Patent ID: 11922679
Assignee: XI'AN JIAOTONG UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. An automatic seismic facies identification method based on combination of Self-Attention mechanism and U-shape network architecture, the method comprising:
(a) obtaining and preprocessing post-stack seismic data to construct a sample training and validation dataset;
(b) building an encoder by using an overlapped patch merging module with a down-sampling function and a self-attention transformer module capable of performing global modeling;
(c) building a decoder by using a patch expanding module with linear up-sampling function, the self-attention transformer module, and a skip connection module capable of performing multi-scale feature fusion;
(d) building a seismic facies identification model by using the encoder, the decoder, and a Hypercolumn module, wherein the seismic facies identification model comprises a Hypercolumns-U-Segformer (HUSeg); and
(e) building a hybrid loss function; iteratively training the seismic facies identification model with a training and validation set in the sample training and validation dataset; and inputting test data into a trained seismic facies identification model to obtain seismic facies corresponding to the test data;
wherein the step of “building an encoder by using an overlapped patch merging module and a self-attention transformer module” comprises:
for a seismic section image x∈H×W with an input height of H and an input width of W, building an encoder composite function ƒe=ƒe4∘ƒe3∘ƒe2∘ƒe1 to satisfy, f
     e
    
    (
    x
    )
   
   ∈
   
    ℝ
    
     
      H
      
       1
       ⁢
       6
      
     
     ×
     
      W
      
       1
       ⁢
       6
      
     
     ×
     
      C
      4
     
    
   
  
  ;, and
for a feature map, x
    
     (
     
      i
      -
      1
     
     )
    
   
   ∈
   
    ℝ
    
     
      H
      
       2
       
        i
        -
        1
       
      
     
     ×
     
      W
      
       2
       
        i
        -
        1
       
      
     
     ×
     
      C
      
       i
       -
       1
      
     
    
   
  
  ,, constructing a first subfunction, f
     e
     i
    
    (
    
     x
     
      (
      
       i
       -
       1
      
      )
     
    
    )
   
   ∈
   
    ℝ
    
     
      H
      
       2
       i
      
     
     ×
     
      W
      
       2
       i
      
     
     ×
     
      C
      i
     
    
   
  
  ;
 

wherein Ci is the number of channels of a feature map output by an i-th subfunction of the encoder; the first subfunction ƒei consists of the overlapped patch merging module and the self-attention transformer module, wherein the number of the overlapped patch merging module is one, and the number of the self-attention transformer module is two; the number of the first subfunction ƒei is four; four first subfunctions ƒei constitute four consecutive stages of the encoder; the overlapped patch merging module is implemented by a convolutional layer with a stride less than a kernel size; the self-attention transformer module comprises a self-attention submodule and a feedforward neural network (FNN) submodule; and calculation formulas of the self-attention submodule and the FNN submodule are respectively expressed as:

sAtt(x)=MHSA(LN(x))+x  (1); and

FFN(x)=L2(cv(L1(LN(x))))+x  (2);

wherein LN is a layer normalization function; MHSA is a multi-head self-attention calculation function; L1 and L2 are two fully-connected layer functions; and cv is a convolutional layer function.