High-resolution processing of seismic data using adaptive attention mechanism U-net
With the deepening of oil and gas exploration and development,thin and lithologic reservoirs have gradually become important exploration targets,which also leads to higher requirements for the resolution of seismic data.This paper presents a high-resolution seismic data processing method of U-net based on an adap-tive attention mechanism.This method first uses the U-net structure to learn the feature representation of seis-mic data,extracts the abstract features of seismic data through the encoder of the down-sampling process,and then reconstructs and refines the features through the decoder of the up-sampling process.The attention mecha-nism is introduced in the process of up-sampling,which is utilized to adjust the attention of the network to diffe-rent seismic features.Therefore,the network can capture more details and features of the seismic data more effectively.The experimental results of synthetic seismic records of the Marmousi model and real data show that the new network has less error and is more stable than the original U-net,as it can effectively improve the prediction accuracy and realize the high-resolution processing of seismic data.
seismic data processinghigh-resolutionU-netattention mechanismadaptive
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