Method to predict coal seam thickness and small fault using RS and NN

The thesis put forward a new method of Rough Sets (RS) and Neural Network (NN) technique to detect small faults and coal seam thickness by analyzing 3D seismic data. This method uses RS to reduce seismic data noise, and after reduction, low noise seismic data can be hold. After inputting those reduced data to NN, a predicting model which can detect small faults and predict coal seam thickness can be achieved after NN's training. After this step, this model was used to detect small fault by 3D seismic data. We find that this method has high precision.