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Multitrace first-break picking using an integrated seismic and machine learning method

Duan Xudong and Zhang Jie
Multitrace first-break picking using an integrated seismic and machine learning method
Geophysics (July 2020) 85 (4): WA269-WA277

Abstract

Picking the first breaks from seismic data is often a challenging problem and still requires significant human effort. We have developed an iterative process that applies a traditional seismic automated picking method to obtain preliminary first breaks and then uses a machine learning (ML) method to identify, remove, and fix poor picks based on a multitrace analysis. The ML method involves constructing a convolutional neural network architecture to help identify poor picks across multiple traces and eliminate them. We then further refill the picks on empty traces with the help of the trained model. To allow training samples applicable to various regions and different data sets, we apply moveout correction with preliminary picks and address the picks in the flattened input. We collect 11,239,800 labeled seismic traces. During the training process, the model's classification accuracy on the training and validation data sets reaches 98.2% and 97.3%, respectively. We also evaluate the precision and recall rate, both of which exceed 94%. For prediction, the results of 2D and 3D data sets that differ from the training data sets are used to demonstrate the feasibility of our method.


ISSN: 0016-8033
EISSN: 1942-2156
Coden: GPYSA7
Serial Title: Geophysics
Serial Volume: 85
Serial Issue: 4
Title: Multitrace first-break picking using an integrated seismic and machine learning method
Affiliation: University of Science and Technology of China, Geophysical Research Institute, Hefei, China
Pages: WA269-WA277
Published: 20200701
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 51
Accession Number: 2020-059316
Categories: Applied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus.
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2020, American Geosciences Institute. Reference includes data from GeoScienceWorld, Alexandria, VA, United States. Reference includes data supplied by Society of Exploration Geophysicists, Tulsa, OK, United States
Update Code: 2020
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