G. Geogdzhayev, G. Geogdzhayev, A. N. Souza
et al.
<p>Fast emulators of comprehensive climate models are often used to explore the impact of anthropogenic emissions on future climate. A new approach to emulators is introduced that generates means and covariances of monthly averaged climate variables as a function of global mean surface temperature. The emulator is trained with output from a state-of-the-art climate model and serves as a good first-order representation for the evolution of spatially resolved climate variables and their variability. To train the emulator, data is first projected into a reduced-dimensional space; the emulator then learns the dependence of climate variables on global mean surface temperature in the projected space. To recover climate variables in physical space, an inverse transformation is applied. The resulting emulator can cheaply generate means and variances of climate fields averaged over arbitrarily defined regions and in previously unseen warming scenarios. For illustrative purposes, the emulator is applied to predict changes in the mean and variability of monthly values of both surface temperature and relative humidity as a function of global mean surface temperature changes. However, the approach can be applied to any other variable of interest on yearly, monthly or daily timescales.</p>
Vladimir Leonidovich Trushko, Vladimir Yakovlevich Klimov, Elena Konstantinovna Baeva
et al.
Existing pile foundations in the Arctic face significant limitations regarding bearing capacity, environmental impact, and dismantling capabilities. This study proposes and geomechanically justifies a novel technology for constructing dismantlable modular pile foundations in permafrost using a pile with a dome-plug (PDP). Comparative numerical modeling was conducted to analyze the bearing capacity of the proposed PDP versus a conventional pile without a dome-plug (PWDP) across six types of frozen rocks (clays, loams, sandy loams), specifically accounting for salinity. The results indicate that the dome-plug effectively transforms the shell pile into a combined pile-column, providing a bearing capacity increase ranging from 35% to 63%. Notably, the highest relative improvement was observed in the weakest saline rocks. The proposed technology serves as a superior alternative to traditional piling methods, enabling the deployment of modular foundations as a cost-effective and eco-friendly substitute for artificial soil islands.
Subhayan De, Tianhao Yu, Patrick T. Brewick
et al.
Accurate prediction of dynamical response of structural system depends on the correct modeling of that system. However, modeling becomes increasingly challenging when there are many candidate models available to describe the system behavior. Furthermore, uncertainties can be present even for the parameters of these model classes. The plausibility of each input-output model class of the structures with uncertain components can be determined by a Bayesian approach from measured dynamic responses to one or more input records; predictions of the structural system response to alternate input records can then be made. However, this approach may require many model simulations, even though most of those model classes are quite implausible. An approach is proposed herein to use a bound, computed from the false discovery rate, on the likelihood of measured data to falsify models considering uncertainties in the passive control devices that do not reproduce the measured data to sufficient accuracy. Response prediction is then performed using the unfalsified models in an approximate Bayesian sense by assigning weights, computed from the likelihoods, only to the unfalsified models approach incurring only a fraction of the computational cost of the standard Bayesian approach. The proposed approach for response prediction is illustrated using three structural examples: an earthquake-excited four--degree-of-freedom building model with a hysteretic isolation layer; a 1623--degree-of-freedom three-dimensional building model, with tuned mass dampers attached to its roof, subjected to wind loads; and a full-scale four-story base-isolated building tested on world's largest shake table in Japan's E-Defense lab. The results exhibit accurate response predictions and significant computational savings, thereby illustrating the potential of the proposed method.
Bayesian learning has emerged as a compelling and vital research direction in the field of structural dynamics, offering a probabilistic lens to understand and refine the analysis of complex dynamical systems. This review meticulously traces the three-decade evolution of Bayesian learning in structural dynamics, illuminating core principles, groundbreaking methodologies, and diverse applications that have significantly influenced the field. The narrative commences by delving into the basics of Bayesian theory, clarifying essential concepts, and introducing primary methods for deriving posterior distributions, with an in-depth exploration of three types: Laplace approximation, stochastic sampling, and variational inference. Subsequently, the text explores the implementation of two types of Bayesian learning in structural dynamics: physical model learning and data-centric statistical model learning. Physical model learning emphasizes inferring physical model parameters within a Bayesian framework for system identification and prediction, while statistical model learning integrates Bayesian learning methodologies into data-centric statistical modeling within probabilistic machine learning. Both types resonate across various applications, such as modal analysis, model updating, damage detection, and reliability updating, highlighting their pivotal role in enhancing comprehension of dynamical systems and decision-making. The paper also navigates obstacles by proposing ways to enhance existing Bayesian inference strategies. Distinguished from previous research, this study offers a thorough examination of both traditional and cutting-edge Bayesian methods. It not only underscores the transformative influence of Bayesian approaches but also serves as a beacon, guiding researchers in the judicious selection and refinement of suitable methods for various challenges in structural dynamics.
