First-Break Picking of Large-Offset Seismic Data Based on CNNs with Weighted Data

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 2; p. 356
• Main Authors: Yin, Yuchen, Han, Liguo, Zhang, Pan, Lu, Zhanwu, Shang, Xujia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-01-2023
• Subjects: Algorithms; Arrivals; Artificial neural networks; convolutional neural network; Datasets; Deep learning; Dynamic correction; first-break picking; large-offset seismic data; Methods; Neural networks; Offsets; Tomography; tomography images; Training; Travel time; Windows (intervals)
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15020356

Deep reflection seismic data are usually accompanied by large-offset data, and the accurate and rapid identification of the first arrivals of seismic records plays an important role in eliminating the effects of topography and other factors that increase with the increasing offsets. In this paper, we propose a method based on convolutional neural networks (CNNs) that can accurately identify the first arrivals of large-offset seismic data. A time window for linear dynamic correction was established to convert the raw seismic data into rectangular images so as to reduce the amount of invalid sample data and improve the training efficiency. In order to enhance the prediction effect of the far-offset first arrivals, we propose the strategy of adjusting the weight of the far-offset data to increase the weight of the far-offset data in the training dataset and, thus, to improve the first arrival accuracy. The manually picked first arrivals are used as labels and the input to the CNNs for training, and the full-offset first arrivals are the output. The travel time tomography velocity is modeled and compared based on the first arrivals obtained through manual picking, industrial software automatic picking, and CNN prediction. The results show that the application of CNNs to large-offset seismic datasets can help researchers to obtain the first arrivals at different offsets, while the inclusion of far-offset weights can effectively improve the modeling depth of the tomography inversion, and the accuracy of the results is high.