Engineering Example Research Articles

Sliding Mode Fault-Tolerant Control for Nonlinear LPV Systems with Variable Time-Delay

This paper presents a robust sliding mode fault-tolerant control (FTC) strategy for a class of linear parameter variant (LPV) systems with variable time-delays and uncertainties. First fault estimation (FE) is conducted using a robust sliding mode observer, synthesized to simultaneously estimate the states and actuator faults of LPV polytopic delayed systems. Second, a sliding mode FTC is developed, ensuring all states of the closed-loop system converge to the origin. This paper presents an integrated sliding mode FTC strategy to achieve optimal robustness between the observer and controller models. The integrated design approach offers several advantages over traditional separated FTC methods. Our novel approach is based on incorporating adaptive law into the design of the Lyapunov–Krasovskii functional to improve both robustness and performance. This is achieved by combining the concept of sliding mode control (SMC) with the Lyapunov–Krasovskii function under the H∞ criteria, which plays a key role in guaranteeing the stability of this class of system. The effectiveness of the proposed method is demonstrated through a diesel engine example, which highlights the validity and benefits of the integrated and separated FTC strategy for uncertain nonlinear systems with time delays and the sliding mode control.

Boosting Engineering Optimization With a Novel Recursive Transfer Bifidelity Surrogate Modeling

Abstract In the engineering optimization, there often exist the multiple sources of information with different fidelity levels. In general, low-fidelity (LF) information is usually more accessible than high-fidelity (HF) information, while the latter is usually more accurate than the former. Thus, to capitalize on the advantages of this information, this study proposes a novel recursive transfer bifidelity surrogate modeling to fuse information from HF and LF levels. First, the selection method of optimal scale factor is proposed for constructing bifidelity surrogate model. Then, a recursive method is developed to further improve its performance. The efficacy of the proposed model is comprehensively evaluated using numerical problems and an engineering example. Comparative analysis with some surrogate models (five multifidelity and a single-fidelity surrogate models) demonstrates the superior prediction accuracy and robustness of the proposed model. Additionally, the impact of varying cost ratios and combinations of HF and LF samples on the performance of the proposed model is also investigated, yielding consistent results. Overall, the proposed model has superior performance and holds potential for practical applications in engineering design optimization problems.

Interval prediction method for dynamic zoning of displacements in super-high arch dam under significant water level fluctuations

Displacement is one of the most intuitive indicators reflecting the influence of internal and external environments on dam structures. However, existing displacement monitoring models mostly rely on static fitting of single point and deterministic point value estimation, failing to deeply consider the similarity and uncertainty of displacement in dynamic changes under significant water level fluctuations. To address this issue, the displacements are first divided into three periods: ascending, descending, and stable periods. Second, based on the Ward criterion, the displacements of measuring points in different periods are clustered and partitioned, and the CRITIC method is applied to amalgamate the displacement of multiple points within each partition into comprehensive displacements, which reflect the displacement changes of the corresponding partition. Then, the comprehensive displacements are predicted utilizing deep learning model convolutional neural network-gated recurrent unit (CNN-GRU). Finally, to consider the uncertainty and randomness of displacement changes, prediction intervals based on Bootstrap method are employed to quantify the impact of uncertain factors. Engineering examples show that the proposed method can provide scientific basis and technical support for the safety monitoring and health diagnosis of super-high arch dams.

Single-Crystalline 3D Covalent Organic Frameworks with Exceptionally High Specific Surface Areas and Gas Storage Capacities.

