Abstract
Accurate simulation of wave motion for the modeling and inversion of seismic wave propagation is a classical high-performance computing (HPC) application using the finite difference, the finite element methods and spectral element methods to solve the wave equations numerically. The paper presents a new method to improve the performance of the seismic wave simulation and inversion by integrating the deep learning software platform and deep learning models with the HPC application. The paper has three contributions: 1) Instead of using traditional HPC software, the authors implement the numerical solutions for the wave equation employing recently developed tensor processing capabilities widely used in the deep learning software platform of PyTorch. By using PyTorch, the classical HPC application is reformulated as a deep learning recurrent neural network (RNN) framework; 2) The authors customize the automatic differentiation of PyTorch to integrate the adjoint state method for an efficient gradient calculation; 3) The authors build a deep learning model to reduce the physical model dimensions to improve the accuracy and performance of seismic inversion. The authors use the automatic differentiation functionality and a variety of optimizers provided by PyTorch to enhance the performance of the classical HPC application. Additionally, methods developed in the paper can be extended into other physics-based scientific computing applications such as computational fluid dynamics, medical imaging, nondestructive testing, as well as the propagation of electromagnetic waves in the earth.
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Acknowledgment
This research work is supported by the US National Science Foundation (NSF) awards ##1649788, #1832034 and by the Office of the Assistant Secretary of Defense for Research and Engineering (OASD(R&E)) under agreement number FA8750-15-2-0119. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the US NSF, or the Office of the Assistant Secretary of Defense for Research and Engineering (OASD(R&E)) or the U.S. Government. The authors would also like to thank the XSEDE for providing the computing resources.
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Huang, L., Clee, E., Ranasinghe, N. (2020). Improving Seismic Wave Simulation and Inversion Using Deep Learning. In: Nichols, J., Verastegui, B., Maccabe, A.‘., Hernandez, O., Parete-Koon, S., Ahearn, T. (eds) Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI. SMC 2020. Communications in Computer and Information Science, vol 1315. Springer, Cham. https://doi.org/10.1007/978-3-030-63393-6_1
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