On the importance of benchmarking algorithms under realistic noise conditions

Claire Birnie*, Kit Chambers, Doug Angus, Anna L. Stork

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Testingwith synthetic data sets is a vital stage in an algorithm's development for benchmarking the algorithm's performance. A common addition to synthetic data sets is White, Gaussian Noise (WGN) which is used to mimic noise that would be present in recorded data sets. The first section of this paper focuses on comparing the effects of WGN and realistic modelled noise on standard microseismic event detection and imaging algorithms using synthetic data sets with recorded noise as a benchmark. The data sets with WGN underperform on the traceby- trace algorithmwhile overperforming on algorithms utilizing the full array. Throughout, the data sets with realistic modelled noise perform near identically to the recorded noise data sets. The study concludes by testing an algorithm that simultaneously solves for the source location and moment tensor of a microseismic event. Not only does the algorithm fail to perform at the signal-to-noise ratios indicated by the WGN results but the results with realistic modelled noise highlight pitfalls of the algorithm not previously identified. The misleading results from theWGN data sets highlight the need to test algorithms under realistic noise conditions to gain an understanding of the conditions under which an algorithm can perform and to minimize the risk of misinterpretation of the results.

Original languageEnglish (US)
Pages (from-to)504-520
Number of pages17
JournalGeophysical Journal International
Issue number1
StatePublished - Apr 1 2020

Bibliographical note

Funding Information:
The authors would like to thank the Petroleum Technology Research Centre (PTRC) for access to Aquistore Data. Aquistore is an independent research and monitoring project managed by the PTRC which intends to demonstrate that storing liquid carbon dioxide deep underground (in a brine and sandstone water formation) is a safe, workable solution to reduce greenhouse gases. CB is funded by the NERC Open CASE studentship NE/L009226/1 and Pinnacle-Halliburton. DA acknowledges the Research Council UK (EP/K035878/1, EP/K021869/1 and NE/L000423/1) for financial support. ALS thanks the Bristol University Microseismicity Projects (BUMPS) sponsors for supporting this research.

Publisher Copyright:
© The Author(s) 2020.


  • Induced seismicity
  • Numerical modelling
  • Site effects
  • Statistical methods
  • Statistical seismology
  • Time-series analysis

ASJC Scopus subject areas

  • Geophysics
  • Geochemistry and Petrology


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