Abstract As transport channels of oil and gas, fracture networks can greatly improve reservoir seepage, which is of great significance to the hydraulic fracturing and hydrocarbon deposit exploitation in petroleum science and engineering. In this paper, our target reservoirs are deep karsted carbonates at depths of >6000 m and highly heterogeneous, leading to complex seismic responses with weak energy and low resolution. Therefore, it is challenging to predict the spatial distribution of a carbonate fracture-cavern reservoir and to characterize its delicate structure. We present a characterization method for an excellent fracture description by integrating several attribute results on 3D seismic field data. First, we use a noise elimination method to remove the noise interference in seismic data without damaging the fault structure characteristics. Next, we propose a novel spatially windowed 2D Hilbert transform-based operator to perform volumetric edge detection on 3D seismic field data. Then, the volumetric edge results are co-rendered with other seismic geometric attributes to generate multi-attribute fusion results for a comprehensive prediction that can excellently delineate geologic anomalies at different scales in deep carbonates. The results indicate that integrating several scale attributes can obtain more rich geological discontinuity and reveal more subtle fractures than using single attribute. The multi-attribute fusion results can effectively delineate some small-medium-sized faults, and they provide practical support for the exploration and production of Tahe carbonate fracture-cavern reservoirs.