optimisation is time-consuming and impractical. Therefore, the pre-calculation and efficient storage of synthetic GFs on a dense grid of source to receiver combinations enables the efficient lookup and utilisation of GFs in time-critical scenarios. We present a Python-based framework and toolkit - Pyrocko-GF - that enables the pre-calculation of synthetic GF stores, which are independent of their numerical calculation method and GF transfer function. The framework aids in the creation of such GF stores by interfacing a suite of established numerical forward modelling codes in seismology (computational back ends). So far, interfaces to back ends for layered Earth model cases have been provided; however, the architecture of Pyrocko-GF is designed to cover back ends for other geometries (e.g. full 3-D heterogeneous media) and other physical quantities (e.g. gravity, pressure, tilt). Therefore, Pyrocko-GF defines an extensible GF storage format suitable for a wide range of GF types, especially handling elasticity and wave propagation problems. The framework assists with visualisations, quality control, and the exchange of GF stores, which is supported through an online platform that provides many pre-calculated GF stores for local, regional, and global studies. The Pyrocko-GF toolkit comes with a well-documented application programming interface (API) for the Python programming language to efficiently facilitate forward modelling of geophysical processes, e.g. synthetic waveforms or static displacements for a wide range of source models.
Y1  - 2019
U6  - https://doi.org/10.5194/se-10-1921-2019
SN  - 1869-9510
SN  - 1869-9529
VL  - 10
IS  - 6
SP  - 1921
EP  - 1935
PB  - Copernicus
CY  - Göttingen
ER  - 
TY  - JOUR
A1  - Isken, Marius Paul
A1  - Vasyura-Bathke, Hannes
A1  - Dahm, Torsten
A1  - Heimann, Sebastian
T1  - De-noising distributed acoustic sensing data using an adaptive frequency-wavenumber filter
JF  - Geophysical journal international
N2  - Data recorded by distributed acoustic sensing (DAS) along an optical fibre sample the spatial and temporal properties of seismic wavefields at high spatial density. Often leading to massive amount of data when collected for seismic monitoring along many kilometre long cables. The spatially coherent signals from weak seismic arrivals within the data are often obscured by incoherent noise. We present a flexible and computationally efficient filtering technique, which makes use of the dense spatial and temporal sampling of the data and that can handle the large amount of data. The presented adaptive frequency-wavenumber filter suppresses the incoherent seismic noise while amplifying the coherent wavefield. We analyse the response of the filter in time and spectral domain, and we demonstrate its performance on a noisy data set that was recorded in a vertical borehole observatory showing active and passive seismic phase arrivals. Lastly, we present a performant open-source software implementation enabling real-time filtering of large DAS data sets.
KW  - Fourier analysis
KW  - Image processing
KW  - Time-series analysis
KW  - Seismic noise
KW  - Distributed acoustic sensing
Y1  - 2022
U6  - https://doi.org/10.1093/gji/ggac229
SN  - 0956-540X
SN  - 1365-246X
VL  - 231
IS  - 2
SP  - 944
EP  - 949
PB  - Oxford University Press
CY  - Oxford
ER  -