Regularization with differential evolution‐based common‐offset common‐reflection surface: A case study for field onshore seismic data

Abstract We introduce a novel approach for seismic pre‐stack data regularization using the common‐offset common‐reflection‐surface method with a global attribute search strategy, employing a bio‐inspired differential evolution optimization algorithm. We compare the global common‐offset common‐reflection‐surface approach with the conventional sequential attribute search, focusing on their performance in pre‐stack data regularization. Tests on synthetic and onshore (field) seismic datasets demonstrate that the global search approach significantly improves performance, enhancing signal‐to‐noise ratio and coherently filling missing traces. We show that the global common‐offset common‐reflection‐surface method effectively addresses spatial irregularities, reconstructing reflections without artefacts, filling data gaps and highlighting geological details even in complex areas. In contrast, the sequential common‐offset common‐reflection‐surface method, while capable of reconstructing missing traces, shows lower interpolation quality and fails to adequately highlight complex geological features such as high‐dip reflections.