<p>In many regions considered for deep geological repositories (DGR) for nuclear waste, repeated glaciations could occur within the time frame relevant to their long-term post-closure safety (up to 1 million years; Myr). Ice sheets can affect the long-term safety of a DGR by elevating the hydrostatic pressure at DGR depth, altering groundwater flow and chemistry, influencing the frequency and severity of earthquakes, and causing surface bedrock erosion. Therefore, DGR safety assessments must account for uncertainties in future ice-sheet variability, including the timing, frequency and duration of ice sheets at the DGR site. Using coupled ice sheet-climate models to constrain uncertainties in ice-sheet variability over the next 1 Myr is not feasible due to the long timescales involved and substantial computational requirements. Instead, we propose a simplified methodology to assess future ice-sheet variability at potential DGR sites using reconstructions of past ice sheets and global simulations of future climate change. The simulations are conducted using a conceptual climate model driven by changes in insolation and atmospheric CO<span class="inline-formula"><sub>2</sub></span> concentrations resulting from anthropogenic emissions. The model is calibrated with 500 000 years (500 kyr) of climate proxy data inferred from deep-ocean sediments.</p>
<p>Applying this methodology to the planned Swedish DGR site intended for disposal of spent nuclear fuel in Forsmark suggests that the onset of the next glaciation at the site will not occur until 100 kyr after present, even in the absence of anthropogenic CO<span class="inline-formula"><sub>2</sub></span> emissions. However, anthropogenic emissions have the potential to delay the next glaciation in Forsmark by several hundred thousand additional years. Following the initial glaciation, our results suggest that the frequency and duration of subsequent glaciations in Forsmark resemble those observed over the last 800 kyr. Considering uncertainties in anthropogenic emissions and future climate evolution, a wide range of possible future glacial developments is identified. At the extremes of this uncertainty range – developments with a low likelihood of occurrence but relevant for evaluating the robustness of the DGR – we find that Forsmark could experience ice-sheet coverage for nearly half of the next 1 Myr or remain almost entirely ice-free throughout this period. The proposed methodology is easy to implement and applicable to any potential DGR site with a recorded history of glaciations.</p>
Marco Pascal Roth, Alessandro Verdecchia, Rebecca Harrington
et al.
Fluid injection/extraction activity related to hydraulic fracturing can induce earthquakes. Common mechanisms attributed to induced earthquakes include elevated pore pressure, poroelastic stress change, and fault loading through aseismic slip. However, their relative influence is still an open question. Estimating subsurface rock properties, such as pore pressure distribution, crack density, and fracture geometry can help quantify the causal relationship between fluid-rock interaction and fault activation. Inferring rock properties by means of indirect measurement may be a viable strategy to help identify weak structures susceptible to failure in regions where increased seismicity correlates with industrial activity, such as the Western Canada Sedimentary Basin. Here we present in situ estimates of Vp/Vs for 34 induced earthquake clusters in the Kiskatinaw area in northeast British Columbia. We estimate significant changes of up to ±4.5% for nine clusters generally associated with areas of high injection volume. Predominantly small spatiotemporal Vp/Vs variations suggest pore pressure increase plays a secondary role in initiating earthquakes. In contrast, computational rock mechanical models that invoke a decreasing fracture aspect ratio and increasing fluid content in a fluid-saturated porous medium that are consistent with the treatment pressure history better explain the observations.
