Multi-scale fault characterization using synchrosqueezing generalized S-transform
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摘要: 叠后断裂识别一般基于构造类属性, 但该类属性都存在断点刻画不清、断层连续性差等特点; 深度学习对于大、中尺度的断裂有着较好的表征能力, 但是针对小尺度的断裂刻画能力有限。基于此, 提出同步挤压广义S变换的振幅梯度凌乱性多尺度断裂刻画算法:首先, 在时频域内将地震数据分解为不同频带的单频数据体; 其次, 基于不同频带的地震数据体计算振幅梯度向量的凌乱性; 最后, 运用不同频带的地震数据体刻画不同尺度的断裂信息。基于模型及实际资料的研究结果表明, 同步挤压广义S变换振幅梯度凌乱性属性不仅对大、中尺度断裂有较好的刻画能力, 同时对于小尺度断裂也有着很好的表征。Abstract: Post-stack fault recognition is typically performed based on structural attributes, which, however, frequently exhibit unclear characterization of fault contacts and poor fault continuity. Deep learning can characterize middle- to large-scale faults accurately but has a limited capacity to characterize small-scale ones. This study developed a multi-scale fault characterization algorithm using amplitude gradient clutter based on synchrosqueezing generalized S-transform (SSGST). First, seismic data were decomposed into single-frequency data volumes across different frequency bands in the time-frequency domain. Then, the clutter of the amplitude gradient vectors was computed based on the seismic data volumes of different frequency bands. Finally, multi-scale faults were characterized using seismic data volumes of different frequency bands. The results from both model simulations and practical data demonstrate that the amplitude gradient clutter property derived using SSGST provides can effectively characterize small-scale faults besides large-and medium-scale faults.
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