Variable window reconstruction algorithm and adaptive SegNet network in the application of stratigraphic sequence classification
The emergence of deep learning provides a direction for automatic stratigraphic division,which is characterized by a large amount of batch interpretation data,a short interpretation cycle and high interpretation accuracy.Deep learning requires a large amount of training data,but due to the high dimensionality of logging data,limited sample size,and similar features between adjacent samples,there are issues with sample independence and reliability.In the face of complex underground structures and unconformity surfaces,it is difficult for the general deep learning method to accurately divide the stratum boundary,especially when dealing with poor sample quality and a small number of samples.Considering that the logging data belongs to small sample data with limited quantity and poor quality,which is not conducive to model training and construction,we built a variable window waveform reconstruction algorithm to increase the volume of training data.This algorithm generates reconstructed waveforms based on the characteristics of the original waveforms and simulates the waveform characteristics under different speed models.Manually stratify and reconstruct some of the original logging data,and input the reconstructed data as training samples into a SegNet network with adaptive variable convolution kernel size.Use SegNet with adaptive variable convolution kernel size to solve complex underground structure problems.Through experiments,SegNet with adaptive variable convolution kernel size can fit faults and unconformity surfaces in seismic data on multiple scales to achieve a better segmentation effect.After verification,we found that the model has good recognition efficiency and robustness.
万方数据
维普
