Despite technological and computational advances in geophysical imaging, near-surface geophysics continues to pose significant challenges in modeling and imaging the subsurface. Geoscientists from around the world attended the first and second editions of the SEG/DGS Near-surface Modeling and Imaging Workshop in 2014 and 2016 to address these challenges. A range of near-surface disciplines were represented from academia and industry, covering aspects of engineering and hydrocarbon exploration. The previous workshops explored emerging and underdeveloped techniques, including deep learning (machine learning), nonseismic methods, full-waveform inversion (FWI), and joint inversion. The necessity to further understand guided waves, anisotropy, velocity inversion, and the creation of an inclusive near-surface model was identified. The previous editions led to a greater understanding of the importance of knowledge sharing among various disciplines in modeling and imaging of the near surface.
Near-Surface Modeling Using First Arrival Tomography: A Case Study on High Variation Topography and High Surface Energy Attenuation Area