@article{BayonaViverosvonSpechtStraderetal.2019, author = {Bayona Viveros, Jose Antonio and von Specht, Sebastian and Strader, Anne and Hainzl, Sebastian and Cotton, Fabrice Pierre and Schorlemmer, Danijel}, title = {A Regionalized Seismicity Model for Subduction Zones Based on Geodetic Strain Rates, Geomechanical Parameters, and Earthquake-Catalog Data}, series = {Bulletin of the Seismological Society of America}, volume = {109}, journal = {Bulletin of the Seismological Society of America}, number = {5}, publisher = {Seismological Society of America}, address = {Albany}, issn = {0037-1106}, doi = {10.1785/0120190034}, pages = {2036 -- 2049}, year = {2019}, abstract = {The Seismic Hazard Inferred from Tectonics based on the Global Strain Rate Map (SHIFT_GSRM) earthquake forecast was designed to provide high-resolution estimates of global shallow seismicity to be used in seismic hazard assessment. This model combines geodetic strain rates with global earthquake parameters to characterize long-term rates of seismic moment and earthquake activity. Although SHIFT_GSRM properly computes seismicity rates in seismically active continental regions, it underestimates earthquake rates in subduction zones by an average factor of approximately 3. We present a complementary method to SHIFT_GSRM to more accurately forecast earthquake rates in 37 subduction segments, based on the conservation of moment principle and the use of regional interface seismicity parameters, such as subduction dip angles, corner magnitudes, and coupled seismogenic thicknesses. In seven progressive steps, we find that SHIFT_GSRM earthquake-rate underpredictions are mainly due to the utilization of a global probability function of seismic moment release that poorly captures the great variability among subduction megathrust interfaces. Retrospective test results show that the forecast is consistent with the observations during the 1 January 1977 to 31 December 2014 period. Moreover, successful pseudoprospective evaluations for the 1 January 2015 to 31 December 2018 period demonstrate the power of the regionalized earthquake model to properly estimate subduction-zone seismicity.}, language = {en} } @article{HaendelvonSpechtKuehnetal.2015, author = {H{\"a}ndel, Annabel and von Specht, Sebastian and Kuehn, Nicolas M. and Scherbaum, Frank}, title = {Mixtures of ground-motion prediction equations as backbone models for a logic tree: an application to the subduction zone in Northern Chile}, series = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering}, volume = {13}, journal = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering}, number = {2}, publisher = {Springer}, address = {Dordrecht}, issn = {1570-761X}, doi = {10.1007/s10518-014-9636-7}, pages = {483 -- 501}, year = {2015}, abstract = {In probabilistic seismic hazard analysis, different ground-motion prediction equations (GMPEs) are commonly combined within a logic tree framework. The selection of appropriate GMPEs, however, is a non-trivial task, especially for regions where strong motion data are sparse and where no indigenous GMPE exists because the set of models needs to capture the whole range of ground-motion uncertainty. In this study we investigate the aggregation of GMPEs into a mixture model with the aim to infer a backbone model that is able to represent the center of the ground-motion distribution in a logic tree analysis. This central model can be scaled up and down to obtain the full range of ground-motion uncertainty. The combination of models into a mixture is inferred from observed ground-motion data. We tested the new approach for Northern Chile, a region for which no indigenous GMPE exists. Mixture models were calculated for interface and intraslab type events individually. For each source type we aggregated eight subduction zone GMPEs using mainly new strong-motion data that were recorded within the Plate Boundary Observatory Chile project and that were processed within this study. We can show that the mixture performs better than any of its component GMPEs, and that it performs comparable to a regression model that was derived for the same dataset. The mixture model seems to represent the median ground motions in that region fairly well. It is thus able to serve as a backbone model for the logic tree.