Hasil untuk "Dynamic and structural geology"

Guangtan Cheng, Ning Zhang

T. Mitsui, P. Ditlevsen, N. Boers et al.

<p>The dominant period of the Late Pleistocene glacial–interglacial cycles is roughly 100 <span class="inline-formula">kyr</span>, rather than other major astronomical periods such as 19, 23, 41, and 400 <span class="inline-formula">kyr</span>. Various models explain this fact through distinct dynamical mechanisms, including synchronization of self-sustained oscillations and resonance in mono- or multi-stable systems. However, the diversity of proposed models and dynamical mechanisms may obscure the essential factor behind the emergence of the <span class="inline-formula">∼100</span> <span class="inline-formula">kyr</span> periodicity. We propose the hypothesis that the ice-sheet climate system responds to astronomical forcing at the <span class="inline-formula">∼100</span> <span class="inline-formula">kyr</span> periodicity because the intrinsic timescale of the system is closer to 100 <span class="inline-formula">kyr</span> than to other major astronomical periods. We support this idea with analyses and sensitivity studies of several simple ice age models with contrasting mechanisms.</p>

A. L. Wild, A. L. Wild, J. Braun et al.

<p>The grain size preserved within the stratigraphic record over thousands to millions of years has several important applications. In particular, it can serve as a record of significant climatic, eustatic, or tectonic events. Here we present a new model for grain size fining predictions that combines a landscape evolution model based on the Stream Power Law but modified for sedimentation <span class="cit" id="xref_paren.1">(<a href="#bib1.bibx34">Yuan et al.</a>, <a href="#bib1.bibx34">2019</a>)</span> with an extension of the self-similar grain size model <span class="cit" id="xref_paren.2">(<a href="#bib1.bibx13">Fedele and Paola</a>, <a href="#bib1.bibx13">2007</a>)</span>. The new model, which we called GravelScape, includes the effects on grain size fining of lateral heterogeneities in deposition rate caused by dynamically evolving channels. We show that, when multi-channel dynamics (i.e. avulsions) are prevented, by reducing the planform model to a single downstream dimension, our new model can reproduce results obtained by other methods that assume that fining is controlled by subsidence only. We demonstrate that including across-basin (two-dimensional) effects can lead to deviations from previous subsidence predictions for grain size fining. The magnitude of these deviations correlates with the extent of sediment bypass and the configuration of surface topography, both of which influence the amplitude of across-basin variability within the sedimentary system.</p>

John E. Ebel

This paper presents a prospective forecast of the locations of the next MW ​≥ ​6.5 earthquakes in California and Nevada based on the locations and rates of occurrence of M ​≥ ​4.0 earthquakes during the past 30 years, called here preshocks. The time period of the forecast is arbitrarily set at 33 years. The forecast faults are the Anza section of the San Jacinto Fault, the Calaveras Fault, the creeping section of the San Andreas Fault, the Maacama Fault, the San Bernardino section of the San Jacinto Fault, and the southern San Andreas Fault, all strike-slip faults in California, and the normal-faulting Wassuk Range Fault in Nevada. The suspected preshocks have occurred randomly along the expected future fault ruptures at rates of at least 0.5 events per year. The temporal history of preshocks for past M ​≥ ​6.5 earthquakes in California do not indicate when the future mainshock will occur. Outside of California, preshock activity was observed before the 2016 MW 7.0 Kumamoto, Japan earthquake, the 2023 MW 7.8 Kahramanmaras, Turkey earthquake, and the 2017 MW 6.5 Jiuzhaigou, China earthquake, all strike-slip events, as well as the 2008 MW 7.9 Wenchuan, China thrust earthquake. The two mainshocks in China had preshock rates less than 0.5 events per year. By publishing this spatial earthquake forecast, seismologists in the future can evaluate whether or not this forecast was a total success, a total failure, or a partial success. The probability of just one of the forecast events actually taking place during the forecast time period is less than 2%.

Ernesto Ausilio, Maria Giovanna Durante, Roberto Cairo et al.

