The structure-tensor analysis for optimal microseismic data partial stack
(2016) Meeting 2016 - Society of Exploration Geophysicists 35. p.2612-2616- Abstract
Microseismic monitoring of hydrofrac is an actively developing technology utilizing various acquizition arrays. In this paper we consider processing of microseismic data recorded by specific surface network geometry-patch arrays (far separated local receiver groups). The project aim is to produce an optimal partial stacking of the data within patches for improving a signal to noise ratio for microseismic events detection and location. We propose to use a structure-tensor analysis for estimating directions of coherency in the data, which can be used for data stacking for each patch. Unlike to the standard slantstacking method, we do not scan all possible directions, but receive them as eigenvectors of the structure tensor. We used the... (More)
Microseismic monitoring of hydrofrac is an actively developing technology utilizing various acquizition arrays. In this paper we consider processing of microseismic data recorded by specific surface network geometry-patch arrays (far separated local receiver groups). The project aim is to produce an optimal partial stacking of the data within patches for improving a signal to noise ratio for microseismic events detection and location. We propose to use a structure-tensor analysis for estimating directions of coherency in the data, which can be used for data stacking for each patch. Unlike to the standard slantstacking method, we do not scan all possible directions, but receive them as eigenvectors of the structure tensor. We used the synthetic data for testing our approach in presence of random and coherent noise, in the case of interfering events. The testing showed that the structure-tensor analysis provides robust coherent summation results. We also discuss the usefulness of the structure-tensor attributes for detecting (triggering) the arriving wave and separating body wave from surface waves based on the apparent velocity.
(Less)
- author
- Loginov, Georgy N. ; Duchkov, Anton and Andersson, Fredrik LU
- organization
- publishing date
- 2016
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- SEG Technical Program Expanded Abstracts 2016
- volume
- 35
- pages
- 5 pages
- publisher
- Society of Exploration Geophysicists
- conference name
- Meeting 2016 - Society of Exploration Geophysicists
- conference location
- Dallas, United States
- conference dates
- 2016-10-16 - 2016-10-21
- external identifiers
-
- scopus:85019087503
- DOI
- 10.1190/segam2016-13972705.1
- language
- English
- LU publication?
- yes
- id
- 411c7510-298e-4617-b97a-725fefa6718b
- date added to LUP
- 2017-06-01 15:38:11
- date last changed
- 2022-01-30 20:38:59
@inproceedings{411c7510-298e-4617-b97a-725fefa6718b,
abstract = {{<p>Microseismic monitoring of hydrofrac is an actively developing technology utilizing various acquizition arrays. In this paper we consider processing of microseismic data recorded by specific surface network geometry-patch arrays (far separated local receiver groups). The project aim is to produce an optimal partial stacking of the data within patches for improving a signal to noise ratio for microseismic events detection and location. We propose to use a structure-tensor analysis for estimating directions of coherency in the data, which can be used for data stacking for each patch. Unlike to the standard slantstacking method, we do not scan all possible directions, but receive them as eigenvectors of the structure tensor. We used the synthetic data for testing our approach in presence of random and coherent noise, in the case of interfering events. The testing showed that the structure-tensor analysis provides robust coherent summation results. We also discuss the usefulness of the structure-tensor attributes for detecting (triggering) the arriving wave and separating body wave from surface waves based on the apparent velocity.</p>}},
author = {{Loginov, Georgy N. and Duchkov, Anton and Andersson, Fredrik}},
booktitle = {{SEG Technical Program Expanded Abstracts 2016}},
language = {{eng}},
pages = {{2612--2616}},
publisher = {{Society of Exploration Geophysicists}},
title = {{The structure-tensor analysis for optimal microseismic data partial stack}},
url = {{http://dx.doi.org/10.1190/segam2016-13972705.1}},
doi = {{10.1190/segam2016-13972705.1}},
volume = {{35}},
year = {{2016}},
}