Single-crystalline covalent organic frameworks (COFs) are highly desirable toward understanding their pore chemistry and functions. Herein, two 50-100 μm single-crystalline three-dimensional (3D) COFs, TAM-TFPB-COF and TAPB-TFS-COF, were prepared from the condensation of 4,4',4″,4‴-methanetetrayltetraaniline (TAM) with 3,3',5,5'-tetrakis(4-formylphenyl)bimesityl (TFPB) and 3,3',5,5'-tetrakis(4-aminophenyl)bimesityl (TAPB) with 4,4',4″,4‴-silanetetrayltetrabenzaldehyde (TFS), respectively, in 1,4-dioxane under the catalysis of acetic acid. Single-crystal 3D electron diffraction reveals the triply interpenetrated dia-b networks of TAM-TFPB-COF with atom resolution, while the isostructure of TAPB-TFS-COF was disclosed by synchrotron single-crystal X-ray diffraction and synchrotron powder X-ray diffraction with Le Bail refinements. The nitrogen sorption measurements at 77 K disclose the microporosity nature of both activated COFs with their exceptionally high Brunauer-Emmett-Teller surface areas of 3533 and 4107 m2 g-1, representing the thus far record high specific surface area among imine-bonded COFs. This enables the activated COFs to exhibit also the record high methane uptake capacities up to 28.9 wt % (570 cm3 g-1) at 25 °C and 200 bar among all COFs reported thus far. This work not only presents the structures of two single-crystalline COFs with exceptional microporosity but also provides an example of atom engineering to adjust permanent microporous structures for methane storage.

Researching The Dynamic Response Law of High and Steep Subgrade Slopes Using ABAQUS

The study aimed to investigate the dynamic response of vehicles traveling on expressways across high and steep subgrade slopes under vehicle loads. This was done by creating a finite element model of high and steep subgrade slopes through ABAQUS software and its secondary program development. Specific engineering examples were used to analyze the dynamic response states and laws of high and steep subgrade slopes under varying vehicle speeds and load conditions. The study's results indicate that: (1) When the vehicle is travelling at 100 km/h, its vibration frequency is close to the natural frequency of the subgrade in the section, leading to strong dynamic responses. (2) As the number of axles increases, the pressure on the contact surface between the wheel and the roadbed rises, and dynamic responses become stronger. The four-axle vehicle shows the most significant dynamic response. (3) The maximum vertical stress fluctuates around an intermediate value over time. (4) The highest maximum vertical displacement value is observed in the penultimate analysis step, with a noticeable peak value. At this point, the vehicle load has the most prominent impact on the roadbed slope.

TBM Advanced Geological Prediction via Ellipsoidal Positioning Velocity Analysis

Traditional seismic wave-based tunnel advanced geological forecasting techniques are primarily designed for drill and blast method construction tunnels. However, given the fast excavation speed and limited prediction space in tunnel boring machine (TBM) construction tunnels, traditional methods have significant technical limitations. This study analyzes the characteristics of different types of TBM construction tunnels and, considering the practical construction conditions, identifies an effective observation system and data acquisition method. To address the challenges in advanced forecasting for TBM construction tunnels, a method of ellipsoid positioning velocity analysis, which takes into account the constraints of three-component data directions, is proposed. Based on the characteristics of the advanced forecasting observation system, this method compares the maximum values on the spatial isochronous ellipsoidal surface to determine the average velocity of the geological layer rays, thereby enabling accurate inversion of the spatial distribution ahead. Utilizing numerical simulation, a model for the advanced detection of typical unfavorable geological formations is established by obtaining the wave field response characteristics of seismic waves in three-dimensional space, and the velocity structure of the model is retrieved through this velocity analysis method. In the engineering example, the fracture property, water content, and weathering degree of the surrounding rock are predicted accurately.

Extraction of logical networks during decompiling transistor-level CMOS circuit descriptions

