Results 151 to 160 of about 3,292,250 (298)
A new hybrid optimization approach using PSO, Nelder-Mead Simplex and Kmeans clustering algorithms for 1D Full Waveform Inversion. [PDF]
PLoS One, 2022 Aguiar Nascimento R +5 more
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.Flexible tactile sensors have considerable potential for broad application in healthcare monitoring, human–machine interfaces, and bioinspired robotics. This review explores recent progress in device design, performance optimization, and intelligent applications. It highlights how AI algorithms enhance environmental adaptability and perception accuracy
Siyuan Wang +3 more
wiley +1 more source
Seismic Multi-Parameter Full-Waveform Inversion Based on Rock Physical Constraints
Applied SciencesSeismic multi-parameter full-waveform inversion (FWI) integrating velocity and density parameters can fully use the kinematic and dynamic information of observed data to reconstruct underground models. However, seismic multi-parameter FWI is a highly ill-
Cen Cao, Deshan Feng, Jia Tang, Xun Wang
doaj +1 more source
Application of Optimal Basis Functions in Full Waveform Inversion
, 2004 In full waveform inversion, the lack of low frequency information in the inversion results has been a long standing problem. In this work, we show that by using mixed basis functions this problem can be resolved satisfactorily.
SUN, Gang, CHANG, Qianshun, SHENG, Ping
core +1 more source
Harnessing Phase Dynamics Across Diverse Frequencies with Multifrequency Oscillatory Neural Networks
Advanced Intelligent Discovery, EarlyView.Oscillatory Neural Networks (ONNs) are an emerging computing paradigm that encodes information in the phases of coupled oscillators. Traditionally, ONNs have been investigated using homogeneous frequency oscillators. However, physical hardware implementations are inherently subject to frequency mismatches, device variability, and nonuniformities.
Nil Dinç +2 more
wiley +1 more source
Low‐Frequency Reconstruction for Full Waveform Inversion by Unsupervised Learning
Earth and Space ScienceObtaining reliable low‐frequency seismic data is crucial for effectively reducing cycle‐skipping in full waveform inversion. However, acquiring high signal‐to‐noise ratio low‐frequency information from field data remains a challenge.
Ningcheng Cui, Tao Lei, Wei Zhang
doaj +1 more source
Frontiers in Earth ScienceTo obtain more accurate full waveform inversion results, we present a forward modeling method with minimal phase error, low numerical dispersion, and high computational efficiency.
Yanjie Zhou +4 more
doaj +1 more source
Parametric Analysis of Spiking Neurons in 16 nm Fin Field‐Effect Transistor Technology
Advanced Intelligent Discovery, EarlyView.Energy efficient computing has driven a shift toward brain‐inspired neuromorphic hardware. This study explores the design of three distinct silicon neuron topologies implemented in 16 nm fin field‐Effect transistor technology. While the Axon‐Hillock design achieves gigahertz throughput, its functional fragility persists. The Morris–Lecar model captures
Logan Larsh +3 more
wiley +1 more source
sFWI: physics-informed score-based generative modeling for robust full waveform inversion
Machine Learning: Science and TechnologyFull waveform inversion (FWI) is an important geophysical imaging technique with applications in hydrocarbon exploration, subsurface carbon storage, and earthquake hazard assessment. However, FWI’s efficacy is greatly affected by its ill-posed, nonlinear
Zicheng Gai, Yanfei Wang
doaj +1 more source
Full waveform inversion schemes for 3D density structures [PDF]
, 2014 Blom, Nienke, Fichtner, Andreas
core +1 more source