Detection and Denoising of Microseismic Events Using Time–Frequency Representation and Tensor Decomposition
Reliable detection and recovery of a microseismic event in large volume of passive monitoring data is usually a challenging task due to the low signal-to-noise ratio environment. The accuracy of weak microseismic event identification is a very important step in the analysis and interpretation of mic...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8351950/ |
| _version_ | 1818853690931412992 |
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| author | Naveed Iqbal Entao Liu James H. McClellan Abdullatif Al-Shuhail Sanlinn I. Kaka Azzedine Zerguine |
| author_facet | Naveed Iqbal Entao Liu James H. McClellan Abdullatif Al-Shuhail Sanlinn I. Kaka Azzedine Zerguine |
| author_sort | Naveed Iqbal |
| collection | DOAJ |
| description | Reliable detection and recovery of a microseismic event in large volume of passive monitoring data is usually a challenging task due to the low signal-to-noise ratio environment. The accuracy of weak microseismic event identification is a very important step in the analysis and interpretation of microseismic data. This paper introduces an approach for detecting (presence indication) and denoising (accurate recovery) microseismic events using tensor decomposition by considering the time-frequency representation of multiple traces as a 3-D tensor. A tensor is a multiway array having dimension greater than two, and recent signal processing techniques have been developed to manipulate such data by taking advantage of the multidimensional structure. With advances in technology and the availability of cheap memory, it is now possible to store and do mathematical operations, such as higher order singular-value decomposition or tensor decomposition, on multiway data. In active seismic, tensor decomposition has been used for multidimensional reconstruction via higher order interpolation to obtain missing observations. In this paper, we use 3-D tensor decomposition to process passive seismic data. Experiments performed on synthetic and field data sets show promising results achieved by these new methods. |
| first_indexed | 2024-12-19T07:40:50Z |
| format | Article |
| id | doaj.art-d25c2d1a29f34eaba0386281611adc19 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:40:50Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d25c2d1a29f34eaba0386281611adc192022-12-21T20:30:27ZengIEEEIEEE Access2169-35362018-01-016229932300610.1109/ACCESS.2018.28309758351950Detection and Denoising of Microseismic Events Using Time–Frequency Representation and Tensor DecompositionNaveed Iqbal0https://orcid.org/0000-0002-2633-9761Entao Liu1James H. McClellan2Abdullatif Al-Shuhail3Sanlinn I. Kaka4Azzedine Zerguine5Center of Energy and Geo Processing, King Fahd University of Petroleum & Minerals, Dhahran, Saudi ArabiaCenter of Energy and Geo Processing, Georgia Institute of Technology, Atlanta, GA, USACenter of Energy and Geo Processing, Georgia Institute of Technology, Atlanta, GA, USACenter of Energy and Geo Processing, King Fahd University of Petroleum & Minerals, Dhahran, Saudi ArabiaCenter of Energy and Geo Processing, King Fahd University of Petroleum & Minerals, Dhahran, Saudi ArabiaCenter of Energy and Geo Processing, King Fahd University of Petroleum & Minerals, Dhahran, Saudi ArabiaReliable detection and recovery of a microseismic event in large volume of passive monitoring data is usually a challenging task due to the low signal-to-noise ratio environment. The accuracy of weak microseismic event identification is a very important step in the analysis and interpretation of microseismic data. This paper introduces an approach for detecting (presence indication) and denoising (accurate recovery) microseismic events using tensor decomposition by considering the time-frequency representation of multiple traces as a 3-D tensor. A tensor is a multiway array having dimension greater than two, and recent signal processing techniques have been developed to manipulate such data by taking advantage of the multidimensional structure. With advances in technology and the availability of cheap memory, it is now possible to store and do mathematical operations, such as higher order singular-value decomposition or tensor decomposition, on multiway data. In active seismic, tensor decomposition has been used for multidimensional reconstruction via higher order interpolation to obtain missing observations. In this paper, we use 3-D tensor decomposition to process passive seismic data. Experiments performed on synthetic and field data sets show promising results achieved by these new methods.https://ieeexplore.ieee.org/document/8351950/Tensor decompositionhigher order singular values decomposition (HOSVD)microseismicdenoisingdetectionnuclear norm |
| spellingShingle | Naveed Iqbal Entao Liu James H. McClellan Abdullatif Al-Shuhail Sanlinn I. Kaka Azzedine Zerguine Detection and Denoising of Microseismic Events Using Time–Frequency Representation and Tensor Decomposition IEEE Access Tensor decomposition higher order singular values decomposition (HOSVD) microseismic denoising detection nuclear norm |
| title | Detection and Denoising of Microseismic Events Using Time–Frequency Representation and Tensor Decomposition |
| title_full | Detection and Denoising of Microseismic Events Using Time–Frequency Representation and Tensor Decomposition |
| title_fullStr | Detection and Denoising of Microseismic Events Using Time–Frequency Representation and Tensor Decomposition |
| title_full_unstemmed | Detection and Denoising of Microseismic Events Using Time–Frequency Representation and Tensor Decomposition |
| title_short | Detection and Denoising of Microseismic Events Using Time–Frequency Representation and Tensor Decomposition |
| title_sort | detection and denoising of microseismic events using time x2013 frequency representation and tensor decomposition |
| topic | Tensor decomposition higher order singular values decomposition (HOSVD) microseismic denoising detection nuclear norm |
| url | https://ieeexplore.ieee.org/document/8351950/ |
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