}, language = {en} } @article{KwiatekMartinezGarzonPlenkersetal.2018, author = {Kwiatek, Grzegorz and Martinez-Garzon, Patricia and Plenkers, K. and Leonhardt, Maria and Zang, Arno and von Specht, Sebastian and Dresen, Georg and Bohnhoff, Marco}, title = {Insights into complex subdecimeter fracturing processes occurring during a water injection experiment at depth in Aspo Hard Rock Laboratory, Sweden}, series = {Journal of geophysical research : Solid earth}, volume = {123}, journal = {Journal of geophysical research : Solid earth}, number = {8}, publisher = {American Geophysical Union}, address = {Washington}, issn = {2169-9313}, doi = {10.1029/2017JB014715}, pages = {6616 -- 6635}, year = {2018}, abstract = {We investigate the source characteristics of picoseismicity (M-w < -2) recorded during a hydraulic fracturing in situ experiment performed in the underground Aspo Hard Rock Laboratory, Sweden. The experiment consisted of six stimulations driven by three different water injection schemes and was performed inside a 28-m-long, horizontal borehole located at 410-m depth. The fracturing processes were monitored with a variety of seismic networks including broadband seismometers, geophones, high-frequency accelerometers, and acoustic emission sensors thereby covering a wide frequency band between 0.01 and 100,000Hz. Here we study the high-frequency signals with dominant frequencies exceeding 1000 Hz. The combined seismic network allowed for detection and detailed analysis of 196 small-scale seismic events with moment magnitudes M-W < -3.5 (source sizes of decimeter scale) that occurred solely during the stimulations and shortly after. The double-difference relocated hypocenter catalog as well as source parameters were used to study the physical characteristics of the induced seismicity and then compared to the stimulation parameters. We observe a spatiotemporal migration of the picoseismic events away and toward the injection intervals in direct correlation with changes in the hydraulic energy (product of fluid injection pressure and injection rate). We find that the total radiated seismic energy is extremely low with respect to the product of injected fluid volume and pressure (hydraulic energy). The radiated seismic energy correlates well with the hydraulic energy rate. The obtained fault plane solutions for particularly well-characterized events signify the reactivation of preexisting rock defects under influence of increased pore fluid pressure on fault plane orientations in good correspondence with the local stress field orientation.}, language = {en} } @article{LilienkampvonSpechtWeatherilletal.2022, author = {Lilienkamp, Henning and von Specht, Sebastian and Weatherill, Graeme and Caire, Giuseppe and Cotton, Fabrice}, title = {Ground-Motion modeling as an image processing task}, series = {Bulletin of the Seismological Society of America}, volume = {112}, journal = {Bulletin of the Seismological Society of America}, number = {3}, publisher = {Seismological Society of America}, address = {Albany}, issn = {0037-1106}, doi = {10.1785/0120220008}, pages = {1565 -- 1582}, year = {2022}, abstract = {We construct and examine the prototype of a deep learning-based ground-motion model (GMM) that is both fully data driven and nonergodic. We formulate ground-motion modeling as an image processing task, in which a specific type of neural network, the U-Net, relates continuous, horizontal maps of earthquake predictive parameters to sparse observations of a ground-motion intensity measure (IM). The processing of map-shaped data allows the natural incorporation of absolute earthquake source and observation site coordinates, and is, therefore, well suited to include site-, source-, and path-specific amplification effects in a nonergodic GMM. Data-driven interpolation of the IM between observation points is an inherent feature of the U-Net and requires no a priori assumptions. We evaluate our model using both a synthetic dataset and a subset of observations from the KiK-net strong motion network in the Kanto basin in Japan. We find that the U-Net model is capable of learning the magnitude???distance scaling, as well as site-, source-, and path-specific amplification effects from a strong motion dataset. The interpolation scheme is evaluated using a fivefold cross validation and is found to provide on average unbiased predictions. The magnitude???distance scaling as well as the site amplification of response spectral acceleration at a period of 1 s obtained for the Kanto basin are comparable to previous regional studies.}, language = {en} } @article{NiemzCescaHeimannetal.2020, author = {Niemz, Peter and Cesca, Simone and Heimann, Sebastian and Grigoli, Francesco and von Specht, Sebastian and Hammer, Conny and Zang, Arno and Dahm, Torsten}, title = {Full-waveform-based characterization of acoustic emission activity in a mine-scale experiment}, series = {Geophysical journal international / the Royal Astronomical Society, the Deutsche Geophysikalische Gesellschaft and the European Geophysical Society}, volume = {222}, journal = {Geophysical journal international / the Royal Astronomical Society, the Deutsche Geophysikalische Gesellschaft and the European Geophysical Society}, number = {1}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {0955-419X}, doi = {10.1093/gji/ggaa127}, pages = {189 -- 206}, year = {2020}, abstract = {Understanding fracturing processes and the hydromechanical relation to induced seismicity is a key question for enhanced geothermal systems (EGS). Commonly massive fluid injection, predominately causing hydroshearing, are used in large-scale EGS but also hydraulic fracturing approaches were discussed. To evaluate the applicability of hydraulic fracturing