Recent advances in computer sciences have resulted in a significant improvement in reservoir modeling, which is an important stage in studying and comprehending reservoir geometry and properties. It enables the collection of various types of activities such as seismic, geological, and geophysical aspects in a single container to facilitate the characterization of reservoir continuity and homogeneity. The main goal of this work is to build a three-dimensional reservoir model of the Abu Roash G reservoir in the Hamra oil field with enough detail to represent both vertical and lateral reservoir heterogeneity at the well, multi-well, and field scales. The Late Cenomanian Abu Roash G Member is the main reservoir in the Hamra oil field. It is composed mainly of shale, carbonate and some streaks of sandstone, these streaks are shaly in some parts. Conducting the 3D geostatic model begins with the interpretation of seismic data to detect reflectors and horizons, as well as fault picking to explain the structural framework and frequently delineate the container style with proposed limitations to construct the structural model. The lithology and physical properties of Abu Roash G reservoir rock, including total and effective porosity and fluid saturation, were determined using well log data from four wells in the Hamra field. The constructed 3D geological model of the Abu Roash G has showed that the petrophysical parameters are controlled by the facies distribution and structure elements, whereas properties are the central part to the northern side of the deltaic environment than the other sides of the same environment. The model will be useful in displaying the reservoir community and indicating prospective zones for enhancing the dynamic model to improve the behavior of the flow unit productivity, as well as, section of the best sites for the future drilling.
Tatiana V. Kornyushenko, Nadezhda G. Razjigaeva, Larisa A. Ganzey
et al.
We analyzed human impact on landscapes on the example of Steklyanukha-2 Medieval fortress, which is a multi-layered archaeological site, and adjacent territories. Buried soils, rampart matrix, cultural layer and surface soils were sampled within the fortress. Along with the material sampled at the site, two sections of the Upper Holocene deposits of the high floodplain of the Steklyanukha River were studied. The results of studying the sporo-pollen spectra and diatoms are presented. The paleo-landscapes during the formation of various archaeological cultures have been restored and the signs of anthropogenic impact on vegetation have been identified. The buried soil in the section of the high floodplain is a natural archive for the environment history during the appearance of the people of the Yankovskaya Culture on this territory. The soil was formed under conditions of decreasing watering in the valley; the age is estimated at more than 2 ka. Oxbow lake deposits had been accumulated from 1.6 to 0.5 ka, when the valley was actively developed in the Middle Ages. Floodplain deposits at the top of the sections indicate that the valley has been heavily watered during the Little Ice Age. Pollen signs of settlements of the lower part of the valley in the Early Iron Age, Mohe and Late Middle Ages were found. Signals of the development of secondary birch and oak forests are identified. Ambrosia and Xanthium pollen, which are reliable evidence of agricultural activity in the valley, was found in the cultural layer and sediments that formed in the Middle Ages. The pollen of plants common in anthropogenically disturbed territories was also found. The study of diatoms in a depression within the fortress confirmed the archaeologists’ assumption that it was used to store water reserves. The pollen spectra from the surface soils in the fortress and the high floodplain reflect the active agricultural development of the nearby river valleys since the second half of the 19th century. The largest amount of pollen of alien and synanthropic plants and weeds, as well as spores of pathogenic fungi (pathogens of soybeans and rice) and fire indicators were found here.
Jeremy Rimando, Amy Williamson, Raul Benjamin Mendoza
et al.
The geometry and kinematics of the causative fault of the 27 July 2022 moment magnitude (Mw) 7.0 earthquake, which is one of the strongest to hit northern and central Luzon in the past 30 years, were estimated through inverse modeling of line-of-sight interferometric synthetic aperture radar deformation. We modeled rupture along multiple candidate faults based on fit with the pattern of line-of-sight deformation, consistency with focal mechanisms, and compatibility with the known kinematics of the mapped active faults in the region. Our preferred fault model, located west of and parallel to the Abra River Fault (ARF), exhibits localized reverse-slip (average 67° rake) at 15-35 km down-dip. Peak slip occurs at 13-16 km depth, with 95 cm of pure reverse-slip. The existence of a reverse-slip dominated ARF-parallel fault rupture is consistent with a complex shear partitioning model, wherein the NW-SE oblique plate convergence is accommodated not only by the sinistral strike-slip Philippine Fault Zone and the major subduction zones, but also by minor faults in intervening crustal blocks.
