most citedAttenuating Random Noise in Seismic Data by a Deep Learning Approach

3 citations · 6 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG20193 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…

physics.geo-ph20193 cited

Deep Learning Realm for Geophysics: Seismic Acquisition, Processing, Interpretation, and Inversion

Ping Lu

Applying deep-learning models to geophysical applications has attracted special attentions during the past a couple of years. There are several papers published in this domain invo…

eess.IV2019

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

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

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…