Abstract:
The solution of the 3-D wave equation holds significant importance in various fields, but traditional numerical methods employed for solving this equation are typically t...Show MoreMetadata
Abstract:
The solution of the 3-D wave equation holds significant importance in various fields, but traditional numerical methods employed for solving this equation are typically time-consuming. Recently, machine learning-based methods have gained popularity for solving wave equations owing to their capacity to predict physical phenomena. Nonetheless, current research is limited to solving 1-D and 2-D wave equations due to the complex nature of the solutions of the 3-D wave equation. Therefore, a recurrent convolutional neural network (RCNN) that leverages the similarity between RNN and the time-marching process, and the consistency of convolutional layers (CLs) with spatial differentiation, is proposed to solve 3-D wave equation. Taking advantage of the latest advances in machine learning framework, RCNN makes it unsophisticated to achieve efficient GPU computing. Numerical examples demonstrate that the RCNN achieves considerable accuracy and impressive computational efficiency in solving the 3-D wave equation.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 20)