We use high‐resolution earthquake locations to characterize the three‐dimensional structure of active faults in California and how it evolves with fault structural maturity. We investigate the distribution of aftershocks of several recent large earthquakes that occurred on continental strike slip faults of various structural maturity (i.e. various cumulative fault displacement, length, initiation age and slip rate). Aftershocks define a tabular zone of shear deformation surrounding the mainshock rupture plane. Comparing this to geological observations, we conclude that this results from the re‐activation of secondary faults. We observe a rapid fall off of the number of aftershocks at a distance range of 0.06‐0.22 km from the main fault surface of mature faults, and 0.6‐1.0 km from the fault surface of immature faults. The total width of the active shear deformation zone surrounding the main fault plane reaches 1.0‐2.5 km and 6‐9 km for mature and immature faults, respectively. We find that the width of the shear deformation zone decreases as a power law with cumulative fault displacement. Comparing with a dynamic rough fault model, we infer that the narrowing of the shear deformation zone agrees quantitatively with earlier estimates of the smoothing of faults with displacement, both of which are aspects of fault wear. We find that earthquake stress drop decreases with fault displacement and hence with increased smoothness and/or slip rate. This may result from fault healing or the effect of roughness on friction.
<p>With the rapid growth in offshore wind energy, it is important to understand the dynamics of offshore wind farms. Most of the offshore wind farms are currently installed in coastal regions where they are often affected by sea-breezes. In this work, we quantitatively study the recovery processes for coastal wind farms under sea-breeze conditions. We use a modified Borne's method to identify sea breeze days off the west coast
of India in the Arabian Sea. For the identified sea breeze days, we simulate
a hypothetical wind farm covering <span class="inline-formula">50×50 km<sup>2</sup></span> area using the Weather Research and Forecasting (WRF) model driven by realistic initial and boundary conditions. We use three wind farm layouts with the turbines spaced 0.5, 1, and 2 km apart. The results show an interesting power
generation pattern with a peak at the upwind edge and another peak at the
downwind edge due to sea breeze. Wind farms affect the circulation patterns,
but the effects of these modifications are very weak compared to the sea
breezes. Vertical recovery is the dominant factor with more than half of the
momentum extracted by wind turbines being replenished by vertical turbulent
mixing. However, horizontal recovery can also play a strong role for
sparsely packed wind farms. Horizontal recovery is stronger at the edges
where the wind speeds are higher whereas vertical recovery is stronger in
the interior of the wind farms. This is one of the first studies to examine
replenishment processes in offshore wind farms under sea breeze conditions.
It can play an important role in advancing our understanding wind
farm-atmospheric boundary layer interactions.</p>
<p>With the rising share of renewable energy sources like wind energy in the energy mix, high-impact weather events like mid-latitude storms increasingly affect energy production, grid stability and safety and reliable forecasting becomes very relevant for e.g. transmission system operators to allow for actions to reduce imbalances. Traditionally, meteorological forecasts are provided by limited-area weather prediction models (LAMs), which can use high enough model resolution to represent the range of atmospheric scales of motions associated with such storm structures. While generally satisfactory, deterioration and insufficient deepening of large-scale storm structures are observed when they are introduced near the lateral boundaries of the LAM due to inadequate spatial and temporal interpolation. Global models with regional mesh refinement capabilities like the Model for Prediction Across Scales (MPAS) have the potential to provide an alternative, while avoiding sharp resolution jumps and lateral boundaries. In this study, MPAS' capabilities of simulating key evaluation metrics like storm intensity, storm location and storm duration are investigated based on a case study and assessed in comparison with buoy measurements, forecast products from the Climate Forecast System (CFSv2) and simulations with the Weather Research and Forecasting (WRF) LAM. Quasi-uniform and variable-resolution MPAS mesh configurations with different model physics settings are designed to analyze the impact of the mesh refinement and model physics on the model performance. MPAS shows good performance in predicting storm intensity based on the local minimum sea level pressure, while time of local minimum sea level pressure (storm duration) was generally estimated too late (too long) in comparison with the buoy measurements in part due to an early west-wards shift of the storm center in MPAS. The variable-resolution configurations showed a combination of an additional south-westwards shift and deviations in the sea level pressure field south-west of the storm center that introduced additional bias to the time of local minimum sea level pressure at some locations. The study highlights the need for a more detailed analysis of applied mesh refinements for particular applications and emphasizes the importance of methods like data assimilation techniques to prevent model drifts.</p>
The competition of depletion attractions and longer-ranged repulsions between colloidal particles in colloid-polymer mixtures leads to the formation of heterogeneous gel-like structures. For instance, gel networks, i.e., states where the colloids arrange in thin strands that span the whole system occur at low packing fractions for attractions that are stronger than those at the binodal line of equilibrium liquid-fluid phase separation. By using Brownian dynamics simulations we explore the formation, structure, and ageing dynamics of gel networks. We determine reduced network that focus on the essential connections in a gel network. We compare the observed properties to those of bulky gels or cluster fluids. Our results demonstrate that both the structure as well as the (often slow) dynamics of the stable or meta-stable heterogenous states in colloid-polymer mixtures possess distinct features on various length and time scales and thus are richly divers.
