LAUSR

Multifrequency attribute analysis is an effective tool to delineate sand bodies with different thicknesses. Conventionally, red–green–blue (RGB) blending technique is often used to fuse three frequency components for depicting reservoir thicknesses, that is, the low-, medium-, and high-frequency components. However, the seismic signal is a typically broadband signal, while RGB blending can only fuse three frequency components. Moreover, how to select these three specific frequency components is also a difficult and unsolved task. In this study, we suggest a multifrequency attribute analysis workflow for delineating sand bodies. First, we introduce the S-transform (ST) to extract multifrequency components of the analyzed seismic data. Then, the t-distributed stochastic neighbor embedding (t-SNE)-based workflow for fusing multifrequency components is proposed, which is used to capture the local structural features of the analyzed high-dimensional data and reveal the global structures simultaneously. Afterward, we adopt a synthetic trace and a 3-D field data volume to test the effectiveness of the proposed workflow. Compared with the contrastive methods, our workflow performs better in delineating the spatial distribution and thicknesses of sand bodies, which benefits further well deployment.