The long-term safety and operational reliability of zoned earth dams depend on the structural integrity of their internal components, including core, filters, and shell zones. This is particularly relevant for old dams which have been operational for a long period of time. Such existing infrastructure systems are exposed to various loading types over time, including environmental, seepage-related, extreme event, and climate change effects. As a result, even when they look intact externally, changes might affect their internal structure, composition, and possibly functionality. Thus, it is important to delineate a comprehensive and cost-effective strategy to identify potential issues and derive the health status of existing earth dams. This paper outlines a systematic approach for conducting a comprehensive health check of these structures through the implementation of a multi-epoch geotechnical approach based on a variety of standard measured and monitored quantities. The goal is to compare current properties with baseline data obtained during pre-, during-, and post-construction site investigation and laboratory tests. Guidance is provided on how to judge such multi-epoch comparisons, identifying potential outcomes and scenarios. The proposed approach is tested on a well-documented case study in Southern Italy, an area prone to climate change and subjected to very high seismic hazard. The case study demonstrates how the integration of historical and contemporary geotechnical data allows for the identification of critical zones requiring attention, the validation of numerical models, and the proactive formulation of targeted maintenance and rehabilitation strategies. This comprehensive, multi-epoch-based approach provides a robust and reliable assessment of dams’ health, enabling better-informed decision-making workflows and processes for asset management and risk mitigation strategies.

Ertian Chen

Estimation and counterfactual analysis in dynamic structural models rely on assumptions about the dynamic process of latent variables, which may be misspecified. We propose a framework to quantify the sensitivity of scalar parameters of interest (e.g., welfare, elasticity) to such assumptions. We derive bounds on the scalar parameter by perturbing a reference dynamic process, while imposing a stationarity condition for time-homogeneous models or a Markovian condition for time-inhomogeneous models. The bounds are the solutions to optimization problems, for which we derive a computationally tractable dual formulation. We establish consistency, convergence rate, and asymptotic distribution for the estimator of the bounds. We demonstrate the approach with two applications: an infinite-horizon dynamic demand model for new cars in the United Kingdom, Germany, and France, and a finite-horizon dynamic labor supply model for taxi drivers in New York City. In the car application, perturbed price elasticities deviate by at most 15.24% from the reference elasticities, while perturbed estimates of consumer surplus from an additional $3,000 electric vehicle subsidy vary by up to 102.75%. In the labor supply application, the perturbed Frisch labor supply elasticity deviates by at most 76.83% for weekday drivers and 42.84% for weekend drivers.

Saad Rahman, Doyal Sarker, Tri Ngo et al.

This paper presents the modeling and verification of multibody structural dynamics for offshore wind turbines. The flexible tower and support structure of a monopile-based offshore wind turbine are modeled using an acausal, lumped-parameter, multibody approach that incorporates structural flexibility, soil-structure interaction, and hydrodynamic models. Simulation results are benchmarked against alternative modeling approaches, demonstrating the model's ability to accurately capture both static and dynamic behaviors under various wind and wave conditions while maintaining computational efficiency. This work provides a valuable tool for analyzing key structural characteristics of wind turbines, including eigenfrequencies, mode shapes, damping, and internal forces.

Chunxiao Ning, Yazhou Xie

Predicting region-wide structural responses under seismic shaking is essential for enhancing the effectiveness of earthquake engineering task forces such as earthquake early warning and regional seismic risk and resilience assessments. Existing domain-specific and data-driven approaches, however, lack the capability to provide high-fidelity, structure-specific dynamic response predictions for large-scale structural inventories in a timely manner. To address this gap, this study designed a novel deep learning framework, which integrates heterogeneous ground motion sequences and partial structural information as model inputs, to predict structure-specific, probabilistic dynamic responses of regional structural portfolios. Validation on a portfolio of highway bridges in California demonstrates the model's ability to capture inter-structure response variability by inputting critical and accessible bridge parameters while accounting for uncertainties due to the lack of other information. The results underscore the framework's efficiency and accuracy, paving the way for various advancements in performance-based earthquake engineering and regional-scale seismic decision-making.

