Seismic data are widely used in oil, gas, and other kinds of mineral exploration and development. However, due to low artificial interpretation accuracy and small sample sizes, seismic data may not meet the needs of convolutional neural network train...
We proposed an economic, cost-constrained optimal design of a water distribution system (WDS) that maximizes seismic reliability while satisfying pressure constraints. The model quantifies the seismic reliability of a WDS through a series of procedur...
Applying deep neural networks (DNNs) to broadband seismic wave impedance inversion is challenging, especially in generalizing from synthetic to field data, which limits the exploitation of their nonlinear mapping capabilities. While many research stu...
The deteriorating state of North America’s bridge infrastructure is a pressing issue, necessitating innovative risk management strategies. This study aims to enhance the seismic resilience of bridge infrastructure using a Bayesian belief networ...
Transport networks play a critical role for living communities, as they facilitate the exchange of people and goods and foster economic growth. Improving their resilience against seismic hazards, among which liquefaction is by far one of the most sig...
In light of the challenging conditions of exploration environments coupled with escalating exploration expenses, seismic data acquisition frequently entails the capturing of signals entangled amidst diverse noise interferences and instances of data l...
Porosity prediction from seismic data is of significance in reservoir property assessment, reservoir architecture delineation, and reservoir model building. However, it is still challenging to use traditional model-driven methodology to characterize...
Evaluating the seismic damage of urban road infrastructure systems is of vital importance in reducing the earthquake hazard risk of cities. Urban road infrastructure systems are commonly represented as a spatial network that covers the whole city, an...
The Montefeltro seismic network (FDSN Network code: 1S) was deployed in the Apennines area of northern Marche and southern Emilia-Romagna regions (central Italy). A temporary network was set up in December 2018 and continues to operate, with an array...
Random noise is unavoidable in seismic data acquisition due to anthropogenic impacts or environmental influences. Therefore, random noise suppression is a fundamental procedure in seismic signal processing. Herein, a deep denoising convolutional auto...
Capacity curves obtained from nonlinear static analyses are widely used to perform seismic assessments of structures as an alternative to dynamic analysis. This paper presents a novel ‘en masse’ method to assess the seismic vulnerability...
This study proposes a novel model to quantitatively evaluate functionality loss in railway network systems during earthquakes and assesses its applicability to a hypothetical railway network system. The model combines seismic fragility functions and...
In this study, an artificial neural network (ANN)-based surrogate model is proposed to evaluate the system-level seismic risk of bridge transportation networks efficiently. To estimate the performance of a network, total system travel time (TSTT) was...
Currently, machine learning techniques are widely used in structural seismic response studies. The developed network models for various types of seismic response provide new ways to analyse seismic hazards. However, it is not easy to balance the appl...
Reinforced concrete structures often require retrofitting due to damage caused by natural disasters such as earthquakes, floods, or hurricanes; deterioration from aging; or exposure to harsh environmental conditions. Retrofitting strategies may invol...
Seismic signal processing often relies on general convolutional neural network (CNN)-based models, which typically focus on features in the time domain while neglecting frequency characteristics. Moreover, down-sampling operations in these models ten...
Seismic response prediction is a challenging problem and is significant in every stage during a structure’s life cycle. Deep neural network has proven to be an efficient tool in the response prediction of structures. However, a conventional neu...
The nation’s transportation systems are complex and are some of the highest valued and largest public assets in the United States. As a result of repeated natural hazards and their significant impact on transportation functionality and the soci...
17 August 2023
Resilient stormwater infrastructure is one of the fundamental components of resilient and sustainable cities. For this, the resilience assessment of stormwater infrastructure against earthquake hazards is crucial for municipal authorities. The object...
The current seismic prediction methods of the shale brittleness index are all based on the pre-stack seismic inversion of elastic parameters, and the elastic parameters are transformed by Rickman and other simple linear mathematical relationship form...
Passive seismic experiments have been proposed as a cost-effective and non-invasive alternative to controlled-source seismology, allowing body–wave reflections based on seismic interferometry principles to be retrieved. However, from the huge v...
This study introduces a multiple-input convolutional neural network (MI-CNN) model for the seismic damage assessment of regional buildings. First, ground motion sequences together with building attribute data are adopted as inputs of the proposed MI-...
The geoPebble system is a network of wirelessly interconnected seismic and GPS sensor nodes with geophysical sensing capabilities for the study of ice sheets in Antarctica and Greenland, as well as mountain glaciers. We describe our design methodolog...
A structural analysis model to represent the dynamic behavior of building structure is required to develop a semi-active seismic response control system. Although the finite element method (FEM) is the most widely used method for seismic response ana...
Most regional seismic damage assessment (RSDA) methods are based on the rigid-base assumption to ensure evaluating efficiency, while these practices introduce factual errors due to neglecting the soil–structure interaction (SSI). Predicting the...
