@article{FiedlerZoellerHolschneideretal.2018, author = {Fiedler, Bernhard and Z{\"o}ller, Gert and Holschneider, Matthias and Hainzl, Sebastian}, title = {Multiple Change-Point Detection in Spatiotemporal Seismicity Data}, series = {Bulletin of the Seismological Society of America}, volume = {108}, journal = {Bulletin of the Seismological Society of America}, number = {3A}, publisher = {Seismological Society of America}, address = {Albany}, issn = {0037-1106}, doi = {10.1785/0120170236}, pages = {1147 -- 1159}, year = {2018}, abstract = {Earthquake rates are driven by tectonic stress buildup, earthquake-induced stress changes, and transient aseismic processes. Although the origin of the first two sources is known, transient aseismic processes are more difficult to detect. However, the knowledge of the associated changes of the earthquake activity is of great interest, because it might help identify natural aseismic deformation patterns such as slow-slip events, as well as the occurrence of induced seismicity related to human activities. For this goal, we develop a Bayesian approach to identify change-points in seismicity data automatically. Using the Bayes factor, we select a suitable model, estimate possible change-points, and we additionally use a likelihood ratio test to calculate the significance of the change of the intensity. The approach is extended to spatiotemporal data to detect the area in which the changes occur. The method is first applied to synthetic data showing its capability to detect real change-points. Finally, we apply this approach to observational data from Oklahoma and observe statistical significant changes of seismicity in space and time.}, language = {en} } @article{ProkhorovFoersterLesuretal.2018, author = {Prokhorov, Boris E. and F{\"o}rster, Matthias and Lesur, Vincent and Namgaladze, Alexander A. and Holschneider, Matthias and Stolle, Claudia}, title = {Modeling of the ionospheric current system and calculating its}, series = {Magnetic Fields in the Solar System: Planets, Moons and Solar Wind Interactions}, volume = {448}, journal = {Magnetic Fields in the Solar System: Planets, Moons and Solar Wind Interactions}, publisher = {Springer}, address = {Dordrecht}, isbn = {978-3-319-64292-5}, issn = {0067-0057}, doi = {10.1007/978-3-319-64292-5_10}, pages = {263 -- 292}, year = {2018}, abstract = {The additional magnetic field produced by the ionospheric current system is a part of the Earth's magnetic field. This current system is a highly variable part of a global electric circuit. The solar wind and interplanetary magnetic field (IMF) interaction with the Earth's magnetosphere is the external driver for the global electric circuit in the ionosphere. The energy is transferred via the field-aligned currents (FACs) to the Earth's ionosphere. The interactions between the neutral and charged particles in the ionosphere lead to the so-called thermospheric neutral wind dynamo which represents the second important driver for the global current system. Both processes are components of the magnetosphere-ionosphere-thermosphere (MIT) system, which depends on solar and geomagnetic conditions, and have significant seasonal and UT variations. The modeling of the global dynamic Earth's ionospheric current system is the first aim of this investigation. For our study, we use the Potsdam version of the Upper Atmosphere Model (UAM-P). The UAM is a first-principle, time-dependent, and fully self-consistent numerical global model. The model includes the thermosphere, ionosphere, plasmasphere, and inner magnetosphere as well as the electrodynamics of the coupled MIT system for the altitudinal range from 80 (60) km up to the 15 Earth radii. The UAM-P differs from the UAM by a new electric field block. For this study, the lower latitudinal and equatorial electrodynamics of the UAM-P model was improved. The calculation of the ionospheric current system's contribution to the Earth's magnetic field is the second aim of this study. We present the method, which allows computing the additional magnetic field inside and outside the current layer as generated by the space current density distribution using the Biot-Savart law. Additionally, we perform a comparison of the additional magnetic field calculation using 2D (equivalent currents) and 3D current distribution.}, language = {en} } @article{BaerenzungHolschneiderWichtetal.2018, author = {B{\"a}renzung, Julien and Holschneider, Matthias and Wicht, Johannes and Sanchez, Sabrina and Lesur, Vincent}, title = {Modeling and predicting the short-term evolution of the geomagnetic field}, series = {Journal of geophysical research : Solid earth}, volume = {123}, journal = {Journal of geophysical research : Solid earth}, number = {6}, publisher = {American Geophysical Union}, address = {Washington}, issn = {2169-9313}, doi = {10.1029/2017JB015115}, pages = {4539 -- 4560}, year = {2018}, abstract = {We propose a reduced dynamical system describing the coupled evolution of fluid flow and magnetic field at the top of the Earth's core between the years 1900 and 2014. The flow evolution is modeled with a first-order autoregressive process, while the magnetic field obeys the classical frozen flux equation. An ensemble Kalman filter algorithm serves to constrain the dynamics with the geomagnetic field and its secular variation given by the COV-OBS.x1 model. Using a large ensemble with 40,000 members provides meaningful statistics including reliable error estimates. The model highlights two distinct flow scales. Slowly varying large-scale elements include the already documented eccentric gyre. Localized short-lived structures include distinctly ageostophic features like the high-latitude polar jet on the Northern Hemisphere. Comparisons with independent observations of the length-of-day variations not only validate the flow estimates but also suggest an acceleration of the geostrophic flows over the last century. Hindcasting tests show that our model outperforms simpler predictions bases (linear extrapolation and stationary flow). The predictability limit, of about 2,000 years for the magnetic dipole component, is mostly determined by the random fast varying dynamics of the flow and much less by the geomagnetic data quality or lack of small-scale information.}, language = {en} } @article{FiedlerHainzlZoelleretal.2018, author = {Fiedler, Bernhard and Hainzl, Sebastian and Z{\"o}ller, Gert and Holschneider, Matthias}, title = {Detection of Gutenberg-Richter b-Value Changes in Earthquake Time Series}, series = {Bulletin of the Seismological Society of America}, volume = {108}, journal = {Bulletin of the Seismological Society of America}, number = {5A}, publisher = {Seismological Society of America}, address = {Albany}, issn = {0037-1106}, doi = {10.1785/0120180091}, pages = {2778 -- 2787}, year = {2018}, abstract = {The Gutenberg-Richter relation for earthquake magnitudes is the most famous empirical law in seismology. It states that the frequency of earthquake magnitudes follows an exponential distribution; this has been found to be a robust feature of seismicity above the completeness magnitude, and it is independent of whether global, regional, or local seismicity is analyzed. However, the exponent b of the distribution varies significantly in space and time, which is important for process understanding and seismic hazard assessment; this is particularly true because of the fact that the Gutenberg-Richter b-value acts as a proxy for the stress state and quantifies the ratio of large-to-small earthquakes. In our work, we focus on the automatic detection of statistically significant temporal changes of the b-value in seismicity data. In our approach, we use Bayes factors for model selection and estimate multiple change-points of the frequency-magnitude distribution in time. The method is first applied to synthetic data, showing its capability to detect change-points as function of the size of the sample and the b-value contrast. Finally, we apply this approach to examples of observational data sets for which b-value changes have previously been stated. Our analysis of foreshock and after-shock sequences related to mainshocks, as well as earthquake swarms, shows that only a portion of the b-value changes is statistically significant.}, language = {en} }