Seismic Volumetric Local Slope Estimation Using Multiscale Gradient Structure Tensor

• Published in: IEEE geoscience and remote sensing letters Vol. 20; p. 1
• Main Authors: He, Yu, Yang, Ping, Qian, Feng, Geng, Weifeng, Zheng, Bingwei, Ren, Xiaoqiao, Hu, Guangmin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Earthquakes; Eigenvalues and eigenfunctions; Estimation; Gaussian pyramid (GP); gradient structure tensor (GST); Kalman filters; Kernel; Mathematical analysis; multiscale GST (MGST); Noise measurement; Plane waves; Random noise; Reflection; Seismic activity; Seismic surveys; Slope; Slopes; Smoothing methods; Tensors; Tracking; Volumetric local slope
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2023.3298325

The volumetric local slope, which indicates the orientation of seismic events, plays a prominent role in the subsequent geological interpretation typically including horizon tracking, seismic facies analysis, and fault interpretation. Although numerous existing estimation methods are available, they still suffer from the challenge of reaching a balance between resolution preservation and resisting the heavy random noise. As an alternative, this letter proposes a seismic volumetric local slope estimation method named the multiscale gradient structure tensor (MGST), combining GST with the 3-D multiscale Gaussian pyramid (GP). In this regard, to preserve the details of the original resolution and fully exploit the unique information at different scales, the GP is reconstructed in 3-D space by decomposing the data into multiple scales. After that, we attempt to employ the GST to derive the local slopes in two directions at each scale, along with a corresponding quality metric. Finally, within the Kalman filter framework, the local slope of each scale is sequentially integrated using the quality metric as the weighting mechanism, resulting in an accurate and robust estimation. Experiments on both synthetic and real field datasets indicate that the proposed MGST method outperforms the traditional GST and plane-wave destruction (PWD) methods.