ALBERT

Description: Ab Weihnachten 2023 kam es zu einer Hochwassersituation vor allem im Bereich der Flüsse Ems, Weser und Elbe, welche bis in den Januar 2024 hinein zu einer angespannten Lage vor allem in weiten Teilen Nordwestdeutschlands führte. Dieses Winterhochwasser wurde ausgelöst durch großräumige Dauerniederschläge vor allem in Norddeutschland über Weihnachten 2023 (19.-25.12.2023) und verstärkt durch nachfolgende, wenn auch schwächere Niederschlagsereignisse bis Anfang Januar 2024, welche darüber hinaus auf bereits gesättigte Böden trafen. Die Ungewöhnlichkeit des Weihnachtsniederschlagsereignisses bestand in seiner großen räumlichen Ausdehnung und langen zeitlichen Andauer von sieben Tagen. Es hing zusammen mit einer Wetterlage (charakterisiert durch ein ausgedehntes Tiefdrucksystem mit Zentrum über Südskandinavien), welche an sich nicht außergewöhnlich war, jedoch extrem lange andauerte. Das spezifische räumliche Muster dieses einwöchigen Niederschlagsereignisses war auch in der Vergangenheit sowohl mit ergiebigen Dauerniederschlägen als auch mit der zuvor erwähnten Wetterlage assoziiert. Die nachfolgenden Niederschlagsereignisse Ende Dezember und Anfang Januar waren für sich betrachtet wesentlich schwächer. Deren zeitliches Zusammenspiel mit dem vorherigen Weihnachtsereignis führte jedoch dazu, dass gebietsweise über lange Zeiträume von bis zu zweieinhalb Wochen extreme mittlere Niederschlagsintensitäten auftraten. Das Hochwasser als Auswirkung der Niederschläge war ebenfalls im Wesentlichen durch seine große räumliche Ausdehnung charakterisiert, nur vereinzelt wurden extreme Flusspegelstände gemessen. Unter allen Hochwassern in Deutschland seit 1955 (für welche die Abflüsse über zwei Wochen eine Wiederkehrzeit von mindestens 10 Jahren aufwiesen) rangiert die räumliche Ausdehnung des Weihnachtshochwassers 2023 mit gut 100.000 km² auf Platz 9. Die räumliche Ausdehnung ist hierbei nicht die Überflutungsfläche, sondern die Fläche, in der die Hochwasserabflüsse einen bestimmten Schwellenwert überschreiten. Die Überflutungsfläche selbst erreichte eine Ausdehnung von ca. 1000 km² und betraf mehr als 40 Landkreise, vor allem in Niedersachsen und Bremen, aber auch in Teilen Hessens und Nordrhein-Westfalens. Betroffen waren dabei, je nach Abschätzung, 18.000 bis 30.000 Personen, rund 2000 Gebäude, 4,6 km² bebaute Fläche und 470 km Straßen. Die im Dezember 2023 gemessene Monatsniederschlagssumme von 164 mm (über einem besonders von den Niederschlägen betroffenen Gebiet in Niedersachsen: 51,5°N - 53,5°N, 8,0°O - 11,0°O) tritt in den Wintermonaten im heutigen Klima durchschnittlich nur ca. alle 120 Jahre auf. Eine Attributionsstudie des Deutschen Wetterdienstes (DWD) zeigt, dass sich die Wahrscheinlichkeit für ein Ereignis dieser Intensität aufgrund der bisherigen Klimaerwärmung von 1,2°C (seit etwa 1900) um den Faktor 1,8 (Ergebnisspanne: 0,1 bis 140) erhöht hat, und dass sich diese Wahrscheinlichkeit im Falle eines 2°C wärmeren Klimas, d.h. einer zusätzlichen Erwärmung um weitere 0,8°C, nochmals erhöhen wird. Die Studie zeigt jedoch auch, dass diese Abschätzungen mit großen Unsicherheiten verbunden sind. Dennoch sind diese Abschätzungen konsistent mit den Ergebnissen verschiedener Studien, welche sowohl eine Zunahme des mittleren Winterniederschlags zeigen als auch eine Intensivierung extremer Niederschlagsereignisse im nördlichen Mitteleuropa.