techniques in EGS, six in situ, multistage hydraulic fracturing experiments with three different injection schemes were performed under controlled conditions in crystalline rock at the Aspo Hard Rock Laboratory (Sweden). During the experiments the near-field ground motion was continuously recorded by 11 piezoelectric borehole sensors with a sampling rate of 1 MHz. The sensor network covered a volume of 30x30x30 m around a horizontal, 28-m-long injection borehole at a depth of 410 m. To extract and characterize massive, induced, high-frequency acoustic emission (AE) activity from continuous recordings, a semi-automated workflow was developed relying on full waveform based detection, classification and location procedures. The approach extended the AE catalogue from 196 triggered events in previous studies to more than 19600 located AEs. The enhanced catalogue, for the first time, allows a detailed analysis of induced seismicity during single hydraulic fracturing experiments, including the individual fracturing stages and the comparison between injection schemes. Beside the detailed study of the spatio-temporal patterns, event clusters and the growth of seismic clouds, we estimate relative magnitudes and b-values of AEs for conventional, cyclic progressive and dynamic pulse injection schemes, the latter two being fatigue hydraulic fracturing techniques. While the conventional fracturing leads to AE patterns clustered in planar regions, indicating the generation of a single main fracture plane, the cyclic progressive injection scheme results in a more diffuse, cloud-like AE distribution, indicating the activation of a more complex fracture network. For a given amount of hydraulic energy (pressure multiplied by injected volume) pumped into the system, the cyclic progressive scheme is characterized by a lower rate of seismicity, lower maximum magnitudes and significantly larger b-values, implying an increased number of small events relative to the large ones. To our knowledge, this is the first direct comparison of high resolution seismicity in a mine-scale experiment induced by different hydraulic fracturing schemes.}, language = {en} } @article{SocquetValdesJaraetal.2017, author = {Socquet, Anne and Valdes, Jesus Pina and Jara, Jorge and Cotton, Fabrice Pierre and Walpersdorf, Andrea and Cotte, Nathalie and von Specht, Sebastian and Ortega-Culaciati, Francisco and Carrizo, Daniel and Norabuena, Edmundo}, title = {An 8month slow slip event triggers progressive nucleation of the 2014 Chile megathrust}, series = {Geophysical research letters}, volume = {44}, journal = {Geophysical research letters}, publisher = {American Geophysical Union}, address = {Washington}, issn = {0094-8276}, doi = {10.1002/2017GL073023}, pages = {4046 -- 4053}, year = {2017}, abstract = {The mechanisms leading to large earthquakes are poorly understood and documented. Here we characterize the long-term precursory phase of the 1 April 2014 M(w)8.1 North Chile megathrust. We show that a group of coastal GPS stations accelerated westward 8months before the main shock, corresponding to a M(w)6.5 slow slip event on the subduction interface, 80\% of which was aseismic. Concurrent interface foreshocks underwent a diminution of their radiation at high frequency, as shown by the temporal evolution of Fourier spectra and residuals with respect to ground motions predicted by recent subduction models. Such ground motions change suggests that in response to the slow sliding of the subduction interface, seismic ruptures are progressively becoming smoother and/or slower. The gradual propagation of seismic ruptures beyond seismic asperities into surrounding metastable areas could explain these observations and might be the precursory mechanism eventually leading to the main shock.}, language = {en} } @article{VehKorupvonSpechtetal.2019, author = {Veh, Georg and Korup, Oliver and von Specht, Sebastian and R{\"o}ßner, Sigrid and Walz, Ariane}, title = {Unchanged frequency of moraine-dammed glacial lake outburst floods in the Himalaya}, series = {Nature climate change}, volume = {9}, journal = {Nature climate change}, number = {5}, publisher = {Nature Publ. Group}, address = {London}, issn = {1758-678X}, doi = {10.1038/s41558-019-0437-5}, pages = {379 -- 383}, year = {2019}, abstract = {Shrinking glaciers in the Hindu Kush-Karakoram-Himalaya-Nyainqentanglha (HKKHN) region have formed several thousand moraine-dammed glacial lakes(1-3), some of these having grown rapidly in past decades(3,4). This growth may promote more frequent and potentially destructive glacial lake outburst floods (GLOFs)(5-7). Testing this hypothesis, however, is confounded by incomplete databases of the few reliable, though selective, case studies. Here we present a consistent Himalayan GLOF inventory derived automatically from all available Landsat imagery since the late 1980s. We more than double the known GLOF count and identify the southern Himalayas as a hotspot region, compared to the more rarely affected Hindu Kush-Karakoram ranges. Nevertheless, the average annual frequency of 1.3 GLOFs has no credible posterior trend despite reported increases in glacial lake areas in most of the HKKHN3,8, so that GLOF activity per unit lake area has decreased since the late 1980s. We conclude that learning more about the frequency and magnitude of outburst triggers, rather than focusing solely on rapidly growing glacial lakes, might improve the appraisal of GLOF hazards.