Sagnik Mukherjee, Indrajit Barua

The optimization of structural parameters, such as mass(m), stiffness(k), and damping coefficient(c), is critical for designing efficient, resilient, and stable structures. Conventional numerical approaches, including Finite Element Method (FEM) and Computational Fluid Dynamics (CFD) simulations, provide high-fidelity results but are computationally expensive for iterative optimization tasks, as each evaluation requires solving the governing equations for every parameter combination. This study proposes a hybrid data-driven framework that integrates a Graph Neural Network (GNN) surrogate model with a Genetic Algorithm (GA) optimizer to overcome these challenges. The GNN is trained to accurately learn the nonlinear mapping between structural parameters and dynamic displacement responses, enabling rapid predictions without repeatedly solving the system equations. A dataset of single-degree-of-freedom (SDOF) system responses is generated using the Newmark Beta method across diverse mass, stiffness, and damping configurations. The GA then searches for globally optimal parameter sets by minimizing predicted displacements and enhancing dynamic stability. Results demonstrate that the GNN and GA framework achieves strong convergence, robust generalization, and significantly reduced computational cost compared to conventional simulations. This approach highlights the effectiveness of combining machine learning surrogates with evolutionary optimization for automated and intelligent structural design.

Wei Cao

This paper systematically analyzes the geological characteristics of tight reservoirs, including structural characteristics, sedimentary characteristics, reservoir characteristics, formation pressure and fluid characteristics and temperature field characteristics. The study shows that the distribution of tight reservoirs is significantly influenced by the tectonic environment and sedimentary facies belt, and the reservoirs are characterized by low porosity and permeability, strong heterogeneity and complex pore structure. At the same time, the properties of crude oil are complex and abnormal formation pressure is common. On this basis, this paper constructs a technical system for efficient development of tight reservoirs, covering key technologies such as reservoir evaluation, fracturing reconstruction, displacement technology, numerical simulation and real-time monitoring, and establishes an evaluation index system for technical adaptability from three aspects of reservoir conditions, development effect and economy, and realizes dynamic optimization by coupling multiple methods. In view of the technical bottleneck in the development of tight reservoirs, some improvement directions and suggestions are put forward, such as optimization of reservoir reconstruction technology, improvement of displacement technology, real-time monitoring and dynamic regulation, and optimization of development scheme, in order to provide theoretical basis and technical support for the economic and efficient development of tight reservoirs.

Olusile Akinyele, Olusile Akinyele Babayeju, Dazok Donald Jambol et al.

Reducing drilling risks is paramount to ensuring safer oil and gas operations, and enhanced reservoir characterization plays a critical role in this endeavor. This review delves into the multifaceted approach of utilizing advanced technologies and methodologies for better understanding subsurface conditions, thereby mitigating drilling hazards Reservoir characterization encompasses the comprehensive analysis and interpretation of geological, geophysical, and petrophysical data to create an accurate model of the subsurface environment. By employing high-resolution seismic imaging, well logging, core sampling, and advanced computational modeling, geoscientists and engineers can delineate the structural, stratigraphic, and lithological features of the reservoir. This detailed insight into the subsurface heterogeneities, such as fault systems, fracture networks, and varying rock properties, is crucial for predicting and managing drilling risks. One of the primary risks in drilling operations is the unanticipated encounter with high-pressure zones, which can lead to blowouts and well control incidents. Enhanced reservoir characterization aids in identifying these high-pressure zones and in planning appropriate drilling mud weights and casing programs to counteract such risks. Additionally, understanding the spatial distribution of reservoir properties allows for the optimization of well trajectories, reducing the likelihood of penetrating problematic formations or encountering unexpected fluid contacts. Advanced seismic techniques, including 3D and 4D seismic surveys, have significantly improved the resolution and accuracy of subsurface imaging. These techniques help in identifying subsurface anomalies and discontinuities that could pose risks during drilling. Furthermore, integration of seismic data with real-time drilling data through techniques such as seismic-while-drilling (SWD) provides dynamic updates to the subsurface model, enabling proactive risk management. The use of machine learning and artificial intelligence (AI) in reservoir characterization has also emerged as a powerful tool. By analyzing large datasets from various wells and fields, AI algorithms can predict potential drilling hazards and recommend mitigation strategies. This predictive capability is particularly valuable in complex geological settings where traditional methods may fall short. Moreover, geomechanical modeling, which involves the study of rock mechanical properties and in-situ stresses, is essential for understanding wellbore stability. Enhanced reservoir characterization allows for accurate geomechanical models that predict the response of the subsurface to drilling activities, helping to prevent wellbore collapse, stuck pipe incidents, and other mechanical failures. Collaboration between geoscientists, drilling engineers, and data scientists is vital to maximizing the benefits of enhanced reservoir characterization. By integrating multidisciplinary expertise, the oil and gas industry can develop more robust drilling plans and contingency strategies, ultimately leading to safer and more efficient operations. Enhanced reservoir characterization is a cornerstone of reducing drilling risks in oil and gas operations. Through the integration of advanced seismic imaging, real-time data analytics, machine learning, and geomechanical modeling, the industry can achieve a deeper understanding of subsurface conditions. This comprehensive approach not only mitigates drilling hazards but also enhances operational efficiency and safety, ensuring the sustainable development of hydrocarbon resources.

