This work focuses on enhancing low frequency seismic data using a convolutional neural network trained on synthetic data.Traditional seismic data often lack both high and low frequencies,which are essential for detail...This work focuses on enhancing low frequency seismic data using a convolutional neural network trained on synthetic data.Traditional seismic data often lack both high and low frequencies,which are essential for detailed geological interpretation and various geophysical applications.Low frequency data is particularly valuable for reducing wavelet sidelobes and improving full waveform inversion(FWI).Conventional methods for bandwidth extension include seismic deconvolution and sparse inversion,which have limitations in recovering low frequencies.The study explores the potential of the U-net,which has been successful in other geophysical applications such as noise attenuation and seismic resolution enhancement.The novelty in our approach is that we do not rely on computationally expensive finite difference modelling to create training data.Instead,our synthetic training data is created from individual randomly perturbed events with variations in bandwidth,making it more adaptable to different data sets compared to previous deep learning methods.The method was tested on both synthetic and real seismic data,demonstrating effective low frequency reconstruction and sidelobe reduction.With a synthetic full waveform inversion to recover a velocity model and a seismic amplitude inversion to estimate acoustic impedance we demonstrate the validity and benefit of the proposed method.Overall,the study presents a robust approach to seismic bandwidth extension using deep learning,emphasizing the importance of diverse and well-designed but computationally inexpensive synthetic training data.展开更多
Power grid simulation calculation iswidely used in fields such as power grid operation,planning,safety defense,and accident inversion,which is one of the fundamental support technologies for power grid operation.At pr...Power grid simulation calculation iswidely used in fields such as power grid operation,planning,safety defense,and accident inversion,which is one of the fundamental support technologies for power grid operation.At present,mainstream software for power grid simulation calculation used by domestic power grid enterprises include PSD Power Tools,Power System Analysis Software Package(PSASP),Advanced Digital Power System Simulator(ADPSS)and so on,which can provide various simulation calculation functions such as power flow calculation,transient stability calculation,short circuit current calculation,etc.But in the process of using these software,various calculation data adjustments still rely entirely on manual experience.This article introduces artificial intelligence technology into power grid simulation calculation and develops an intelligent power system analysis platform which can be used in the simulation for large power grids,achieving the combination of artificial intelligence technology and power grid simulation technology,which can provide technical support for the transformation of power grid simulation and analysis work mode.展开更多
文摘This work focuses on enhancing low frequency seismic data using a convolutional neural network trained on synthetic data.Traditional seismic data often lack both high and low frequencies,which are essential for detailed geological interpretation and various geophysical applications.Low frequency data is particularly valuable for reducing wavelet sidelobes and improving full waveform inversion(FWI).Conventional methods for bandwidth extension include seismic deconvolution and sparse inversion,which have limitations in recovering low frequencies.The study explores the potential of the U-net,which has been successful in other geophysical applications such as noise attenuation and seismic resolution enhancement.The novelty in our approach is that we do not rely on computationally expensive finite difference modelling to create training data.Instead,our synthetic training data is created from individual randomly perturbed events with variations in bandwidth,making it more adaptable to different data sets compared to previous deep learning methods.The method was tested on both synthetic and real seismic data,demonstrating effective low frequency reconstruction and sidelobe reduction.With a synthetic full waveform inversion to recover a velocity model and a seismic amplitude inversion to estimate acoustic impedance we demonstrate the validity and benefit of the proposed method.Overall,the study presents a robust approach to seismic bandwidth extension using deep learning,emphasizing the importance of diverse and well-designed but computationally inexpensive synthetic training data.
基金supported by the State Grid Corporation of China‘s project:Research on artificial intelligence analysis technology of available transmission capacity(ATC)of key section under multiple power grid operation modes(5100-202255020A-1-1-ZN).
文摘Power grid simulation calculation iswidely used in fields such as power grid operation,planning,safety defense,and accident inversion,which is one of the fundamental support technologies for power grid operation.At present,mainstream software for power grid simulation calculation used by domestic power grid enterprises include PSD Power Tools,Power System Analysis Software Package(PSASP),Advanced Digital Power System Simulator(ADPSS)and so on,which can provide various simulation calculation functions such as power flow calculation,transient stability calculation,short circuit current calculation,etc.But in the process of using these software,various calculation data adjustments still rely entirely on manual experience.This article introduces artificial intelligence technology into power grid simulation calculation and develops an intelligent power system analysis platform which can be used in the simulation for large power grids,achieving the combination of artificial intelligence technology and power grid simulation technology,which can provide technical support for the transformation of power grid simulation and analysis work mode.
