A Label-Free High-Precision Residual Moveout Picking Method for Depth-Domain Tomography Based on Deep Learning
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2025.3612370
Abstract: Residual moveout (RMO) provides critical information for depth-domain tomography. The current industry-standard method for fitting RMO involves scanning high-order polynomial equations. However, this analytical approach does not accurately capture abrupt variation of the RMO, leading… read more here.
Keywords: deep learning; method; picking method; depth domain ... See more keywords
A depth-variant seismic wavelet extraction method for basis pursuit inversion with impedance trend constraint
Sign Up to like & getrecommendations! Published in 2024 at "GEOPHYSICS"
DOI: 10.1190/geo2023-0255.1
Abstract: The seismic images produced by pre-stack depth migration show more accurate subsurface structures than time images, resulting in a growing need for depth-domain inversion. However, due to the strong non-stationarity exhibited by depth-domain seismic data,… read more here.
Keywords: impedance; depth; depth domain; wavelet ... See more keywords
Early Stuck Pipe Sign Detection with Depth-Domain 3D Convolutional Neural Network Using Actual Drilling Data
Sign Up to like & getrecommendations! Published in 2020 at "SPE Journal"
DOI: 10.2118/204462-pa
Abstract: A real-time stuck pipe prediction using the deep-learning approach is studied in this paper. Early signs of stuck pipe, hereinafter called stuck, are assumed to show common patterns in the monitored data set, and designing… read more here.
Keywords: neural network; depth domain; stuck pipe; convolutional neural ... See more keywords