Description: As a population parameter, obtaining a reliable estimation of the b-value is inherently complex, especially when considering spatial variability. To tackle this issue, we adopt an approach that treats the spatial b-value distribution as a non-stationary Gauss process for the underlying earthquake-realizing Poisson process. For Gauss process inference, it is necessary to specify the covariance, which in this context describes the spatial correlation of the b-value, a priori. We formulate the anisotropic covariance as another Gauss process based on the local fault structure. The covariance anisotropy characterizes, in terms of the b-value, the correlation between earthquakes on a fault, which is higher than between an on-fault earthquake and an off-fault earthquake (or an event on another fault). This adaptive feature captures the geological structure more effectively than an isotropic covariance or similarly defined and commonly used running-window estimates of the b-value. In our research, we demonstrate the Bayesian inference of the Gauss process b-value estimation for several regions with dense earthquake catalogs and fault catalogs, such as southern California based on the SCEDC earthquake catalog and UCERF3 fault model. Our model provides a continuous b-value estimate (including its uncertainties) that reflects the local fault structure to a very high degree. We can associate the b-value with the local seismicity distribution and major faults with higher resolution than conventional (isotropic) estimation methods. Furthermore, in light of the Turkish earthquake sequence in 2023, we also assess the spatial variability of the b-value of aftershocks and their association with various faults in the region.

Description: Distributed acoustic sensing (DAS) sees increased utilization in the seismological community in recent years and various applications are investigated for the usage of DAS in different branches of seismology. Strong-motion seismology uses records of earthquakes of engineering concern (MW〉4.5) with hypocentral distances within few hundreds of kilometers. This demands dense networks over a wide area and installation of typical strong-motion instruments (accelerometers) can be achieved quickly and at a reasonable budget, compared to other network types. For DAS, installation and operation are more involved, and deployment is very still limited. Consequently, DAS recordings of nearby large events are still very unlikely and rare compared to accelerometers. On September 18, 2022, a shallow earthquake sequence with a M〈sub〉W〈/sub〉 6.9 mainshock struck near Chishang (Taiwan) and was recorded by DAS in Hualien city, appr. 100 km north. Shaking of the mainshock and several aftershocks were noticeable in Hualien, though not damaging with PGA recorded at 0.28 m/s^2 nearby the DAS site. The DAS campaign was originally conceptualized as a test suite with different fiber installations: including buried, within a gutter (as in commercial fiber installation) and loose within a basement. The test site is in an urban area affected by surface rupturing during the 2018 Hualien earthquake. The presented recordings provide not only an unprecedented insight how strong-motion appears on DAS but also how effective different installation techniques are for this kind of event. The waveforms are also compared to records of a collocated broadband seismometer and an accelerometer 1 km away.

Description: Distributed Acoustic Sensing (DAS) is used to record high-spatial resolution strain-rate data. For ground motion observation, the DAS data can be converted from strain rate to acceleration or velocity by array-based measurements with coherent plane waves. DAS provides an opportunity to map high-resolution shaking patterns near faults. We installed collocated geophones and optical fiber in Hualien City (a very seismically active area in Taiwan) from the end of January to the end of February in 2022. Earthquakes with magnitudes (Mw) between 3.2 and 5.4 have been recorded. These records illustrate the typical magnitude-distance dependence of ground-motion but also show saturation for higher magnitudes and/or at shorter distances (e.g for an earthquake of Mw 5.2 earthquake recorded at 100 km). For frequency-based analyses, clipped signals on DAS result in challenges not present in classical instruments (seismometers). The upper limit in dynamic range of seismometers results in easily identifiable trapezoidal signals. The dynamic range of DAS interrogators is limited by gauge length, sampling frequency, and wrapped phase in the interferometric phase demodulation. We observe that clipped DAS signals not only affect time series but also contaminate their spectra on all frequencies, due to the random nature of clipping in DAS—contrasting to the flat plateaus in clipped time series on seismometers. Therefore, the identification of the start and end points of clipped DAS records poses a major challenge, which we aim to resolve with a neural network. This approach enhances the efficiency for quality control of massive DAS datasets.

