ALBERT

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.

Description: Stress maps show the orientation of the current maximum horizontal stress (SHmax) in the earth's crust. Assuming that the vertical stress (SV) is a principal stress, SHmax defines the orientation of the 3D stress tensor; the minimum horizontal stress Shmin is than perpendicular to SHmax. In stress maps SHmax orientations are represented as lines of different lengths. The length of the line is a measure of the quality of data and the symbol shows the stress indicator and the color the stress regime. The stress data are freely available and part of the World Stress Map (WSM) project. For more information about the data and criteria of data analysis and quality mapping are plotted along the WSM website at http://www.world-stress-map.org. The stress map of Taiwan 2022 is based on the WSM database release 2016. However, all data records have been checked and we added a large number of new data from earthquake focal mechanisms from the national earthquake catalog and from publications. The total number of data records has increased from n=401 in the WSM 2016 to n=6,498 (4,234 with A-C quality) in the stress map of Taiwan 2022 The update with earthquake focal mechanims is even larger since another 1313 earthquake focal mechanism data records beyond the scale of this map have been added to the WSM database. The digital version of the stress map is a layered pdf file generated with GMT (Wessel et al., 2019). It also provide estimates of the mean SHmax orientation on a regular 0.1° grid using the tool stress2grid (Ziegler and Heidbach, 2019). Two mean SHmax orientations are estimated with search radii of r=25 and 50 km, respectively, and with weights according to distance and data quality. The stress map and data are available on the landing page at https://doi.org/10.5880/WSM.Taiwan2022 where further information is provided. The earthquake focal mechanism that are used for this stress map are provided by the Taiwan Earthquake Research Center (TEC) available at the TEC Data Center (https://tec.earth.sinica.edu.tw).

Description: This data set contains measurements of an underground hydraulic fracture experiment at Äspö Hard Rock Laboratory in May and June 2015. The experiment tested various injection schemes for rock fracture stimulation and monitored the resulting seismicity. The primary purpose of the experiment is to identify injection schemes that provide rock fracturing while reducing seismicity or at least mitigate larger seismic events. In total, six tests with three different injection schemes were performed in various igneous rock types. Both the injection process and the accompanied seismicity were monitored. For injection monitoring, the water flow and pressure are provided and additional tests for rock permeability. The seismicity was monitored in both triggered and continuous mode during the tests by high-resolution acoustic emission sensors, accelerometers and broadband seismometers. Both waveform data and seismicity catalogs are provided.

Description: In this article, a high-resolution acoustic emission sensor, accelerometer, and broadband seismometer array data set is made available and described in detail from in situ experiments performed at Äspö Hard Rock Laboratory in May and June 2015. The main goal of the hydraulic stimulation tests in a horizontal borehole at 410m depth in naturally fractured granitic rock mass is to demonstrate the technical feasibility of generating multi-stage heat exchangers in a controlled way superiorly to former massive stimulations applied in enhanced geothermal projects. A set of six, sub-parallel hydraulic fractures is propagated from an injection borehole drilled parallel to minimum horizontal in situ stress and is monitored by an extensive complementary sensor array implemented in three inclined monitoring boreholes and the nearby tunnel system. Three different fluid injection protocols are tested: constant water injection, progressive cyclic injection, and cyclic injection with a hydraulic hammer operating at 5 Hz frequency to stimulate a crystalline rock volume of size 30m30m30m at depth. We collected geological data from core and borehole logs, fracture inspection data from an impression packer, and acoustic emission hypocenter tracking and tilt data, as well as quantified the permeability enhancement process. The data and interpretation provided through this publication are important steps in both upscaling laboratory tests and downscaling field tests in granitic rock in the framework of enhanced geothermal system research. Data described in this paper can be accessed at GFZ Data Services under https://doi.org/10.5880/GFZ.2.6.2023.004 (Zang et al., 2023).

Description: 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.

Description: 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 A¨ spo¨ 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 30×30×30 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 19 600 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.