Objectives. The problem of restoring the functional description of digital VLSI devices presented at the transistor level is considered. The objective of the work is to develop means for extraction of blocks representing logical networks from two-level descriptions of CMOS circuits at the transistor level, which were obtained as a result of recognition (extraction) of subcircuits that implement logic elements.Methods. Graph based methods and software tools are proposed for extracting a connected blocks representing a logical network from two-level descriptions of a transistor circuits in SPICE format. In the graph interpretation, the task is reduced to constructing a labeled directed graph of a logical network based on a labeled undirected bipartite graph specifying a two-level description of the transistor circuit.Results. The proposed method makes it possible to identify lexicographically ranked logical networks, from which a transition is made to logical equations that specify the functions implemented at the outputs of the resulting networks. Software tools have been developed that provide the generation of a hierarchical description in SPICE format that implements the original circuit at the transistor level, as well as descriptions of found logical networks in the SF language of hierarchical structural and functional descriptions of discrete devices and in high-level languages (VHDL and Verilog).Conclusion. The developed methods are implemented in C++, included in the program for decompiling transistor CMOS circuits and tested within it on practical examples of transistor-level circuits. The paper provides examples of reverse engineering of some practical transistor circuits.

Analysis of the influencing factors of the cost of village prefabricated housing in rural revitalization: a southern Shaanxi case study

PurposeUnder the rural revitalization, the effect of China's implementation of rural prefabricated housing is not obvious. Cost has become the biggest obstacle to its development. Therefore, it is necessary to study the factors influencing the cost of prefabricated buildings in villages and clarify the focus of cost control.Design/methodology/approachThis paper focuses on the whole process of prefabricated housing construction in villages in China and uses grounded theory to identify and screen out 27 related factors that affect the construction cost of prefabricated buildings. A system dynamics model is used to dynamically analyze the influencing factors. The engineering examples in rural areas of southern Shaanxi are simulated. Finally, five key factors that influence cost are obtained. Based on this, cost control countermeasures are proposed for rural prefabricated housing in southern Shaanxi.FindingsThe results show that: the key factors affecting the cost of prefabricated buildings in villages include the selection of production methods, the degree of design standardization, the quality of construction personnel, the level of construction technology and the circulation cycle of molds. The cost of prefabricated housing in villages can be controlled through five aspects: mass production of components, design exchange and feasibility analysis, improvement of employee professionalism, strict selection of construction schemes and technologies and improvement of mold turnover rate.Research limitations/implicationsThe system dynamics model applied in this paper is based on the idealized state. The system boundary is narrow and has a certain subjectivity. It needs further detailed research to make it closer to the engineering practice. In addition, this paper applies the rural engineering example in southern Shaanxi to carry out a single case study, and the universality of the research results needs to be further tested. There are many village construction projects and building types, so the research results can be further enriched through large sample research.Practical implicationsRural construction is an important step in the implementation of rural revitalization. Exploring the factors that affect the key costs of prefabricated buildings in villages and towns in view of the particularity of rural areas will help provide a reference for their cost control and help the rural development of prefabricated houses.Social implicationsThe research results of this paper can provide a reference for the development of prefabricated buildings in other rural revitalization areas.Originality/valueDifferent from the traditional research on urban prefabricated buildings, this paper focuses on rural areas and explores the core factors affecting the cost of prefabricated buildings from the micro level. This study establishes a system dynamics model suitable for the cost control of prefabricated housing at the village level and provides methods for its cost control. Based on the identified key factors affecting costs, cost control measures were proposed for prefabricated housing tailored to the unique characteristics of villages.

Efficient analysis and evaluation method for overall lifting of large-span spatial grid structures

Large-span spatial grid structures are commonly employed on the roofs of various public buildings and are typically installed via an overall lifting method. However, the process of overall lifting cannot ensure complete synchronization, which may result in damage to the structure or the lifting equipment. It is imperative to ascertain the limit state of the structure under asynchronous conditions and to develop an accurate model for rapidly assessing the critical response during the lifting process. This paper introduces an efficient approach for evaluating and analyzing the overall lifting process of large-span spatial grid structures. The iterative limit boundary (ILB) method is proposed in this paper, which facilitates the rapid determination of the limit boundary and the limit state for a structure-equipment system with asynchronous lifting. Furthermore, the genetic algorithm is integrated into the ILB method to enhance its efficiency when dealing with numerous lifting points. An agent model capable of outputting key structural responses is developed for the rapid evaluation of those responses throughout the lifting process. Both a numerical example and an engineering example are provided to validate the proposed method. The computational results indicate that the method proposed achieves accurate results with a computational cost merely 3 % of that of the Monte Carlo simulation. Additionally, the agent model established within the limit boundary, operates at a speed 200 times faster than finite element analysis, thereby enabling precise and real-time critical response assessments.

