@article{IskenVasyuraBathkeDahmetal.2022,
  author    = {Isken, Marius Paul and Vasyura-Bathke, Hannes and Dahm, Torsten and Heimann, Sebastian},
  title     = {De-noising distributed acoustic sensing data using an adaptive frequency-wavenumber filter},
  series = {Geophysical journal international},
  volume    = {231},
  journal   = {Geophysical journal international},
  number    = {2},
  publisher = {Oxford University Press},
  address   = {Oxford},
  issn      = {0956-540X},
  doi       = {10.1093/gji/ggac229},
  pages     = {944 -- 949},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{VasyuraBathkeDettmerDuttaetal.2021,
  author    = {Vasyura-Bathke, Hannes and Dettmer, Jan and Dutta, Rishabh and Mai, Paul Martin and J{\´o}nsson, Sigurj{\´o}n},
  title     = {Accounting for theory errors with empirical Bayesian noise models in nonlinear centroid moment tensor estimation},
  series = {Geophysical journal international / the Royal Astronomical Society, the Deutsche Geophysikalische Gesellschaft and the European Geophysical Society},
  volume    = {225},
  journal   = {Geophysical journal international / the Royal Astronomical Society, the Deutsche Geophysikalische Gesellschaft and the European Geophysical Society},
  number    = {2},
  publisher = {Oxford University Press},
  address   = {Oxford},
  issn      = {0956-540X},
  doi       = {10.1093/gji/ggab034},
  pages     = {1412 -- 1431},
  year      = {2021},
  abstract  = {Centroid moment tensor (CMT) parameters can be estimated from seismic waveforms. Since these data indirectly observe the deformation process, CMTs are inferred as solutions to inverse problems which are generally underdetermined and require significant assumptions, including assumptions about data noise. Broadly speaking, we consider noise to include both theory and measurement errors, where theory errors are due to assumptions in the inverse problem and measurement errors are caused by the measurement process. While data errors are routinely included in parameter estimation for full CMTs, less attention has been paid to theory errors related to velocity-model uncertainties and how these affect the resulting moment-tensor (MT) uncertainties. Therefore, rigorous uncertainty quantification for CMTs may require theory-error estimation which becomes a problem of specifying noise models. Various noise models have been proposed, and these rely on several assumptions. All approaches quantify theory errors by estimating the covariance matrix of data residuals. However, this estimation can be based on explicit modelling, empirical estimation and/or ignore or include covariances. We quantitatively compare several approaches by presenting parameter and uncertainty estimates in nonlinear full CMT estimation for several simulated data sets and regional field data of the M-1 4.4, 2015 June 13 Fox Creek, Canada, event. While our main focus is at regional distances, the tested approaches are general and implemented for arbitrary source model choice. These include known or unknown centroid locations, full MTs, deviatoric MTs and double-couple MTs. We demonstrate that velocity-model uncertainties can profoundly affect parameter estimation and that their inclusion leads to more realistic parameter uncertainty quantification. However, not all approaches perform equally well. Including theory errors by estimating non-stationary (non-Toeplitz) error covariance matrices via iterative schemes during Monte Carlo sampling performs best and is computationally most efficient. In general, including velocity-model uncertainties is most important in cases where velocity structure is poorly known.},
  language  = {en}
}