Description: The branch of seismology that deals with strong motion refers to seismic events that are hazardous to society in general.Two aspects drive the development in strong-motion seismology: First comes the societal need to understand the earth-quake hazard and to mitigate the associated risk. While the hazard changed little during human history, the risk in-creases steadily. A growing population—also in the most earthquake-prone regions of the world—and a more and morevulnerable infrastructure contribute to higher exposure to seismic events and higher vulnerability in case an earthquakestruck. The second driver in strong-motion seismology is shared with many other fields: the technological advancement.The available options for processing more and more data is unprecedented in human history and are still not exhausted.Both drivers also pose new challenges as in how to interpret and make use of the data.The scientific question, on the other hand, is clear: What can we learn from the rupture process (the source of earth-quakes), Earth’s structure (the medium through which seismic wave travels), and their interactions (how does an earth-quake affect its surrounding medium)? The question is broad and this thesis can focus only for specific aspects of thisquestion and provide answers for them. To reach the answers, I developed several new algorithms and models, all rootedin the concept of the likelihood function.Seismicity (and population alike) is concentrated along the tectonic plate boundaries. Different earthquake typesoccur at these boundaries and their characteristics in terms of ground shaking are considerably different. It is thereforeimportant to classify earthquakes according to their style of faulting. This classification is the objective of ACE (angularclusterization with expectation-maximization). Founded on the geomechanical principles, ACE provides earthquakeclassifications which can be applied not only for ground-motion related topics but also to study the Earth’s stress field.The development of reliable ground-motion models requires waveform data of high quality. Instrument related errorscan compromise the data quality, however, with large archives of waveform data, the correction for spurious s cannot behandled manually anymore. To alleviate the effect of instrument related data shifts, I developed the integrated combinedbaseline modification (ICBM). This routine is implemented during the data pre-processing and is particularly necessarywhen determining integrated quantities from acceleration records, such as coseismic displacement and radiated seismicenergy.Radiated seismic energy plays a major role in the development of a new type of ground-motion model that uses thesite-dependent energy estimates to model the seismic radiation pattern at lower frequencies of the earthquake amplitudespectrum. This kind of ground-motion model performs better when relating ground motion to earthquake triggeredlandslides, which is demonstrated with the landslides triggered by the 2016MW7.1 Kumamoto earthquake which struckcentral Kyushu (Japan). In this case study, it is also shown that the landslide movement direction is to some extent linkedto the seismic wave polarization.The preferred mathematical model in ground-motion model development is the mixed-effect model. However, themost widely used formalism does not allow data weighting beyond directly related measurement errors and weightsderived from ACE would inadvertently bias the model. To overcome this problem, I derived the model estimators onthe basis of the weighted likelihood. The derivation is exhaustive to allow for any of the currently used model types onthe basis of mixed effects to be augmented with data weighting. This formalism in connection with ACE allows for atransparent model development and also avoids model choices on subjective expert judgment

