A neural network-based method for analyzing diffracted wave velocity
[Background]In seismic imaging,accurate velocity models are crucial for characterizing subsurface struc-tures in a fine-scale manner.Notably,in coal mining,small-scale geological structures like faults and collapse columns are closely associated with mining accidents.These structures typically occur as diffracted waves in seismograms.[Ob-jective and Methods]To effectively image these small-scale geological bodies,fine-scale velocity modeling using dif-fracted wave information is particularly important.Hence,this study proposed a neural network-based method for ana-lyzing diffracted-wave velocity.First,diffracted-wave velocity spectra were generated using the gathers of diffracted waves that were separated in the common virtual source domain.Second,a gamma-ray spectrum of diffracted-wave ve-locity ratios was generated using the quasi-linear characteristics of diffracted waves in the migrated dip-angle domain.Finally,with the conventional reflected-wave velocity spectrum and the two diffracted-wave velocity spectra as inputs,intelligent diffracted-wave velocity modeling was performed based on a convolutional attention neural network.[Res-ults and Conclusions]The tests using numerical simulation data verified that the diffracted-wave velocity analysis net-work can enhance the accuracy of fine-scale velocity modeling for geological bodies like stratigraphic pinch-outs and karst collapse columns.This network allows for the effective focusing of diffracted waves in the imaging profiles,thus achieving the fine-scale characterization of small-to medium-scale structures.The application of the proposed method to actual data from a coal mine further demonstrates the method's superiority over conventional methods in terms of velo-city modeling efficiency and imaging accuracy.Therefore,the proposed method is more applicable to the imaging of small-scale structures under complex geologic conditions.
万方数据
维普
