Velocity modeling is an important part of prestack depth migration,which usually requires lateral ex-trapolation of the formation velocity of observation points under layer constraints.However,in the early stage of velocity modeling,there is a lack of framework information such as the seismic interpretation layer.There-fore,a method for establishing a velocity model with Siamese networks based on curvature spectrum lateral simi-larity and improved cyclic structure is proposed in the article.Siamese network is currently a commonly used deep learning based object recognition and tracking network,which can quickly compare the similarity of target images without the need for manual labeling.The curvature spectrum can be seen as a two-dimensional image that reflects the characteristics and velocity information of the formation.Velocity modeling as a lateral feature similarity analogy problem can automatically obtain the framework and velocity update information of the forma-tion by analogy with the curvature spectrum.Firstly,the prestack depth migrated gathers are converted into cur-vature spectra;Secondly,the curvature spectrum images are determined to be searched and its corresponding target tracking object,and the similarity coefficients between the current tracking target and the objective tracking target are calculated;Then,the reference curvature spectrum image and the current tracked object are updated based on the similarity coefficient;Finally,with all tracking objects traversed,a velocity model is established based on the layer velocity and depth of each tracked object.Theoretical models and actual data experimental re-sults show that this method can quickly generate velocity models that are congruent with geological structures and stratigraphic characteristics without interpretation data.
维普
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