00000nam 2200000zi 4500 9.910026 CaOODSP 20251031142448 m o d f cr mn||||||||| 220407t20222022onca ob f000 0 eng d CaOODSP eng rda CaOODSP n-cn--- M183-2/8877E-PDF Patwa, B., author. Predictive models for first arrivals on seismic reflection data, Manitoba, New Brunswick, and Ontario / B. Patwa, P.L. St-Charles, G. Bellefleur, and B. Rousseau. [Ottawa] : Geological Survey of Canada, 2022. ©2022 1 online resource (41 pages) : illustrations (chiefly colour). text txt rdacontent computer c rdamedia online resource cr rdacarrier Open file ; 8877 Includes bibliographical references (pages 40-41). "First arrivals are the primary waves picked and analyzed by seismologists to infer properties of the subsurface. Here we try to solve a problem in a small subsection of the seismic processing workflow: first break picking of seismic reflection data. We formulate this problem as an image segmentation task. Data is preprocessed, cleaned from outliers and extrapolated to make the training of deep learning models feasible. We use Fully Convolutional Networks (specifically UNets) to train initial models and explore their performance with losses, layer depths, and the number of classes. We propose to use residual connections to improve each UNet block and residual paths to solve the semantic gap between UNet encoder and decoder which improves the performance of the model. Adding spatial information as an extra channel helped increase the RMSE performance of the first break predictions. Other techniques like data augmentation, multitask loss, and normalization methods, were further explored to evaluate model improvement"--Abstract, page 1. Seismic reflection method Data processing. Deep learning (Machine learning) Méthode sismique-réflexion Informatique. Apprentissage profonde. Geological Survey of Canada, issuing body. Open file (Geological Survey of Canada) 8877. (CaOODSP)9.506878 PDF 9.08 MB https://publications.gc.ca/collections/collection_2022/rncan-nrcan/m183-2/M183-2-8877-eng.pdf HTML N/A https://doi.org/10.4095/329758