Description: Rapid assessment of an earthquake’s impact on the affected society is a crucial first step of disaster management, determining further emergency measures. We demonstrate that macroseismic observations, collected as felt reports via the LastQuake service of the European Mediterranean Seismological Center, can be utilized to estimate the probability of a felt earthquake to have a “high impact” rather than a “low impact” on the affected population on a global scale. In our fully data-driven, transparent, and reproducible approach we compare the distribution of felt reports to documented earthquake impact in terms of economic losses, number of fatalities, and number of damaged or destroyed buildings. Using the distribution of felt-reports as predictive parameters and an impact measure as the target parameter, we infer a probabilistic model utilizing Bayes’ theorem and Kernel Density Estimation, that provides the probability of an earthquake to be “high impact”. For 393 felt events in 2021, a sufficient number of felt reports to run the model is collected within 10 minutes after the earthquake. While a clean separation of “high-impact” and “low-impact” events remains a challenging task, unambiguous identification of many “low-impact” events in our dataset is identified as a key strength of our approach. We consider our method a complementary and inexpensive impact assessment tools, that can be utilized instantly in all populated areas on the planet, with the necessary technological infrastructure. Being fully independent of seismic data, our framework poses an affordable option to support disaster management in regions that currently lack expensive seismic instrumentation.

Description: The Taiwan Milun fault zone located at the boundary between the Eurasian and Philippine Sea plates. This fault slips frequently and produced large earthquakes, as for example the Mw6.4 Hualien earthquake (6 February 2018). We map and observe the fault zone and its behavior at depth by high spatial resolution dynamic strain sensing with optical fiber. In 2021-2022, we drilled and cored the fault, and deployed a 3D multi-cross-fault fiber array comprising a borehole loop with a depth of 700 m (Hole-A, Hanging wall site, crossing the fault at depth), a surface array crossing the fault rupture zone using commercial fiber, and a second borehole loop of 500m fiber (Hole-B, Footwall site). The high spatial resolution from distributed acoustic sensing (DAS) and the retrieved core combined with geophysical logs allow us to characterize the structure on meter-scale. Within the Milun fault zone, we identified a 20-m wide fault core comprised of gray and black gouge in the core sample. DAS strain-rate records associated with the same depth as the fault core show a distinct amplification. The amplification ratio of 2.5-3 is constant as for all types of events (local, teleseismic ), when compared to DAS channels at larger depth, related to a consolidated rock material. Although the fault gouge is narrow, the nature of the amplification in strain is due to its strong material contrast from fault gouge. This result may shed the light on the understanding of fault-zone dynamics in terms of remote earthquake triggering and near-fault ground motion.

Description: As a population parameter, reliable estimation of the b-value is intrinsically complicated, particularly when spatial variability is considered. We approach this issue by treating the spatial b-value distribution as a non-stationary Gaussian process for the underlying earthquake-realizing Poisson process. For Gaussian process inference the covariance—which describes here the spatial correlation of the b-value—must be specified a priori. We base the covariance on the local fault structure, i.e. the covariance is anisotropic: elongated along the dominant fault strike and shortened when normal to the fault trace. This adaptive feature captures the geological structure better than an isotropic covariance or similarly defined and commonly used running-window estimates of the b-value. We demonstrate the Bayesian inference of the Gaussian process b-value estimation for two regions: California based on SCEDC earthquake and Turkey based on the AFAD earthquake catalog. The covariances in the inferences are calibrated with the SCEC community fault model the GEM fault model for California and Turkey, respectively. Our model provides a continuous b-value estimate (including its uncertainties) which reflects the local fault structure to a very high degree. We are able to associate the b-value with the local seismicity distribution and link it to major faults. In light of the recent Turkish earthquake sequence, we also assess the temporal evolution of the b-value of recent seismicity before and after major events.