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The structure-tensor analysis for optimal microseismic data partial stack

Loginov, Georgy N.; Duchkov, Anton and Andersson, Fredrik LU (2016) Meeting 2016 - Society of Exploration Geophysicists In SEG Technical Program Expanded Abstracts 2016 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.

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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
SEG Technical Program Expanded Abstracts 2016
volume
35
pages
5 pages
publisher
Society of Exploration Geophysicists
conference name
Meeting 2016 - Society of Exploration Geophysicists
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
2017-06-04 05:00:19
@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},
  volume       = {35},
  year         = {2016},
}