Application of an Improved U-Net Neural Network on Fracture Segmentation from Outcrop Images

Zhibao Wang; Ziming Zhang; Lu Bai; Yuze Yang; Qiang Ma

doi:10.1109/igarss46834.2022.9883208

Application of an Improved U-Net Neural Network on Fracture Segmentation from Outcrop Images

Zhibao Wang
, Ziming Zhang
, Lu Bai
, Yuze Yang
, Qiang Ma

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Citations (Scopus)

38 Downloads (Pure)

Abstract

Outcrop records contain very rich geological historical information, and the study of fractures in outcrop areas is an important part of geological exploration work. The accurate fracture information can provide useful technical support for the development and exploration of subsurface oil and gas. The outcrop images usually include unclear boundaries, complex structure and inconspicuous features, which make fracture detection from outcrop images a difficult task. To tackle these challenges, an improved U-Net algorithm based on the ResNeXt module is proposed in this paper to segment the fractures from the outcrop images. Experiments are conducted on the outcrop images from Yijianfang area in the Tarim Basin in China, and the results show that the proposed algorithm has improved the accuracy and IoU in fracture segmentation from the outcrop images.

Original language	English
Title of host publication	IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Publisher	IEEE
Pages	3512-3515
Number of pages	4
ISBN (Electronic)	978-1-6654-2792-0, 978-1-6654-2791-3
ISBN (Print)	978-1-6654-2793-7
DOIs	https://doi.org/10.1109/igarss46834.2022.9883208
Publication status	Published (in print/issue) - 28 Sept 2022
Event	2022 IEEE International Geoscience and Remote Sensing Symposium IGARSS. - Kuala Lumpur, Malaysia Duration: 17 Jul 2022 → 22 Jul 2022

Publication series

Name
ISSN (Print)	2153-6996
ISSN (Electronic)	2153-7003

Conference

Conference	2022 IEEE International Geoscience and Remote Sensing Symposium IGARSS.
Country/Territory	Malaysia
City	Kuala Lumpur
Period	17/07/22 → 22/07/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Deep learning
outcrop
fracture detection
ResNeXt
U-Net
Image segmentation
Costsw
Geology
Oils
Data integrity
Neural networks
Geoscience and remote sensing

Access to Document

10.1109/igarss46834.2022.9883208

Wang_igarss_2022_accepted_manuscriptAuthor Accepted Manuscript, 741 KBLicence: Unspecified

Cite this

@inproceedings{bc68941ba9a440e0b7542054f5a8e5e1,

title = "Application of an Improved U-Net Neural Network on Fracture Segmentation from Outcrop Images",

abstract = "Outcrop records contain very rich geological historical information, and the study of fractures in outcrop areas is an important part of geological exploration work. The accurate fracture information can provide useful technical support for the development and exploration of subsurface oil and gas. The outcrop images usually include unclear boundaries, complex structure and inconspicuous features, which make fracture detection from outcrop images a difficult task. To tackle these challenges, an improved U-Net algorithm based on the ResNeXt module is proposed in this paper to segment the fractures from the outcrop images. Experiments are conducted on the outcrop images from Yijianfang area in the Tarim Basin in China, and the results show that the proposed algorithm has improved the accuracy and IoU in fracture segmentation from the outcrop images.",

keywords = "Deep learning, outcrop, fracture detection, ResNeXt, U-Net, Image segmentation, Costsw, Geology, Oils, Data integrity, Neural networks, Geoscience and remote sensing",

author = "Zhibao Wang and Ziming Zhang and Lu Bai and Yuze Yang and Qiang Ma",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Geoscience and Remote Sensing Symposium IGARSS. ; Conference date: 17-07-2022 Through 22-07-2022",

year = "2022",

month = sep,

day = "28",

doi = "10.1109/igarss46834.2022.9883208",

language = "English",

isbn = "978-1-6654-2793-7",

publisher = "IEEE",

pages = "3512--3515",

booktitle = "IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",

address = "United States",

}

Wang, Z, Zhang, Z, Bai, L, Yang, Y & Ma, Q 2022, Application of an Improved U-Net Neural Network on Fracture Segmentation from Outcrop Images. in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, pp. 3512-3515, 2022 IEEE International Geoscience and Remote Sensing Symposium IGARSS., Kuala Lumpur, Malaysia, 17/07/22. https://doi.org/10.1109/igarss46834.2022.9883208

TY - GEN

T1 - Application of an Improved U-Net Neural Network on Fracture Segmentation from Outcrop Images

AU - Wang, Zhibao

AU - Zhang, Ziming

AU - Bai, Lu

AU - Yang, Yuze

AU - Ma, Qiang

PY - 2022/9/28

Y1 - 2022/9/28

N2 - Outcrop records contain very rich geological historical information, and the study of fractures in outcrop areas is an important part of geological exploration work. The accurate fracture information can provide useful technical support for the development and exploration of subsurface oil and gas. The outcrop images usually include unclear boundaries, complex structure and inconspicuous features, which make fracture detection from outcrop images a difficult task. To tackle these challenges, an improved U-Net algorithm based on the ResNeXt module is proposed in this paper to segment the fractures from the outcrop images. Experiments are conducted on the outcrop images from Yijianfang area in the Tarim Basin in China, and the results show that the proposed algorithm has improved the accuracy and IoU in fracture segmentation from the outcrop images.

AB - Outcrop records contain very rich geological historical information, and the study of fractures in outcrop areas is an important part of geological exploration work. The accurate fracture information can provide useful technical support for the development and exploration of subsurface oil and gas. The outcrop images usually include unclear boundaries, complex structure and inconspicuous features, which make fracture detection from outcrop images a difficult task. To tackle these challenges, an improved U-Net algorithm based on the ResNeXt module is proposed in this paper to segment the fractures from the outcrop images. Experiments are conducted on the outcrop images from Yijianfang area in the Tarim Basin in China, and the results show that the proposed algorithm has improved the accuracy and IoU in fracture segmentation from the outcrop images.

KW - Deep learning

KW - outcrop

KW - fracture detection

KW - ResNeXt

KW - U-Net

KW - Image segmentation

KW - Costsw

KW - Geology

KW - Oils

KW - Data integrity

KW - Neural networks

KW - Geoscience and remote sensing

UR - https://www.scopus.com/pages/publications/85140373891

U2 - 10.1109/igarss46834.2022.9883208

DO - 10.1109/igarss46834.2022.9883208

M3 - Conference contribution

SN - 978-1-6654-2793-7

SP - 3512

EP - 3515

BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

PB - IEEE

T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium IGARSS.

Y2 - 17 July 2022 through 22 July 2022

ER -

Application of an Improved U-Net Neural Network on Fracture Segmentation from Outcrop Images

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

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Cite this