On improving the efficiency of providing budgetary places in Russian universities (on the example of engineering and technical areas of study)

Relevance. In the current economic and political conditions in Russia, there is an acute shortage of welltrained engineers, and not only in the IT field. In order to overcome this problem, the state annually raises the admission target numbers (CAP) for technical educational programs. This increases the amount of public funding for universities (both per capita and for general development); it is measured in hundreds of billions of rubles, which requires studying the efficiency of resource use. The purpose of the article is, based on the results of a comprehensive empirical analysis, to identify the key problems that reduce the effectiveness of state financing of state-funded places in engineering and technical areas, and to substantiate proposals for minimizing them. Methodology and methods. The methodological basis is a systematic approach, within which general scientific methods (deduction, induction, generalization, comparative analysis, etc.) were used, as well as special economic and statistical research methods. Results and scientific novelty. It was revealed that the use of the current methodology for the distribution of CSC by the Ministry of Education and Science of Russia leads to the fact that a significant part of budget places in engineering and technical areas of study are occupied by students with low (18.2% excluding IT areas) and extremely low (at least 14.5 %) by the level of incoming knowledge. In a number of areas, the proportion of such students reaches 87%, despite the fact that ¾ of the applicants have an extremely weak physical and mathematical base (within the framework of the school course). The actual scale of the problems turned out to be greater due to the non-linear scale of the Unified State Examination, “tuned” by the Ministry of Education of Russia to overestimate the score for “C” students. Taken together, this naturally leads to the training of engineers of the appropriate level at the state expense. A set of measures is substantiated to change the methodology for distributing budgetary places, aimed at improving the efficiency of the use of public funding. Practical significance. The results of the study can be used by the Ministry of Education and Science of Russia, as well as by the administrations of technical universities to improve the quality of applicants for engineering education programs.

Optimal operation strategy predictive control for an integrated radiant cooling with fresh air system based on machine learning

Radiant cooling systems are widely valued for their great comfort and energy-saving potential. However, they still face the risk of condensation in the early stages of operation, especially in case of random occupancy and intermittent operation. This study aims to avoid unacceptable discomfort durations in randomly occupied rooms that installed integrated radiant cooling and fresh air system while consuming as little energy as possible. This paper firstly compares the effects of adopting only the setback or standby cooling strategy in a randomly occupied conference room by simulation. The simulation results demonstrate the necessity for predicting optimal operation strategy. Subsequently, optimal operation strategy predictive models were built using three machine learning algorithms on three datasets. The evaluation results of the models indicate the feasibility of using data from neighbouring cities to improve the generalisation ability of the target city model. Finally, the best one of models was used to predict optimal operation strategy and achieved good results: discomfort durations of 97.56% of the conferences were within the acceptable range. Additionally, compared to only adopting the standby cooling strategy, the radiant cooling system operating time was reduced by 8.88%, and the total energy consumption was reduced by 28.85 kWh. In this study, a model to predict optimal operation strategy is proposed. By simulating and analysing from both comfort and energy perspectives, the optimal operation strategy predictive control method effectively limits the discomfort duration and reduces energy consumption and radiant system runtime. This study provides an example of practical engineering and machine learning applications for radiant cooling systems.

