Results 131 to 140 of about 1,357,139 (273)

Seismic random noise separation and suppression based on improved variational mode decomposition via grey wolf optimization.

open access: yesPLoS ONE
Seismic noise separation and suppression is an important topic in seismic signal processing to improve the quality of seismic data recorded at monitoring stations.
Zhenjing Yao   +4 more
doaj   +1 more source

Advances in crosshole seismic reflection processing [PDF]

open access: yes, 1993
In recent years there have been significant advances in the acquisition and processing of crosshole seismic reflection data, and the method has been shown to be a high resolution imaging technique.
Rowbotham, Peter S
core  

Interpreting Non‐Linear Compaction Mechanics of Expansive Soils Using Reliability Calibrated Framework

open access: yesApplied Research, Volume 5, Issue 5, October 2026.
This study introduces a reliability‐calibrated machine learning framework to predict the maximum dry density and optimum moisture content of heterogeneous soils. By integrating 15 algorithms with conformal prediction, the models deliver accurate compaction estimates alongside rigorous, distribution‐free 90% confidence intervals.
Farjad Aziz   +3 more
wiley   +1 more source

A Critical Analysis of Optimization Algorithms for Cultural Heritage Conservation and Management

open access: yesEngineering Reports, Volume 8, Issue 10, October 2026.
Methodological framework and decision‐support roadmap for optimization algorithms in cultural heritage conservation and management. ABSTRACT Heritage buildings symbolize cultural identity, architectural character, and historical epitome of communities.
Eslam Mohammed Abdelkader   +6 more
wiley   +1 more source

Suppressing random noise in seismic signals using wavelet thresholding based on improved chaotic fruit fly optimization

open access: yesEURASIP Journal on Advances in Signal Processing
Suppressing random noise in seismic signals is an important issue in research on processing seismic data. Such data are difficult to interpret because seismic signals usually contain a large amount of random noise.
Feng Yang, Jun Liu, Qingming Hou, Lu Wu
doaj   +1 more source

Cross‐Entropy of Power Spectral Density Function: A Modal Identification Framework

open access: yesEarthquake Engineering &Structural Dynamics, Volume 55, Issue 12, Page 3012-3028, 10 October 2026.
ABSTRACT The power spectral density (PSD) function of measured structural response contains a significant amount of information, including the modal parameters (natural frequencies, damping ratios). Output‐only system identification or modal identification technique can be used for extracting such modal parameters from the measured response or its ...
Su‐Hong Kim   +3 more
wiley   +1 more source

Multi-scale dual-path attention network for seismic background noise attenuation

open access: yesScientific Reports
The background noise in seismic records severely interferes with the extraction of effective reflection events, particularly in complex exploration environments such as deserts.
Li Han, Dongyan Wang, Feng Li
doaj   +1 more source

Wind turbines noise measurements inside homes

open access: yes, 2019
Wind energy is a primary source for achieving the objectives of the Oil Free Society. However, noise emission is often a significant problem encountered during wind turbines operation.
Ciaburro G.   +3 more
core  

Borehole seismic methods for opencast coal exploration [PDF]

open access: yes, 1990
Surface seismic techniques lack the resolution to image the top 100m or so of the earth's surface necessary for opencast coal exploration. The work reported in this thesis is the development of borehole seismic methods making use of the closely spaced ...
Kragh, J. Edward
core  

Learning Rocking Dynamics From Sparse Shake‐Table Data With Interpretable Physics‐Informed Neural Networks

open access: yesEarthquake Engineering &Structural Dynamics, Volume 55, Issue 12, Page 3115-3136, 10 October 2026.
ABSTRACT We present a hybrid interpretable Physics‐Informed Neural Network Long‐Short Term Memory (Hybrid PINN LSTM) framework for predicting the seismic response of rocking blocks. Existing analytical models rely on uncertain idealizations, while purely data‐driven and machine‐learning approaches lack physical consistency and interpretability.
Shirley Shen   +1 more
wiley   +1 more source

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