Dynamic treatment regimens (DTRs), also known as treatment algorithms or adaptive interventions, play an increasingly important role in many health domains. DTRs are motivated to address the unique and changing needs of individuals by delivering the type of treatment needed, when needed, while minimizing unnecessary treatment. Practically, a DTR is a sequence of decision rules that specify, for each of several points in time, how available information about the individual's status and progress should be used in practice to decide which treatment (e.g., type or intensity) to deliver. The sequential multiple assignment randomized trial (SMART) is an experimental design widely used to empirically inform the development of DTRs. Sample size planning resources for SMARTs have been developed for continuous, binary, and survival outcomes. However, an important gap exists in sample size estimation methodology for SMARTs with longitudinal count outcomes. Further, in many health domains, count data are overdispersed - having variance greater than their mean. We propose a Monte Carlo-based approach to sample size estimation applicable to many types of longitudinal outcomes and provide a case study with longitudinal overdispersed count outcomes. A SMART for engaging alcohol and cocaine-dependent patients in treatment is used as motivation.
The Mangatolu Triple Junction (MTJ) is an intraoceanic back-arc spreading center that is host to at least 3 distinct hydrothermal systems. It is located in the NE Lau Basin, which opened due to rollback of the Pacific plate along the Tonga-Kermadec trench. At the MTJ, three spreading centers meet in a ridge-ridge-ridge (RRR)-type triple junction separating the Tonga plate in the east, the Niuafo’ou microplate in the southwest, and an unnamed microplate in the north. The MTJ is directly linked to the formation and evolution of the Northeast Lau microplate mosaic, as plate fragmentation inevitably results in the formation of triple junctions, but it remains unclear whether the spreading centers are the drivers of plate fragmentation or a consequence of stress relocation related to microplate rotation. Detailed investigation of the geology and structural setting of the MTJ therefore provides valuable insight into the development in the northeast Lau Basin. Here we present the first comprehensive 1:200,000 geological map of the MTJ, based on a compilation of marine geophysical data (hydroacoustics, magnetics, and gravity) derived from 7 research cruises that have investigated the region between 2004 and 2018. Analysis of the mapped geological formations at the MTJ shows the importance of relict arc crust originating from the Tofua Arc in the architecture of the triple junction, which includes three stages of back-arc crust development and extensive off-axis volcanism. The spreading centers along each arm of the MTJ exploit pre-existing crustal weaknesses, interpreted to have formed during initial Lau Basin opening. A reconstruction of the basin opening, based on the mapped features and published spreading rates, revealed that initiation of the MTJ commenced approximately 180,000 years ago, consistent with the very recent and ongoing dynamic evolution of the NE Lau Basin and emerging microplate mosaic. Intersecting fabrics indicate sequential evolution of the 3 arms of the triple junction, with extension along the northeast arm dominant in the early history and more recent extension along the southern and western arms. The results of this study contribute to our growing understanding of the tectonic framework of the northeast Lau Basin and the role of triple junctions in microplate formation.