An improved prairie dog optimization algorithm integrating multiple strategies and its application

Aiming at the problems in prairie dog optimization (PDO), such as uneven population distribution at initialization, slow convergence, imbalance between global exploration and local exploitation, and the tendency to get trapped in the local optimum, this study proposes an Improved prairie dog optimisation algorithm integrating multiple strategies (IMSPDO). Firstly, the population is initialized using spatial pyramid matching (SPM) chaotic mapping combined with improved random opposition-based learning (IROL) to solve the problems of uneven distribution and poor diversity of the population. Secondly, the prey escapes energy formula mentioned in the harris hawks optimization (HHO) is introduced to achieve the smooth transition between the exploration phase and the exploitation phase, balancing the algorithm’s global exploration capability and local exploitation capability. Additionally, the idea of the particle swarm optimization (PSO) is applied to enhance the global optimization capability of the algorithm. Finally, the ideas of simulated annealing (SA), polynomial mutation and Cauchy mutation are also introduced to improve the ability that individuals to jump out of the local optimum. The performance of the improved algorithm is verified on a set of 21 classical benchmark functions and 8 CEC2020 test functions. The proposed IMSPDO is also evaluated against original PDO, and six other commonly used algorithms. The result of the Wilcoxon rank-sum test shows that there is a significant difference between the selected algorithms and IMSPDO. Furthermore, 3 engineering examples are used to further test the superiority of IMSPDO in dealing with real-world problems.

Membership categorisation, sociological description and role prompt engineering with ChatGPT

Large Language Models (LLMs) and generative Artificial Intelligence (A.I.) have become the latest disruptive digital technologies to breach the dividing lines between scientific endeavour and public consciousness. LLMs such as ChatGPT are platformed through commercial providers such as OpenAI, which provide a conduit through which interaction is realised, via a series of exchanges in the form of written natural language text called ‘prompt engineering’. In this paper, we use Membership Categorisation Analysis to interrogate a collection of prompt engineering examples gathered from the endogenous ranking of prompting guides hosted on emerging generative AI community and practitioner-relevant social media. We show how both formal and vernacular ideas surrounding ‘natural’ sociological concepts are mobilised in order to configure LLMs for useful generative output. In addition, we identify some of the interactional limitations and affordances of using role prompt engineering for generating interactional stances with generative AI chatbots and (potentially) other formats. We conclude by reflecting the consequences of these everyday social-technical routines and the rise of ‘ethno-programming’ for generative AI that is realised through natural language and everyday sociological competencies.

Stochastic optimization of levitation control system for maglev vehicles subjected to random guideway irregularity

The dynamic performance of maglev vehicle-bridge coupled systems subjected to random guideway irregularity, especially the levitation stability of the running vehicle subsystem, is a critical issue affecting the safe operation and the comfort riding of maglev railways. This study is devoted to stochastic optimization of the levitation control system considering the coupled vibration of maglev vehicle and bridge under the random guideway irregularity. To alleviate the computational burden in the stochastic optimization process, the explicit expressions of the critical responses of the maglev vehicle-bridge coupled systems and the sensitivities of critical responses with respect to the feedback gains of the proportional-integral-derivative (PID) levitation controller are respectively constructed in terms of the guideway irregularity by virtue of the dimension-reduced formulation capability of the explicit time-domain method (ETDM), enabling stochastic dynamic analysis and stochastic sensitivity analysis to be executed at high efficiency. The stochastic optimization problem is formulated as the minimization of the standard deviation of the air gap variation with the constraint on the standard deviation of the current variation, and the optimal values of the PID gains are searched by the gradient-based method of moving asymptotes (MMA), in which the statistical moments and moment sensitivities of the critical responses required in the optimization process are obtained by ETDM. A numerical example with a 2-degree-of-freedom maglev vehicle moving on a multi-span simply-supported bridge is used to demonstrate the efficacy of ETDM for the stochastic analysis of maglev vehicle-bridge coupled systems under guideway irregularity. An engineering example involving a 3-car maglev train traversing a multi-span simply-supported bridge is further presented to show the effectiveness of the ETDM-based approach for the stochastic optimization of large-scale engineering problems. The levitation stability of the maglev train under the action of random guideway irregularity is considerably improved with the use of optimal feedback gains.