Earthquakes threaten people, homes, and infrastructure. Early warning systems provide prior warning of oncoming significant shaking to decrease seismic risk by providing location, magnitude, and depth information of the event. Their usefulness depend...
This study examines reservoir-triggered seismicity (RTS) in Poland and Vietnam. The current state of individual RTS seismic networks necessitates detecting earthquakes from only a few stations. The number of P waves is often inadequate for phase asso...
Seismic networks in urban areas today represent key infrastructure to better address the tasks of earthquake preparation and mitigation in the pre-event phase, and are an important knowledge tool supporting disaster risk management during seismic cri...
Deep-learning-based seismic data interpretation has received extensive attention and focus in recent years. Research has shown that training data play a key role in the process of intelligent seismic interpretation. At present, the main methods used...
Structural seismic resilience is influenced by both the structural performance loss (loss) and the repair path (path). Some studies ensure the reality of path but lack accuracy of loss. Others ensure the accuracy of loss but lack the reality of path....
Seismic activity of small, medium or high intensity has a destructive effect on existing water supply and distribution networks. In the scholar literature, these are included in Class I—Vital Performance Systems, whose operation must be uninter...
The angle of seismic excitation is a significant factor in the seismic response of RC buildings. The procedure required for the calculation of the angle for which the potential seismic damage is maximized (critical angle) contains multiple nonlinear...
Over recent years, frequent earthquakes have caused huge losses in human life and property. Rapid and automatic earthquake detection plays an important role in earthquake warning systems and earthquake operation mechanism research. Temporal convoluti...
As natural gas reserves are generally decreasing there is a need to successfully characterize potential research objects using geophysical data. Presented is a study of amplitude vs. offset, attribute and artificial neural network analysis on a resea...
This investigation concentrates on refining the accuracy of earthquake parameters as reported by various Saudi seismic networks, addressing the significant challenges arising from data discrepancies in earthquake location, depth, and magnitude estima...
Seismic data acquisition in oil and gas exploration employs a large-scale network of geophone sensors deployed in thousands across a survey field. A central control unit acquires and processes measured data from geophones to come up with an image of...
Over the last few years, the analysis of seismic noise recorded by two dimensional arrays has been confirmed to be capable of deriving the subsoil shear-wave velocity structure down to several hundred meters depth. In fact, using just a few minutes o...
A high-resolution seismic image is the key factor for helping geophysicists and geologists to recognize the geological structures below the subsurface. More and more complex geology has challenged traditional techniques and resulted in a need for mor...
The growing need for safe, cheap and sustainable earthquake-resistant buildings means that efficient methods for optimal seismic design must be found. The complexity and nonlinearity of the problem can be addressed using advanced automated techniques...
Realistic synthetic data can be useful for data augmentation when training deep learning models to improve seismological detection and classification performance. In recent years, various deep learning techniques have been successfully applied in mod...
Seismic mitigation of transportation systems has become a worldwide challenge, because identifying an optimal retrofit strategy entails significant computational efforts, especially for large-scale networks with numerous candidate strategies and time...
Urban seismic networks are considered very useful tools for the management of seismic emergencies. In this work, a study of the first urban seismic network in central Italy is presented. The urban seismic network, built using MEMS sensors, was implem...
The process of geothermal energy development may cause induced seismic activities, posing a potential threat to the sustainable utilization and safety of geothermal energy. To effectively evaluate the danger of induced seismic activities, this paper...
Seismic velocity inversion is one of the most critical issues in the field of seismic exploration and has long been the focus of numerous experts and scholars. In recent years, the advancement of machine learning technologies has infused new vitality...
Enhancing the seismic resilience of urban water distribution networks (WDNs) requires the improvement of both earthquake resistance and rapid recovery capabilities within the system. This paper proposes a multi-objective method to enhance the seismic...
This study is focused on the assessment of the seismic risk for elements of the electric network (thermoelectric powerplants and substations) in Romania. Firstly, the main elements of the electric network analyzed in this study are briefly presented....
The paper presents an approach for detecting anomalies in radon concentration in seismically active areas. It involves training multiple artificial neural networks (ANNs) to predict radon concentration during periods without seismic events. The train...
Neural networks have been applied to seismic inversion problems since the 1990s. More recently, many publications have reported the use of Deep Learning (DL) neural networks capable of performing seismic inversion with promising results. However, whe...
We present a new complex network-based study focused on intraplate earthquakes recorded in southern Norway during the period 1980–2020. One of the most recognized limitations of spatial complex network procedures and analyses concerns the defin...
Existing deep learning-based seismic signal denoising methods primarily operate in the time domain. Those methods are ineffective when noise overlaps with the seismic signal in the time domain. Time–frequency domain-based deep learning methods...
of 16