Seismic data de-noising method based on an attention mechanism U-Net
Due to various factors such as instruments,equipment,and environment during field acquisition,there often exist various types of noise in seismic data,including surface waves,ghost waves,random noise,etc.,affecting the reliability and accuracy of seismic data processing and interpretation.Recently,methods based on artificial intelligence have become a research hotspot in seismic data denoising,as they have high com-puting efficiency and good numerical effects.U-Net is a classic convolutional neural network structure com-monly used in image segmentation tasks.Attention mechanism(AM)is a technique that allows models to fo-cus more on specific regions or features during the learning process.This paper constructs a U-Net with atten-tion function by adding an AM module to the U-Net network and applies it to seismic data denoising.To ad-dress the boundary effects generated during the denoising process,the expansion filling method is used to seg-ment data.This method has strong universality and can be used for other network models.By comparing the de-noising effect of AU-Net and U-Net,it has been proved that AU-Net network has better denoising effect than that of the U-Net,which can better preserve weak signals.Meanwhile,AU-Net denoising method is more adap-table by transfer learning.
seismic explorationdeep learningU-Netseismic data denoisingneural network
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