Segmentation of Geophysical Data: A Big Data Friendly Approach Article Swipe
David Stockwell
,
Ligang Zhang
,
Brijesh Verma
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2015.07.277
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2015.07.277
A new scalable segmentation algorithm is proposed in this paper for the forensic determination of level shifts in geophysical time series. While a number of segmentation algorithms exist, they are generally not ‘big data friendly’ due either to quadratic scaling of computation time in the length of the series N or subjective penalty parameters. The proposed algorithm is called SumSeg as it collects a table of potential break points via iterative ternary splits on the extreme values of the scaled partial sums of the data. It then filters the break points on their statistical significance and peak shape. Our algorithm is linear in N and logarithmic in the number of breaks B, while returning a flexible nested segmentation model that can be objectively evaluated using the area under the receiver operator curve (AUC). We demonstrate the comparative performance of SumSeg against three other algorithms. SumSeg is available as an R package from the development site at http://github.com/davids99us/anomaly.
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- Type
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- OA Status
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- 19
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- OpenAlex ID
- https://openalex.org/W885561412
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Raw OpenAlex JSON
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https://openalex.org/W885561412Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.procs.2015.07.277Digital Object Identifier
- Title
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Segmentation of Geophysical Data: A Big Data Friendly ApproachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2015Year of publication
- Publication date
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2015-01-01Full publication date if available
- Authors
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David Stockwell, Ligang Zhang, Brijesh VermaList of authors in order
- Landing page
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https://doi.org/10.1016/j.procs.2015.07.277Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.procs.2015.07.277Direct OA link when available
- Concepts
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Computer science, Big data, Segmentation, Geophysics, Data science, Data mining, Artificial intelligence, GeologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
- References (count)
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19Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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