@phdthesis{Bach2013,
  author    = {Bach, Christoph},
  title     = {Improving statistical seismicity models},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-70591},
  school      = {Universit{\"a}t Potsdam},
  year      = {2013},
  abstract  = {Several mechanisms are proposed to be part of the earthquake triggering process, including static stress interactions and dynamic stress transfer. Significant differences of these mechanisms are particularly expected in the spatial distribution of aftershocks. However, testing the different hypotheses is challenging because it requires the consideration of the large uncertainties involved in stress calculations as well as the appropriate consideration of secondary aftershock triggering which is related to stress changes induced by smaller pre- and aftershocks. In order to evaluate the forecast capability of different mechanisms, I take the effect of smaller--magnitude earthquakes into account by using the epidemic type aftershock sequence (ETAS) model where the spatial probability distribution of direct aftershocks, if available, is correlated to alternative source information and mechanisms. Surface shaking, rupture geometry, and slip distributions are tested. As an approximation of the shaking level, ShakeMaps are used which are available in near real-time after a mainshock and thus could be used for first-order forecasts of the spatial aftershock distribution. Alternatively, the use of empirical decay laws related to minimum fault distance is tested and Coulomb stress change calculations based on published and random slip models. For comparison, the likelihood values of the different model combinations are analyzed in the case of several well-known aftershock sequences (1992 Landers, 1999 Hector Mine, 2004 Parkfield). The tests show that the fault geometry is the most valuable information for improving aftershock forecasts. Furthermore, they reveal that static stress maps can additionally improve the forecasts of off--fault aftershock locations, while the integration of ground shaking data could not upgrade the results significantly. In the second part of this work, I focused on a procedure to test the information content of inverted slip models. This allows to quantify the information gain if this kind of data is included in aftershock forecasts. For this purpose, the ETAS model based on static stress changes, which is introduced in part one, is applied. The forecast ability of the models is systematically tested for several earthquake sequences and compared to models using random slip distributions. The influence of subfault resolution and segment strike and dip is tested. Some of the tested slip models perform very good, in that cases almost no random slip models are found to perform better. Contrastingly, for some of the published slip models, almost all random slip models perform better than the published slip model. Choosing a different subfault resolution hardly influences the result, as long the general slip pattern is still reproducible. Whereas different strike and dip values strongly influence the results depending on the standard deviation chosen, which is applied in the process of randomly selecting the strike and dip values.},
  language  = {en}
}
@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}
}