Xiaozhi Liu, Yue Pan, Jianxiong Zhao et al.

The redox transition between iron and its oxides is of the utmost importance in heterogeneous catalysis, biological metabolism, and geological evolution. The structural characteristics of this reaction may vary based on surrounding environmental conditions, giving rise to diverse physical scenarios. In this study, we explore the atomic-scale transformation of nanosized Fe3O4 under ambient-pressure H2 gas using in-situ environmental transmission electron microscopy. Our results reveal that the internal solid-state reactions dominated by iron diffusion are coupled with the surface reactions involving gaseous O or H species. During reduction, we observe two competitive reduction pathways, namely Fe3O4 → FeO → Fe and Fe3O4 → Fe. An intermediate phase with vacancy ordering is observed during the disproportionation reaction of Fe2+ → Fe0 + Fe3+, which potentially alleviates stress and facilitates ion migration. As the temperature decreases, an oxidation process occurs in the presence of environmental H2O and trace amounts of O2. A direct oxidation of Fe to Fe3O4 occurs in the absence of the FeO phase, likely corresponding to a change in the water vapor content in the atmosphere. This work elucidates a full dynamical scenario of iron redox under realistic conditions, which is critical for unraveling the intricate mechanisms governing the solid-solid and solid-gas reactions.

Jianping Wu, Yan Cai, Wei Wang et al.

S. Cerveny, F. Mallamace, J. Swenson et al.

N. Cross, T. Burchette

Middle Eastern carbonate petroleum reservoirs exhibit a range of heterogeneities which consist of variable combinations of primary stratigraphic and secondary diagenetic and structural characteristics. These produce diverse permeability architectures which can exert a profound influence on reservoir performance during secondary recovery. Of particular importance are laterally persistent discrete zones of elevated permeability (DZEP) that typically make up a volumetrically minor proportion of the reservoir yet show disproportionately high fluid inflow or outflow. The stratigraphic, diagenetic, and structural origins of elevated permeability in Middle Eastern carbonate reservoirs are considered here and the consequences of such features for reservoir performance are discussed. The term DZEP denotes geological sources of elevated permeability at least an order of magnitude greater than background reservoir properties. Stratigraphically organised DZEP comprise coarse-grained layers, event beds or parasequence tops or bases in neritic or platform interior settings. Other origins include bioturbated layers, grainy clinothems, and bed-scale, grain-size variations in shoal deposits. Diagenetic DZEP are typically dissolution horizons with mouldic and touching-vug pore networks or dolomitized intervals which often overprint stratigraphic DZEP. Structural DZEP include individual faults, fracture corridors, and fracture concentrations related to mechanical stratigraphy. During production through natural pressure depletion, DZEP may dominate well productivity. Under secondary recovery, the same intervals may dominate inter-well fluid flow, causing flood conformance issues, cross-zone fluid movement, bypassed pay, and earlier-than-expected water or gas breakthrough to production wells. Optimisation of production and ultimate recovery relies on collecting the correct kinds of data at a sufficiently early stage in the reservoir characterisation process to permit their inclusion in static and dynamic reservoir models.

Julio César Rivadeneira-Moreira

The increasing complexity of geotechnical infrastructures and their exposure to dynamic and variable conditions has motivated the implementation of probabilistic digital twins as a tool for advanced monitoring. This study adopts a systematic literature review methodology, examining recent academic articles that address the development and application of these models in geotechnical contexts. Methodological advances such as the integration of Bayesian inference, stochastic simulations and machine learning were analyzed, which allow representing and updating models in real time, incorporating the uncertainty inherent to ground behavior. Likewise, applications in dams, slopes and tunnels were documented, showing how these systems improve failure prediction and optimize decision making. However, technical and economic challenges related to instrumentation, geological variability, model validation and implementation costs remain. The study concludes that, despite these limitations, probabilistic digital twins represent a significant evolution in structural management, with high potential for adoption in modern civil engineering.

