most citedA Novel Correlation-optimized Deep Learning Method for Wind Speed Forecast

9 citations · 19 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2023★ 9 cited

A Novel Correlation-optimized Deep Learning Method for Wind Speed Forecast

Yang Yang, Jin Lang, Jian Wu +2

The increasing installation rate of wind power poses great challenges to the global power system. In order to ensure the reliable operation of the power system, it is necessary to…

cs.LG2019★ 3 cited

Attenuating Random Noise in Seismic Data by a Deep Learning Approach

Xing Zhao, Ping Lu, Yanyan Zhang +2

In the geophysical field, seismic noise attenuation has been considered as a critical and long-standing problem, especially for the pre-stack data processing. Here, we propose a mo…

eess.IV2019★ 5 cited

Enhancement of seismic imaging: An innovative deep learning approach

Yanyan Zhang, Ping Lu, Hua Yu +1

Enhancing the frequency bandwidth of the seismic data is always the pursuance at the geophysical community. High resolution of seismic data provides the key resource to extract det…

physics.geo-ph2019★ 1 cited

Reservoir Characterizations by Deep-Learning Model: Detection of True Sand Thickness

Ping Lu, Yanyan Zhang, Hua Yu +1

It is an extremely challenging task to precisely identify the reservoir characteristics directly from seismic data due to its inherit nature. Here, we successfully design a deep-le…

eess.IV2019★ 1 cited

Enhanced Seismic Imaging with Predictive Neural Networks for Geophysics

Ping Lu, Yanyan Zhang, Jianxiong Chen +2

We propose a predictive neural network architecture that can be utilized to update reference velocity models as inputs to the full waveform inversion. Deep learning models are expl…