Seismic Attribute Extraction and Application Based on the Gabor Wavelet Transform

Abstract

The most common method for interpreting strata with seismic data is to relate the peaks and troughs of adjacent traces based on the seismic waveform characteristics. This can be captured by machine learning and deep learning methods to stratigraphic segmentation as many people investigated in industry. However, the spatial variability and instability of the peaks and troughs of seismic signals increases the difficulty of applying this technology. In addition, the nonlinear relationship and complicated subsurface geological setting make it more difficult. Thus, we propose a new seismic attribute extraction method based on the Gabor wavelet transform and linear dimensionality reduction. This method does not use the peaks or troughs of the seismic signal and instead focuses on the energy change in the seismic signal at the strata interface. It uses the characteristics of the energy change to identify strata. A sliding window Fourier transforms (STFTs) pretreatment is applied to convert the seismic signal to a spectrum energy form. On this basis, the local texture information of the spectrum can be processed by the Gabor wavelet transform to obtain the Gabor attribute of the seismic signal. The seismic Gabor attributes extracted using the above method contain time, frequency, and energy features, solving the problem of single seismic amplitude data features. Finally, the validity of the extracted seismic attributes is verified by a field data. In this process, the seismic amplitude, spectrum data and Gabor attributes are used as sample data for the support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost) models and deep residual shrinkage network (DRSN) for comparison. The results show that when the seismic Gabor attributes are used, the accuracy and root mean square error (RMSE) of the stratigraphic identification with the SVM, RF and XGBoost models are significantly better than those of the seismic amplitude and spectrum data only.

Keywords

• Spectrum analysis view
• Gabor wavelet transform
• LDA
• SVM
• RF
• XGBoost