Society Of Marine Science and Technology International Journal Of Coastal, Offshore And Environmental Engineering(ijcoe) 2980-8731 Articles in Press 2024 07 24 A Convolutional Neural Network-Based Seismic Full Waveform Inversion for Coastal Subsurface Exploration 200921 10.22034/ijcoe.2024.456865.1080 EN Amir Hossein Moadeli Department of Electrical Engineering, Shiraz university of technology, Shiraz, Iran 0009-0000-3792-1055 Habibollah Danyali Department of Electrical Engineering, Shiraz university of technology, Shiraz, Iran 0000-0002-3594-1674 Mohammad Sadegh Helfroush Department of Electrical Engineering, Shiraz university of technology, Shiraz, Iran 0000-0001-9095-4913 Hossein Rahnema Department of Civil and Environmental Engineering, Shiraz University of Technology, Shiraz, Iran 0000-0002-3117-8077 Journal Article 2024 05 13 In this study, a convolutional neural network has been proposed to estimate the speed model of the subsurface structure based on multi-channel surface wave (MASW) analysis, addressing the Full-waveform Inversion (FWI) problem. A structure of deep supervision U-net architecture with two-stage encoder-decoder has been used in the proposed network. The proposed network structure is evaluated by 2D data models of an open source synthetic seismic Dataset, OPEN FWI, with different layering structures using Mean Squared Error (MSE), Structural Similarity (SSIM) and Mean Absolute Error (MAE) criteria. Also, in order to evaluate the robustness of the proposed method, the effect of filtering and removing parts of the input data have been investigated. A comparison results with several FWI methods in the current literature have been provided. The experimental results show that the proposed method is able to provide more accuracy in estimating and reconstructing the velocity model which results in better subsurface layering estimation.