Imaging Enhancement in Angle-Domain Common-Image-Gathers Using the Connected-Component Labeling Method

Abstract

The quality of seismic migration images strongly depends on accurate velocity models. However, migration velocity models often contain errors causing artifacts which degrade seismic resolution and fidelity. Here, we propose a method to enhance seismic migration images using processes in angle-domain common-image-gathers (ADCIGs), without requiring modification to the velocity model. The ADCIGs impacted from velocity errors show non-flat gathers and scattered noise. To enhance the imaging quality in ADCIGs, these problems need to be treated prior to stacking. Ideally, layers in ADCIGs should be flat without smearing amplitudes through all angles. Using this principle, we iteratively process one reflection at a time by isolating it into a local window and then flattening it. Also, we apply internal processing steps to enhance the signal. The algorithm for segregating a reflection, referred to as the connected-component labeling (CCL) method, is the primary method for extracting any feature that has a continuous form within the ADCIGs. Moreover, this method is insensitive to scattered noise so it is a suitable approach for classifying reflections or noise. To test the efficiency of this algorithm, we create ADCIGs containing relatively poor-quality images using an inaccurate migration velocity model. The primary objective of this test is to show how well the CCL method can select various forms of reflections in the presence of various levels of noise. The results show that the CCL method is capable of individually decomposing a reflection, which can facilitate processing in the internal workflow. Finally, we evaluate the quality of processed migration images by comparing our processed Poynting-vector reverse time migration (PVRTM) images with reverse time migration applied Laplacian filter and partial stacked PVRTM. These comparable migration images are benchmarked against a baseline synthetic reflector model. The essential improvements of our method are addressed by the migration sections, namely the removal of diffraction artifacts at both small and large scales and the improvement of phase coherency. The final stacked migration image produced with our methods shows clearer geological features that are capable of delivering a more reliable seismic interpretation.