Bethany R. S. Fox, Rukhsana R. Din, A. C. Davidson et al.

Geosciences are central to addressing many of the challenges facing our society and environment today, and geoscience undergraduate degrees can lead to influential and lucrative careers in a range of fields. However, geosciences are one of the least diverse of all STEM subject areas. We present results from a series of workshops held in 2022 focused on understanding the experiences of current or recent undergraduates from under-represented groups on UK geoscience degrees. The workshops focused particularly on the participants’ sense of belonging in their degree programmes. Factors that reduced participants’ sense of belonging can be broadly grouped into unfamiliarity of geosciences amongst family and friends, lack of representation in the discipline, lack of representation among/exclusion by peers, and structural barriers. We present and discuss the recommendations made by participants for strategies to tackle each of these barriers to belonging. These strategies are intended to be practical actions that individual educators can take to enhance belonging in the geosciences.

Li Jiapeng, Liu Runze, Li Yabo et al.

Multi-hop question answering is a knowledge-intensive complex problem. Large Language Models (LLMs) use their Chain of Thoughts (CoT) capability to reason complex problems step by step, and retrieval-augmentation can effectively alleviate factual errors caused by outdated and unknown knowledge in LLMs. Recent works have introduced retrieval-augmentation in the CoT reasoning to solve multi-hop question answering. However, these chain methods have the following problems: 1) Retrieved irrelevant paragraphs may mislead the reasoning; 2) An error in the chain structure may lead to a cascade of errors. In this paper, we propose a dynamic retrieval framework called Tree of Reviews (ToR), where the root node is the question, and the other nodes are paragraphs from retrieval, extending different reasoning paths from the root node to other nodes. Our framework dynamically decides to initiate a new search, reject, or accept based on the paragraphs on the reasoning paths. Compared to related work, we introduce a tree structure to handle each retrieved paragraph separately, alleviating the misleading effect of irrelevant paragraphs on the reasoning path; the diversity of reasoning path extension reduces the impact of a single reasoning error on the whole. We conducted experiments on three different multi-hop question answering datasets. The results show that compared to the baseline methods, ToR achieves state-of-the-art performance in both retrieval and response generation. In addition, we propose two tree-based search optimization strategies, pruning and effective expansion, to reduce time overhead and increase the diversity of path extension. We will release our code.

Jie Zhang, Ying-Yen Liu, Guangshuai Li et al.

M. Civera, B. Chiaia

Extended Abstract The evaluation of the seismic response expected at road and rail tunnels is a critical factor in ensuring the structural integrity and safety of such critical infrastructures. However, these underground infrastructures are far less investigated than their above-ground counterparts (e.g. bridges and viaducts). Furthermore, it is also much less common to find case studies with fully dynamic monitoring systems; even less in active seismic areas. The dynamic behaviour of man-made tunnels differs substantially according to several factors such as their design and, most importantly, the different geological conditions. Indeed, tunnels excavated at shallow depths in soft soils are generally expected to be more vulnerable to earthquake loads than those bored through dense soil or hard rock. That derives from the kinematic loading induced by the surrounding materials, with their different stiffnesses and amplification effects, by the depth, and other factors. Nevertheless, few direct comparisons, based on experimental recordings, are available in the current scientific literature; for instance, Cui & Ma [1] tested tunnel portal sections located in the soft-hard rock junctions with laboratory shaking table tests; Tsinidis et al. [2] summarised the main empirical findings for different tunnel typologies and soil characteristics; and Cilingir et al. studied the effects of depth on the seismic response of square [3] and circular [4] tunnels – but only with scaled-down laboratory experiments and numerical simulations. To shed light on these aspects, the seismic responses of one underwater rail tunnel and two nearby road mountain tunnels to the same seismic event have been investigated. More specifically, the recordings of the Mw=4.4 Berkeley Earthquake of 04 January 2018 aftermaths on the Bay Area Rapid Transit