Seismic Diffraction Attribute Fusion for Geological Discontinuities in Hot Dry Rock Resources

Abstract

For the safe development and utilization of hot dry rock resources, it is essential to understand the distribution characteristics of underground faults. However, the commonly used reflection attribute analysis method has an insufficient resolution, and the diffraction attribute analysis method is affected by multiple solutions. Moreover, both are highly dependent on the interpreters’ experience and take a long time. Therefore, based on the classical U-Net model, a diffraction attribute fusion model (DAF-U-Net) with 27-layer convolution is proposed. The DAF-U-Net network takes four-channel diffracted attributes as an input and underground fracture distribution as an output. The new network adds a spatial attention and channel attention mechanism to improve the positioning and extraction ability of the U-Net model for the attribute characteristics of diffractions. After optimizing the diffraction attributes of hot dry rock slices in the Gonghe basin, Qinghai, the slices are input into the network to train the model. According to the prediction and identification results of the network model, the DAF-U-Net network has a high reliability in predicting fracture distributions. It has a specific reference role in the subsequent exploitation of hot dry rock.