In the course of any seismic data acquisition, one inevitably encounters instances of empty seismic traces or insufficient spatial sampling, which results in bad sectors and can greatly affect seismic data quality. It is therefore often necessary to undertake seismic trace interpolation to solve this problem. In this paper, a machine learning based method is proposed and applied. This approach requires that the statistical relationship between the amplitude of each trace at each time point and the amplitude of the adjacent trace and time window be derived using a random forest regression prediction algorithm, then the empty trace can be populated according to the adjacent trace data. The method proposed in this paper has achieved good results in the derivation of empty trace values when applied to both model data and actual data, thus proving its validity and effectiveness.
prestack data processing seismic interpolation random forest machine learning