@article{ShpritsVasileZhelayskaya2019,
  author    = {Shprits, Yuri Y. and Vasile, Ruggero and Zhelayskaya, Irina S.},
  title     = {Nowcasting and Predicting the Kp Index Using Historical Values and Real-Time Observations},
  series = {Space Weather: The International Journal of Research and Applications},
  volume    = {17},
  journal   = {Space Weather: The International Journal of Research and Applications},
  number    = {8},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {1542-7390},
  doi       = {10.1029/2018SW002141},
  pages     = {1219 -- 1229},
  year      = {2019},
  abstract  = {Current algorithms for the real-time prediction of the Kp index use a combination of models empirically driven by solar wind measurements at the L1 Lagrange point and historical values of the index. In this study, we explore the limitations of this approach, examining the forecast for short and long lead times using measurements at L1 and Kp time series as input to artificial neural networks. We explore the relative efficiency of the solar wind-based predictions, predictions based on recurrence, and predictions based on persistence. Our modeling results show that for short-term forecasts of approximately half a day, the addition of the historical values of Kp to the measured solar wind values provides a barely noticeable improvement. For a longer-term forecast of more than 2 days, predictions can be made using recurrence only, while solar wind measurements provide very little improvement for a forecast with long horizon times. We also examine predictions for disturbed and quiet geomagnetic activity conditions. Our results show that the paucity of historical measurements of the solar wind for high Kp results in a lower accuracy of predictions during disturbed conditions. Rebalancing of input data can help tailor the predictions for more disturbed conditions.},
  language  = {en}
}
@article{RichterHainzlDahmetal.2020,
  author    = {Richter, Gudrun and Hainzl, Sebastian and Dahm, Torsten and Z{\"o}ller, Gert},
  title     = {Stress-based, statistical modeling of the induced seismicity at the Groningen gas field},
  series = {Environmental earth sciences},
  volume    = {79},
  journal   = {Environmental earth sciences},
  number    = {11},
  publisher = {Springer},
  address   = {New York},
  issn      = {1866-6280},
  doi       = {10.1007/s12665-020-08941-4},
  pages     = {15},
  year      = {2020},
  abstract  = {Groningen is the largest onshore gas field under production in Europe. The pressure depletion of the gas field started in 1963. In 1991, the first induced micro-earthquakes have been located at reservoir level with increasing rates in the following decades. Most of these events are of magnitude less than 2.0 and cannot be felt. However, maximum observed magnitudes continuously increased over the years until the largest, significant event with ML=3.6 was recorded in 2014, which finally led to the decision to reduce the production. This causal sequence displays the crucial role of understanding and modeling the relation between production and induced seismicity for economic planing and hazard assessment. Here we test whether the induced seismicity related to gas exploration can be modeled by the statistical response of fault networks with rate-and-state-dependent frictional behavior. We use the long and complete local seismic catalog and additionally detailed information on production-induced changes at the reservoir level to test different seismicity models. Both the changes of the fluid pressure and of the reservoir compaction are tested as input to approximate the Coulomb stress changes. We find that the rate-and-state model with a constant tectonic background seismicity rate can reproduce the observed long delay of the seismicity onset. In contrast, so-called Coulomb failure models with instantaneous earthquake nucleation need to assume that all faults are initially far from a critical state of stress to explain the delay. Our rate-and-state model based on the fluid pore pressure fits the spatiotemporal pattern of the seismicity best, where the fit further improves by taking the fault density and orientation into account. Despite its simplicity with only three free parameters, the rate-and-state model can reproduce the main statistical features of the observed activity.},
  language  = {en}
}