arXiv:2511.06731v2 Announce Type: replace-cross
Abstract: Deep learning has transformed seismic phase picking, but a systematic failure mode persists: for some S-wave arrivals that appear unambiguous to human analysts, the model produces only a distor…
arXiv:2604.00307v1 Announce Type: cross
Abstract: Recent advances in generative networks have enabled new approaches to subsurface velocity model synthesis, offering a compelling alternative to traditional methods such as Full Waveform Inversion. Howe…
arXiv:2603.28328v1 Announce Type: new
Abstract: Accurate prediction of hydrogen sorption in fine-grained geological materials is essential for evaluating underground hydrogen storage capacity, assessing caprock integrity, and characterizing hydrogen m…
arXiv:2603.25037v2 Announce Type: replace
Abstract: Satellite Earth observation has accumulated massive spatiotemporal archives essential for monitoring environmental change, yet these remain organized as discrete raster files, making them costly to s…
arXiv:2502.00672v3 Announce Type: replace-cross
Abstract: The increasing availability of large-scale observational data and the rapid development of artificial intelligence (AI) provide unprecedented opportunities to enhance our understanding of the g…
arXiv:2603.25037v1 Announce Type: new
Abstract: Satellite Earth observation has accumulated massive spatiotemporal archives essential for monitoring environmental change, yet these remain organized as discrete raster files, making them costly to store…
arXiv:2603.23984v1 Announce Type: new
Abstract: In recent years, a number of neural-network (NN) methods have exhibited good performance in seismic data processing, such as denoising, interpolation, and frequency-band extension. However, these methods…