Abstract
Adaptive multiple subtraction is an important step for the success of multiple removal after multiple prediction. Generally, the traditional method uses a 2D matching filter to combine the predicted multiples to match with the original data directly. Due to the complicated mismatches between the predicted multiples and true multiples, multiples may be removed aggressively with damaging primaries and vice versa for the traditional method. Especially in complex media, how to balance multiple removal and primary preservation is very important. In this paper we propose to use multi feature-gathers of the predicted multiples for adaptive multiple subtraction. The feature of the predicted multiples is extracted by the convolutional neural network with the predicted multiples as the input and the original data as the output. The multi feature-gathers of the predicted multiples contain more prediction information than the predicted multiples themselves. Therefore, the multi feature-gathers combined by a 3D matching filter can better match with the true multiples than the predicted multiples themselves combined by a 2D matching filter. Synthetic and field data examples demonstrate that the proposed method can better balance multiple removal and primary preservation than the traditional method.