Abstract: The main oil and gas-bearing strata discovered in  Kaiping sag are primarily located in the Paleogene Enping Formation and Wenchang Formation. The depositional environment is mainly  braided river delta plain subfacies, and the microfacies of underwater distributary channel is dominant. Due to the lateral oscillation of multiple river channels, the strata have strong heterogeneity, making stratigraphic correlation extremely difficult. This paper utilizes the powerful data fitting capabilities of machine learning algorithm support vector machine to determine the optimal fusion frequencies and weights for low, medium, and high-frequency data segments. This process yields inversion results with enhanced vertical resolution and detailed lateral seismic characteristics, thereby improving the accuracy of interwell stratigraphic pinchout positions and the description of stratigraphic connectivity. The high-resolution v_P/v_S inversion results demonstrate that this method is capable of identifying reservoirs with a thickness of 6 m. It offers superior vertical and horizontal resolution, which is conducive to stratigraphic correlation within the study area.

Key words: Kaiping sag, Paleogene, machine learning high-resolution fusion inversion, stratigraphic comparison