Generation and evaluation of unimaginable three-dimensional structural joints using generative adversarial networks

Compared to the increasingly programmable and automated structural analysis methods, the current joint design suffers from long design time, poor quality and a low degree of automation. Developing an efficient and intelligent method to generate structurally novel and high-performing joints has become an urgent task. This paper combines artificial intelligence, topology optimization and reverse engineering techniques to develop a method named HD-JointGEN, and elaborates the idea and implementation steps of this method through an actual engineering example of a single-layer reticulated shell structure. The joints generated in batch by HD-JointGEN are analyzed and evaluated, revealing that the selected representative joint can reduce self-weight by 29.55%, maximum displacement by 0.99%, and maximum equivalent stress by 1.28% compared to the topology optimization joint. HD-JointGEN not only automatically generates a variety of innovative and previously unimaginable joints but also offers design solutions with superior force performances. It leads the research on intelligent generative design of the connection in complex three-dimensional structures and has broad application prospects.

Optimization of vanillin biosynthesis in Escherichia coli K12 MG1655 through metabolic engineering

Vanillin is an important flavouring agent applied in food, spices, pharmaceutical industries and other fields. Microbial biosynthesis of vanillin is considered a sustainable and economically feasible alternative to traditional chemical synthesis. In this study, Escherichia coli K12 MG1655 was used for the de novo synthesis of VAN by screening highly active carboxylic acid reductases and catechol O-methyltransferases, optimising the protocatechuic acid pathway, and regulating competitive metabolic pathways. Additionally, major alcohol by-products were identified and decreased by deleting three endogenous aldo–keto reductases and three alcohol dehydrogenases. Finally, a highest VAN titer was achieved to 481.2 mg/L in a 5 L fermenter from glucose. This work provides a valuable example of pathway engineering and screens several enzyme variants for the first time in E. coli.

Carbon Cloth Electrodes for Vanadium Redox Flow Batteries

Renewable energy sources such as wind and solar are playing an increasingly important role as the decarbonization of the energy industry moves forward. However, wind and solar-based energy production fluctuates daily and seasonally. For a stable electrical grid, a large-scale energy storage system is required. One promising technology for this is the Vanadium Redox Flow Battery (VRFB), developed in the late 1980s by Maria Skyllas-Kazacos (1), which has been continuously researched and improved over the last decades.VRFBs are already commercially available but must overcome significant lifetime and efficiency challenges. Polarization and pumping losses account for a large part of the efficiency losses due to the high flow-through resistance of the electrolyte in the electrode (2). Therefore, the electrode material must be optimized to achieve high catalytic activity and excellent flow properties to enhance the battery's performance.In a previous study, we identified key factors such as the electrode's structure, wettability, and electrochemical performance. Therefore, we developed a suitable set of characterization techniques (3). This multimodal characterization approach has been applied to evaluate woven and knitted carbon cloths. We designed electrode materials with specific fiber structures and weaving and knitting patterns. The raw material for the fibers is cellulose from hemp. To successfully commercialize VRVBs, the manufacturing process of the material must be low-cost and easy to fabricate on an industrial scale. Here, the electrode materials are fabricated by weaving or knitting, a well-established technique in the textile industry for centuries, and are ideally suited for the large-scale industrial production of electrodes. After weaving or knitting, the cloths are subsequently carbonized.To understand the influence of the fabrication pattern, the flow direction, and the 3D electrode structure on the flow behavior, we used Electrochemical Impedance Spectroscopy (EIS) coupled with the Distribution of Relaxation Times (DRT) analysis (4,5). This method allows simultaneously investigating the electrochemical performance, wettability, and structure in an application-oriented, in-house-designed setup. To develop a deeper understanding of the microscopic structure of the fiber and its influence on the key factors, Scanning Electron Microscopy (SEM) and nano-X-ray computed tomography (nano-CT) were utilized. These results were combined with Dynamic Vapor Sorption measurements (DVS), which are used to investigate the wettability of the material, and Cyclic Voltammetry (CV), which accesses the electrochemical performance. Additionally, we studied the effect of thermal activation on these parameters with EIS and DRT analysis, SEM, nano-CT, DVS, CV, and X-ray photoelectron spectroscopy.The materials presented in this paper exhibited excellent electrode performance, demonstrating good wettability, high catalytic activity, and large electrochemically active surface area. These are promising examples of electrode engineering as a pathway to optimal designs of VRFBs.1. M. Skyllas‐Kazacos, M. Rychcik, R. G. Robins, A. G. Fane and M. A. Green, J. Electrochem. Soc., 133(5), 1057–1058 (1986).2. N. Bevilacqua, L. Eifert, R. Banerjee, K. Köble, T. Faragó, M. Zuber, A. Bazylak and R. Zeis, Journal of Power Sources, 439, 227071 (2019).3. K. Köble, M. Jaugstetter, M. Schilling, M. Braig, T. Diemant, K. Tschulik and R. Zeis, Journal of Power Sources, 569, 233010 (2023).4. M. Schilling, M. Braig, K. Köble and R. Zeis, Electrochimica Acta, 430, 141058 (2022).5. M. Schilling and R. Zeis, submitted. Figure 1

