Abstract
To automate time-consuming and experience-based analysis of elastic wave velocities in rock samples, we propose a method of determining the arrival time of experimental waveform data using machine learning. Our model is one-dimensional semantic segmentation based on U-net utilizing ResNet and attention mechanisms. The results demonstrate high accuracy of travel time estimation (0.0125 μs) of two types of waves (P- and S-waves). Our approach can estimate the travel time in any experimental device. This method reduces the interpretation time and human biases in analyzing laboratory data of elastic waves. Therefore, this approach contributes to the efficient evaluation of subsurface structure from seismic properties.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
