On the basis of the relationship between the carbonate content and the stratal velocity and density, an exercise has been attempted using an artificial neural network on high-resolution seismic data for inversion of carbonate content with limited well measurements as a control. The method was applied to the slope areaof the northern South ChinaSea near ODP Sites1146 and 1148, and the resultsare sat-isfactory. Before inversion calculation, a stepwise regression method was applied toobtain six properties related most closely to the carbonate content variations among the various properties on the seismic pro-files across or near the wells. These include the average frequency, the integrated absolute amplitude, the dominant frequency, the reflection time, the derivative instantaneous amplitude, and the instantaneous frequency. The results, with carbonate content errors of mostly ±5% relative to those measured from sediment samples, show a relatively accurate picture of carbonate distribution along the slope profile.This method pioneers a new quantitative model to acquire carbonate content variations directly from high-resolution seismic data. It will provide a new approach toward obtaining substitutive high-resolution sediment data for earth systemstudies related to basin evolution, especially in discussing the couplingbe-tween regional sedimentation and climate change.