Search Results - "Cai, Shengjuan"

Quantification of gas hydrate saturation and morphology based on a generalized effective medium model by Pan, Haojie, Li, Hongbing, Chen, Jingyi, Riedel, Michael, Holland, Melanie, Zhang, Yan, Cai, Shengjuan

“…Numerous models have been developed for prediction of gas hydrate saturation based on the microstructural relationship between gas hydrates and sediment…”
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A Hybrid Data‐Driven and Data Assimilation Method for Spatiotemporal Forecasting: PM2.5 Forecasting in China by Cai, Shengjuan, Fang, Fangxin, Tang, Xiao, Zhu, Jiang, Wang, Yanghua

“…Spatiotemporal forecasting involves generating temporal forecasts for system state variables across spatial regions. Data‐driven methods such as Convolutional…”
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A unified contact cementation theory for gas hydrate morphology detection and saturation estimation from elastic-wave velocities by Pan, Haojie, Li, Hongbing, Chen, Jingyi, Zhang, Yan, Cai, Shengjuan, Huang, Yucheng, Zheng, Ying, Zhao, Yi, Deng, Jian

“…Good knowledge of hydrate morphology and accurate quantification of hydrate saturation are significant for reservoir characterization, resource exploitation…”
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Evaluation of gas hydrate resources using hydrate morphology-dependent rock physics templates by Pan, Haojie, Li, Hongbing, Chen, Jingyi, Zhang, Yan, Liu, Xiaobo, Cai, Shengjuan, Cao, Chunjie

“…Gas hydrates often exhibit several microscopic morphologies within the host sediments, which subsequently affect their elastic responses. Hence, good knowledge…”
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Joint elastic-electrical effective medium modeling for improved hydrate quantification by Pan, Haojie, Li, Hongbing, Zhang, Jiajia, Cai, Shengjuan, Gui, Zhixian, Du, Miao

“…The occurrence of gas hydrates significantly alters the elastic and electrical responses of the host sediments. It is expected that joint inversion of the…”
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Advancing operational PM2.5 forecasting with dual deep neural networks (D-DNet) by Cai, Shengjuan, Fang, Fangxin, Peuch, Vincent-Henri, Alexe, Mihai, Navon, Ionel Michael, Wang, Yanghua

“…PM2.5 forecasting is crucial for public health, air quality management, and policy development. Traditional physics-based models are computationally demanding…”
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