Xinming Wu

Deep Learning-based Seismic Reflectivity Estimation by Pre-training on Labeled Synthetic Data and Physics-guided Fine-tuning in Field Data

Summary Reflectivity estimation aims to enhance the resolution of seismic data, providing crucial support for the detailed inversion of reservoir parameters. We propose a method that combines supervised pre-training with synthetic data and physics-guided fine-tuning in field data...

Massive-scale unlabeled field and labeled synthetic seismic datasets of global shelf-edge clinothems

Summary Seismic stratigraphic interpretation of shelf-edge clinothems is essential for revealing tectonic evolution, paleoclimate change, depositional dynamic conditions, and hydrocarbon generation and accumulation during basin filling. However, traditional interpretation methods...