The interevent time fingerprint of t riggering for induced seismicity Mark Naylor

1. The interevent time fingerprint of triggering for induced seismicityMark Naylor School of GeoSciences University of Edinburgh

2. Earthquake inter-event timesETAS - Branching model simulations Magnitude Time Parents 1st order daughters 2nd order daughters …

3. Time Dependent event pairs Earthquake inter-event timesIn simulation we know the “Marks” Magnitude Independent event pairs (Touati, Naylor and Main, Physical review letters, 102, 168501)

4. magnitude time But in real data… … we don’t know the Marks ? (Touati, Naylor and Main, Physical review letters, 102, 168501)

5. Data Synthetic

6. Time Dependent event pairs Earthquake inter-event timesBase case Magnitude Independent event pairs (Touati, Naylor, Main and Christie, JGR, Submitted)

7. Time Time Earthquake inter-event timesSame aftershock properties, Vary rate Low seeding rate Magnitude Magnitude Higher seeding rate Masks correlated event pairs (Touati, Naylor, Main and Christie, JGR, Submitted)

8. “Stationarity filter?” Low rate, IETs~Crossover dist Background rate is constant Varies between runs Analogous for region size All other parameters are the same Overlap of aftershock sequences varies and removes dependent event pairs from the time series High rate, IETs~Exponential

9. Loss of correlations due to overlap fools inversion into predicting higher background rates Implication:Inversion for background rate

10. Space can also help identify clustering But I will consider 3 cases which are spatially localised

13. Geophysical Research LettersVolume 38, Issue 21, L21302, 4 NOV 2011 DOI: 10.1029/2011GL049474http://onlinelibrary.wiley.com/doi/10.1029/2011GL049474/full#grl28625-fig-0005

14. Geophysical Research LettersVolume 38, Issue 21, L21302, 4 NOV 2011 DOI: 10.1029/2011GL049474http://onlinelibrary.wiley.com/doi/10.1029/2011GL049474/full#grl28625-fig-0005

15. Does fluid injection suppress local seismicity? But, what about the non-stationary periods? Here we can’t easily compare high and low rate conditions Geophysical Research LettersVolume 38, Issue 21, L21302, 4 NOV 2011 DOI: 10.1029/2011GL049474http://onlinelibrary.wiley.com/doi/10.1029/2011GL049474/full#grl28625-fig-0005

16. Vesuvius Etna Intrusions

18. Colfiorifo, Umbria-Marche 1997-1998 sequence(Italy)

21. Standard ML ETAS inversion and simulation

22. Resample with uncertainty constrained using the long term rate

24. Summary • We observe the same tending towards a “Poisson” signal in 3 different settings • fluid injection, volcanic, tectonic/fluid • Is fluid driven seismicity genuinely more “Poissonian”? • If so, what process inhibits cascading aftershocks? • Or, are the triggering processes the same? • Do the higher rates and tight spatial proximity mask the triggering signal?

25. 2. Convergence in frequency magnitude distributions • We choose to distinguish between • GR: F(M) ~ M-b • Modified GR F(M) ~ M-b exp(-M/q) q is the corner or characteristic moment • We do not explicitly consider different forms of the rolloff (currently) – assume that there is not sufficient data to resolve form • We want to understand what the convergence trends in a BIC metric will look like as we start to resolve roll-off • Particularly since the safety case for some industries relies on their estimations of maximum magnitudes • We do not attempt to consider the harder question of the risk of triggering larger, inherited structures (important in UK)

26. Evolution of DBIC for GR synthetic

27. Evolution of DBIC for mGR synthetic

28. GR mGR

29. Low b High b

30. Low q High q

31. Analysis of California…

32. Snapshots of Global CMT • Beta converging • Corner moment unconstrained • We previously used DBIC to discriminate models (Main et al 2008)

33. Snapshots of Global CMT • Beta converging • Corner moment unconstrained • Confidence intervals defined by sampling likelihood space

34. Global CMT (Lower cutoff 5.75 Mw)

35. Comparison with GR bootstrap

36. Comments • Convergence trend for: • California consistent with GR sampling • Global CMT appears inconsistent with pure GR • We are currently running large bootstrap to verify this • If the global catalogue is just sampling GR… • …we are observing an uncommon sample • Alternative interpretation: • Global CMT catalogue represents a mixture different subsets with various roll-offs • Next step: • Analyse more regional tectonic catalogues • Analyse high resolution catalogues that may resolve roll-off • Geysers? Mining data?

38. Sensitivity of Global CMT to cutoff

39. Kilauea and Mauna Loa

41. Volcanic precursors – Caldera IETs • Accelerations are due to the failure of new rock as magma is injected • More hope of forecasting failure in such systems

42. A simpler (but still hard) problem:Forecasting (asymptotic) failure Failure Forecasting Method: Least squares on GLM: Power law-link function with Gaussian (top) or Poisson (bottom) error structure

43. FFM vs GLM: Synthetic

44. FFM vs GLM: Real data AE– brittle creep AE – Mnt Etna (preceeding eruption) Strain – brittle creep