An overview of potential excavation compensation method for tunnelling in deep rock engineering

The complicated geological environment of deep rocks poses new challenges to tunnel and mining engineering. Some thorny disasters such as large deformation of soft rock and rockburst are becoming more and more prominent. However, the classic tunnelling methods represented by the mine tunnelling method and the new Austrian tunnelling method are generally unsatisfactory in addressing these issues due to the limited self-stability of surrounding rock mass. Therefore, the excavation compensation method (ECM) with the core of active stress compensation has been proposed and applied in practical engineering construction to solve the above problems. After extensive engineering practice, the theoretical foundation, key technologies, and construction system of ECM have been established and improved. This article provides a comprehensive overview of this novel tunnelling method. In addition, its controlling effects on surrounding rock are demonstrated by two typical engineering examples. It could provide some new ideas and references for the development of future tunnelling technology.

Improving ambient noise cross-correlations using a polarization-based azimuth filter

Seismic interferometry is widely used to image and monitor earth structures at different scales by extracting an empirical Green’s function (EGF) from the cross-correlation of ambient noise under the assumption of diffuse wavefields or energy equipartitioning. The EGFs, however, may be incorrectly estimated and lead to a misunderstanding of subsurface structures, because the distribution of ambient noise sources is neither isotropic nor stationary. To extract reliable EGFs from the non-ideal ambient noise data, we propose a polarization-based azimuth filter (PAF) to attenuate the ambient noise energy of non-stationary sources to improve the quality of cross-correlation functions. The PAF is a time-frequency domain azimuth filter for three-component seismic data, which uses time-frequency polarization analysis to measure azimuths of ambient noise signals, and then filters the three-component data to obtain signals from desired directions. Based on the stationary-phase theory, we use the PAF to extract the ambient noise signals from the stationary-phase zones (SPZs), which improves the quality of the cross-correlation function and gets rid of the requirement for long observations. A synthetic test and two short-time engineering examples demonstrate that uneven source distributions might cause serious spurious signals in EGFs and we show the improvement in EGFs using our method. In addition, the PAF may allow for the complete separation of the fundamental and higher mode of Rayleigh waves, which uncovers more information implicit in the ambient noise data.

Theta-regularized Kriging: Modeling and algorithms

To obtain more accurate model parameters and improve prediction accuracy, we proposed a regularized Kriging model that penalizes the hyperparameter theta in the Gaussian stochastic process, termed the Theta-regularized Kriging. We derived the optimization problem for this model from a maximum likelihood perspective. Additionally, we presented specific implementation details for the iterative process, including the regularized optimization algorithm and the geometric search cross-validation tuning algorithm. Three distinct penalty methods, Lasso, Ridge, and Elastic-net regularization, were meticulously considered. Meanwhile, the proposed Theta-regularized Kriging models were tested on nine common numerical functions and two practical engineering examples. The results demonstrate that, compared with other penalized Kriging models, the proposed model performs better in terms of accuracy and stability.