ALBERT

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: 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: Ground motion with strong‐velocity pulses can cause significant damage to buildings and structures at certain periods; hence, knowing the period and velocity amplitude of such pulses is critical for earthquake structural engineering. However, the physical factors relating the scaling of pulse periods with magnitude are poorly understood. In this study, we investigate moderate but damaging earthquakes (⁠Mw 6–7) and characterize ground‐motion pulses using the method of Shahi and Baker (2014) while considering the potential static‐offset effects. We confirm that the within‐event variability of the pulses is large. The identified pulses in this study are mostly from strike‐slip‐like earthquakes. We further perform simulations using the frequency–wavenumber algorithm to investigate the causes of the variability of the pulse periods within and between events for moderate strike‐slip earthquakes. We test the effect of fault dips, and the impact of the asperity locations and sizes. The simulations reveal that the asperity properties have a high impact on the pulse periods and amplitudes at nearby stations. Our results emphasize the importance of asperity characteristics, in addition to earthquake magnitudes for the occurrence and properties of pulses produced by the forward directivity effect. We finally quantify and discuss within‐ and between‐event variabilities of pulse properties at short distances.

Description: The growth of faults is well studied with field methods, experiments and theoretical models. Fault evolution is largely established from a geometrical and kinematic point of view with respect to the growth of isolated faults and their mutual interaction. However, the dynamics of fault growth (e.g. stress shadowing, damage zone evolution, energy budgets) and the emergence of interactions over various spatial and temporal scales in larger fault networks is a topic of recent interest less illuminated so far. We here introduce a new experimental setup allowing to study “large-n” fault networks evolving in crustal-scale brittle and brittle-ductile analogue models. We document preliminary results helping to demonstrate and verify the capability of the approach. The setup, called “The Expander”, builds on a traditional extensional setup with a basal rubber sheet expanded in one direction. The aspect ratio of the rubber sheet controls its lateral contraction (“Poisson’s effect”) and thus the bulk strain ratio under pure shear conditions. We can thus realize constrictional (prolate) to plane to flattening (oblate) kinematic basal boundary conditions depending on the sheet’s aspect ratio and whether we expand or relax the sheet. Evolving fault networks vary from anastomosing fold-and-thrust belts to conjugate sets of strike-slip fault networks to quasi-parallel normal fault populations, respectively. We apply digital image correlation (DIC) to track the kinematic surface evolution and photogrammetry (structure from motion, SFM) for topography evolution. First observations suggest that strike-slip fault networks in a purely brittle crust under basal pure shear conditions evolve into compartments of synthetic faults, the size of which scale with brittle layer thickness similar to fault spacing. The scaling seems to be controlled by slip partitioned onto the individual faults and mediated by stress shadows. Numerical simulation of the experiment suggests that the compartmentalization might evolve further through sequential de-activation of smaller faults and collapse of deformation into a single regional scale master fault with or without prescribing a zone of crustal weakness (a “seed”). Further experiments are planned to test the fault pattern evolution for different mechanical stratigraphy (brittle-viscous layers, seeds) and kinematic boundary conditions.

Description: Strike-slip faults are classically associated with pull-apart basins where continental crust is thinned between two laterally offset fault segments. We propose a subsidence mechanism to explain the formation of a new type of basin where no substantial segment offset or syn-strike-slip thinning is observed. Such “flexural strike-slip basins” form due to a sediment load creating accommodation space by bending the lithosphere. We use a two-way coupling between the geodynamic code ASPECT and surface-processes code FastScape to show that flexural strike-slip basins emerge if sediment is deposited on thin lithosphere close to a strike-slip fault. These conditions were met at the Andaman Basin Central fault (Andaman Sea, Indian Ocean), where seismic reflection data provide evidence of a laterally extensive flexural basin with a depocenter located parallel to the strike-slip fault trace.

Description: Geodynamic modelling provides a powerful tool to investigate processes in the Earth's crust, mantle, and core that are not directly observable. However, numerical models are inherently subject to the assumptions and simplifications on which they are based. In order to use and review numerical modelling studies appropriately, one needs to be aware of the limitations of geodynamic modelling as well as its advantages. Here, we present a comprehensive yet concise overview of the geodynamic modelling process applied to the solid Earth from the choice of governing equations to numerical methods, model setup, model interpretation, and the eventual communication of the model results. We highlight best practices and discuss their implementations including code verification, model validation, internal consistency checks, and software and data management. Thus, with this perspective, we encourage high-quality modelling studies, fair external interpretation, and sensible use of published work. We provide ample examples, from lithosphere and mantle dynamics specifically, and point out synergies with related fields such as seismology, tectonophysics, geology, mineral physics, planetary science, and geodesy. We clarify and consolidate terminology across geodynamics and numerical modelling to set a standard for clear communication of modelling studies. All in all, this paper presents the basics of geodynamic modelling for first-time and experienced modellers, collaborators, and reviewers from diverse backgrounds to (re)gain a solid understanding of geodynamic modelling as a whole.

