Yuan, S, Liu, J, Wang, S et al. (2 more authors) (2018) Seismic waveform classification and first-break picking using convolution neural networks. IEEE Geoscience and Remote Sensing Letters, 15 (2). pp. 272-276. ISSN 1545-598X
Abstract
Regardless of successful applications of the convolutional neural networks (CNNs) in the different fields, its application to seismic waveform classification and first break (FB) picking has not been explored yet. This letter investigates the application of CNNs for classifying time-space waveforms from seismic shot gathers and picking FBs of both direct wave and refracted wave. We use representative sub-image samples with two types of labeled waveform classification to supervise CNNs training. The goal is to obtain the optimal weights and biases in CNNs, which are solved by minimizing the error between predicted and target label classification. The trained CNNs can be utilized to automatically extract a set of time-space attributes or features from any sub-image in shot gathers. These attributes are subsequently inputted to the trained fully-connected layer of CNNs to output two values between 0 and 1. Based on the two-element outputs, a discriminant score function is defined to provide a single indication for classifying input waveforms. The FB is then located from the calculated score maps by sequentially using a threshold, the first local minimum rule of every trace and a median filter. Finally, we adopt synthetic and real shot data examples to demonstrate the effectiveness of CNNs-based waveform classification and FB picking. The results illustrate that CNNs is an efficient and automatic data-driven classifier and picker.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. This is an author produced version of a paper published in IEEE Geoscience and Remote Sensing Letters. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Uploaded in accordance with the publisher’s self-archiving policy. |
Keywords: | Waveform classification; first break (FB) picking; seismic data; convolutional neural networks (CNNs) |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 08 Feb 2018 17:28 |
Last Modified: | 20 Mar 2018 19:34 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/LGRS.2017.2785834 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:127030 |