Automatic Velocity Analysis Based on Unsupervised Physical Constraints Learning

The velocity analysis requires a significant degree of automation due to its time consuming and labor-intensive nature. Recently, clustering algorithms, an unsupervised learning method, have been used in velocity analysis to achieve automated velocity picking. Unlike supervised learning methods, this approach does not require a large amount of high-quality labeled data and high training costs. Meanwhile, existing clustering-based velocity analysis requires high-signal-to-noise ratio inputs and is particularly sensitive to multiple reflection noise, which severely limits the effectiveness of automatic velocity analysis (AVA) methods. To provide an adaptive AVA method for seismic data that contains multiple reflections, we introduce physical prior knowledge constraints within the framework of clustering algorithms. In particular, we transformed the peak picking problem in the velocity spectrum into a classification problem between primary and multiple reflections. Meanwhile, three physical attributes, velocity, amplitude, and primary similarity, are used as physical prior information for the clustering algorithm to help solving the classification problem. The synthesized and field data examples have proven the effectiveness of the proposed method.