Description: A variety of industrial applications of hydrate-based CO2 capture and utilization technologies are hindered by the complex and slow hydrate formation; however, improving CO2 hydrate formation kinetics can be facilitated by adding the accelerators (promoters). In this regard, understanding the promotion mechanisms of these compounds on the hydrate formation at the molecular level would assist in either establishing feasible processes or finding more efficient promoters. In this work, CO2 hydrate growth and formation in the presence of hybrid metal particles (Ag, Cu, and Fe) and urea molecule has been explored through molecular dynamics (MD) simulation at below and above water freezing point. Different criteria were used to characterize and analyse the CO2 hydrate formation kinetics. The outcomes reveal that, although the mixture of Cu, Ag, and Fe metal particles has positive effects on the rate of hydrate formation above the ice point, the mixture of Cu, Fe, and urea (without the inclusion of Ag) in comparison with the other investigated systems, possesses the highest promotion effect on the clathrate hydrate growth rate. This combination of metal particles creates various functions in the solution phase adjacent to the hydrate surface. The metal particles and urea could promote the formation of new cages at the hydrate-solution boundary by decreasing the heat and mass transport resistances of CO2 in water. In addition, the improvement of combined metal particles and urea under water freezing was found to be less substantial. However, the behaviours of combined metal particles without urea at different thermodynamic conditions are quite dissimilar.

Description: The detection of geothermal anomalies using Thermal Infrared (TIR) remote sensing data is challenging because of how sensor specifications (such as the infrared wavelength used for the measurement, spectral dependence of the emissivity, angle at which the measurement is made, state of the surface and height of the sensor above the surface) and physical parameters (such as solar radiation, topography, albedo, soil compaction and coherence of rocks) affect Land Surface Temperature (LST) retrieval and analysis. This work tests whether TIR remote sensing measurements with thorough spatial and temporal sampling can improve LST retrievals. Multi-temporal TIR data from 2000 through 2019 from Landsat 7 and 8 TIR instruments and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were used to detect geothermal areas in the geologically active region of the southern Main Ethiopian Rift. In addition, field-based temperature data from 19 sites were evaluated for comparison to the remotely detected geothermal anomaly areas. We have used the single-channel algorithm and Normalized Difference Vegetation Index (NDVI) method of emissivity retrieval to derive LST for each year. The result shows that the mean LST is highest in 2003 (320.1 K) and lowest in 2019 (303.1 K). The change in mean LST was between −9 K to 13 K. These LST results from ASTER images were validated with MODIS LST products and showed a correlation coefficient 〉0.6. LST of the year 2003 has been much closer to the actual temperature value from field data. Fifteen sites (79%) fit with the identified geothermal anomaly areas. LST values in known geothermal activity sites show no correlation (〈 0.5) with time attesting. That is, even though LST varies with time (e.g., day and night and seasonal changes), the LST of areas with geothermal potential remain more or less constant on yearly basis.

Description: Finding an appropriate satellite image as simultaneous as possible with the sampling time campaigns is challenging. Fusion can be considered as a method of integrating images and obtaining more pixels with higher spatial, spectral and temporal resolutions. This paper investigated the impact of Landsat 8-OLI and Sentinel-2A data fusion on prediction of several toxic elements at a mine waste dump. The 30 m spatial resolution Landsat 8-OLI bands were fused with the 10 m Sentinel-2A bands using various fusion techniques namely hue-saturation-value, Brovey, principal component analysis, Gram-Schmidt, wavelet, and area-to-point regression kriging (ATPRK). ATPRK was the best method preserving both spectral and spatial features of Landsat 8-OLI and Sentinel-2A after fusion. Furthermore, the partial least squares regression (PLSR) model developed on genetic algorithm (GA)-selected laboratory visible-near infrared-shortwave infrared (VNIR–SWIR) spectra yielded more accurate prediction results compared to the PLSR model calibrated on the entire spectra. It was hence, applied to both individual sensors and their ATPRK-fused image. In case of the individual sensors, except for As, Sentinel-2A provided more robust prediction models than Landsat 8-OLI. However, the best performances were obtained using the fused images, highlighting the potential of data fusion to enhance the toxic elements’ prediction models.