Description: Der Teil der Seismologie, der sich mit starker Bodenbewegung beschäftigt, bezieht sich auf seismische Ereignisse, dieallgemein ein Gefahrenpotenzial für die Gesellschaft darstellen. Die Seismologie der starken Bodenbewegung wird vonzwei Aspekten angetrieben: An erster Stelle kommt die gesellschaftliche Notwendigkeit, die Erdbebengefährdung zuverstehen und das damit verbundene Risiko zu vermeiden. Während die Gefährdung durch Erdbeben kaum Änderun-gen in der Geschichte der Menschheit unterlag, so wächst das Risiko andererseits kontinuierlich an. Eine wachsendeBevölkerung, insbesondere in den am stärksten von Erdbeben geprägten Regionen der Welt, und eine mehr und mehrstörungsanfällige Infrastruktur, tragen dazu bei, seismische Ereignissen vermehrt ausgesetzt zu sein, bei gleichzeitighöherem Schadenspotenzial. Der zweite Antrieb in der Seismologie wird mit vielen anderen Forschungsfeldern geteilt:der technische Fortschritt. Die verfügbaren Möglichkeiten beim Verarbeiten immer größerer Datenmengen sindbeispiellos in der Geschichte und sind bisher noch nicht erschöpft. Beide Triebfedern stellen aber auch neue Heraus-forderungen dar, inwiefern die Daten zu interpretieren sind und wie man sie nutzbar macht.Andererseits ist die wissenschaftliche Frage klar: Was können wir aus Bruchprozessen (als Erdbebenursachen), demAuf bau der Erde (als Medium, durch welches sich die seismischen Wellen ausbreiten), sowie deren Interaktion (inwiefernbeeinflusst das Beben das umgebende Gesteinsmedium)? Diese Frage ist breit gestellt und diese Abhandlung kann sichletztlich nur auf einige Punkte beziehen und Antworten dazu liefern. Um Antworten zu finden, habe ich mehrere neueAlgorithmen und Modelle entwickelt, die allesamt auf dem Konzept der Likelihood-Funktion beruhen.Seismizität (sowie auch die Bevölkerung) ist stark an den Rändern der tektonischen Platten konzentriert. An denPlattenrändern treten verschiedene Erdbebentypen mit teils erheblich abweichenden Eigenschaften auf. Daher ist esvon Wichtigkeit, Erdbeben nach ihrem Verwerfungstyp zu klassifizieren. Das Ziel von ACE (angular clusterization withexpectation-maximization, zu dt. ungefähr Winkelgruppenbestimmung mit Erwartungswertmaximierung) ist genaudiese Klassifizierung. Auf geomechanischen Prinzipien basierend, können die Erdbebenklassifizierungen mittels ACEnicht nur auf Themen der Bodenbewegungen angewandt werden, sondern auch zur Untersuchung des Spannungsfeldesder Erde herangezogen werden.Der Entwicklung von verlässlichen Bodenbewegungsmodellen bedarf es Wellenformdaten hoher Güte. Instrumentenbezogene Fehler können die Qualität beeinträchtigen, jedoch ist eine manuelle Korrektur großer Datenmengen nichtmehr umsetzbar. Um Instrumentenfehler, die sich in Verschiebungen in den Daten zeigen, zu reduzieren, habe icheine Nulllinienkorrektur entwickelt (ICBM, integrated combined baseline modification, zu dt. integriert kombinierteNulllinienmodifikation). Dieser Algorithmus wird in der Datenvorbereitung eingesetzt und ist insbesondere dannnotwendig, wenn integrierte Größen auf Grundlage von Beschleunigungsdaten bestimmt werden, wie statischer Ver-satz eines Erdbebens als auch abgestrahlte seismische Energie.Abgestrahlte seismische Energie spielt eine herausragende Rolle in der Entwicklung einer neuen Art von Bodenbewe-gungsmodell, welches anstellen von Magnituden stationsabhängige Energieabschätzungen nutzt, um die Erdbebenab-strahlcharakteristik auf tieferen Frequenzen des Erdbebenspektrums zu beschreiben. Diese Art Bodenbewegungsmodellist besser geeignet, wenn Bodenbewegungen in Bezug zu Hangrutschungen, welche durch Erdbeben verursacht wur-den, gesetzt werden. Als Beispiel dienen hier die Hangrutschungen, die 2016 durch das Erdbeben in Zentralkyuschu(Japan) mit einer Momentenmagnitude von 7.1 verursacht wurden. In dieser Fallstudie wird auch aufgezeigt, wie dieBewegungsrichtung der Hangrutschungen zu einem gewissen Grad durch die Ausrichtung des seismischen Wellenfeldesbeeinflusst werden.Das bevorzugte mathematische Modell in der Seismologie zur Beschreibung starker Bodenbewegungen ist das gemis-chte Modell. Jedoch lässt der weitläufig angewendete Formalismus nur die Einbettung von Gewichten in Form vonMessunsicherheiten zu. Gewichte wie sie von ACE erzeugt werden, die in keinem direkten Bezug zur Messgröße stehen,liefern zwangsläufig verzerrte Ergebnisse. Um dieses Problem zu umgehen, habe ich Parameterschätzer auf Basis einergewichteten Likelihood hergeleitet. Die rigorose Herleitung erlaubt sämtliche Arten des gemischten Modells, wie siezur Beschreibung von Bodenbewegungen genutzt werde, mit Datengewichtungen zu kombinieren. Dieser Formalis-mus in Verbindung mit ACE erlaubt die Entwicklung nachvollziehbarer Modelle und vermeidet Entscheidungen aufsubjektiver Expertenmeinung.

Description: We investigate the relation between frictional heating on a fault and the resulting conductive surface heat flow anomaly using the fault's long-term energy budget. Analysis of the surface heat flow surrounding the fault trace leads to a constraint on the frictional power generated on the fault—the mechanism behind the San Andreas fault (SAF) heat flow paradox. We revisit this paradox from a new perspective using an estimate of the long-term accumulating elastic power in the region surrounding the fault, and analyze the paradox using two parameters: the seismic efficiency and the elastic power. The results show that the constraint on frictional power from the classic interpretation is incompatible with the accumulating elastic power and the radiated power from earthquake catalogs. We then explore four mechanisms that can resolve this extended paradox. First, stochastic fluctuations of surface heat flow could mask the fault-generated anomaly (we estimate 21% probability). Second, the elastic power accumulating in the region could be overestimated (≥550 MW required). Third, the seismic efficiency—ratio of radiated energy to elastic work—of the SAF could be higher than that of the remaining faults in the region (≥5.8% required). Fourth, the scaled energy—ratio of radiated energy to seismic moment—on the SAF could be lower than on the remaining faults in the region (a factor 5 difference required). In the last three hypotheses, we analyze the interplay of the energy budget on a single fault with the total energy budget of the region.