}, language = {en} } @phdthesis{vonSpecht2019, author = {von Specht, Sebastian}, title = {Likelihood - based optimization in strong-motion seismology}, school = {Universit{\"a}t Potsdam}, pages = {153}, year = {2019}, language = {en} } @article{vonSpechtCotton2020, author = {von Specht, Sebastian and Cotton, Fabrice Pierre}, title = {A link between machine learning and optimization in ground-motion model development}, series = {Bulletin of the Seismological Society of America}, volume = {110}, journal = {Bulletin of the Seismological Society of America}, number = {6}, publisher = {Seismological Society of America}, address = {Albany}, issn = {0037-1106}, doi = {10.1785/0120190133}, pages = {2777 -- 2800}, year = {2020}, abstract = {The steady increase of ground-motion data not only allows new possibilities but also comes with new challenges in the development of ground-motion models (GMMs). Data classification techniques (e.g., cluster analysis) do not only produce deterministic classifications but also probabilistic classifications (e.g., probabilities for each datum to belong to a given class or cluster). One challenge is the integration of such continuous classification in regressions for GMM development such as the widely used mixed-effects model. We address this issue by introducing an extension of the mixed-effects model to incorporate data weighting. The parameter estimation of the mixed-effects model, that is, fixed-effects coefficients of the GMMs and the random-effects variances, are based on the weighted likelihood function, which also provides analytic uncertainty estimates. The data weighting permits for earthquake classification beyond the classical, expert-driven, binary classification based, for example, on event depth, distance to trench, style of faulting, and fault dip angle. We apply Angular Classification with Expectation-maximization, an algorithm to identify clusters of nodal planes from focal mechanisms to differentiate between, for example, interface- and intraslab-type events. Classification is continuous, that is, no event belongs completely to one class, which is taken into account in the ground-motion modeling. The theoretical framework described in this article allows for a fully automatic calibration of ground-motion models using large databases with automated classification and processing of earthquake and ground-motion data. As an example, we developed a GMM on the basis of the GMM by Montalva et al. (2017) with data from the strong-motion flat file of Bastias and Montalva (2016) with similar to 2400 records from 319 events in the Chilean subduction zone. Our GMM with the data-driven classification is comparable to the expert-classification-based model. Furthermore, the model shows temporal variations of the between-event residuals before and after large earthquakes in the region.}, language = {en} } @article{vonSpechtHeidbachCottonetal.2018, author = {von Specht, Sebastian and Heidbach, Oliver and Cotton, Fabrice Pierre and Zang, Arno}, title = {Uncertainty reduction of stress tensor inversion with data-driven catalogue selection}, series = {Geophysical journal international}, volume = {214}, journal = {Geophysical journal international}, number = {3}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {0956-540X}, doi = {10.1093/gji/ggy240}, pages = {2250 -- 2263}, year = {2018}, abstract = {The selection of earthquake focal mechanisms (FMs) for stress tensor inversion (STI) is commonly done on a spatial basis, that is, hypocentres. However, this selection approach may include data that are undesired, for example, by mixing events that are caused by different stress tensors when for the STI a single stress tensor is assumed. Due to the significant increase of FM data in the past decades, objective data-driven data selection is feasible, allowing more refined FM catalogues that avoid these issues and provide data weights for the STI routines. We present the application of angular classification with expectation-maximization (ACE) as a tool for data selection. ACE identifies clusters of FM without a priori information. The identified clusters can be used for the classification of the style-of-faulting and as weights of the FM data. We demonstrate that ACE effectively selects data that can be associated with a single stress tensor. Two application examples are given for weighted STI from South America. We use the resulting clusters and weights as a priori information for an STI for these regions and show that uncertainties of the stress tensor estimates are reduced significantly.}, language = {en} }