The interevent time fingerprint of triggering for induced seismicity

Mark Naylor

School of GeoSciencesUniversity of Edinburgh

Earthquake inter-event timesETAS - Branching model simulations

Time

Mag

nitu

de

Parents

1st order daughters

2nd order daughters

…

Earthquake inter-event timesIn simulation we know the “Marks”

(Touati, Naylor and Main, Physical review letters, 102, 168501)

Dependentevent pairs

Independentevent pairs

Time

Mag

nitu

de

time

mag

nitu

de

But in real data… … we don’t know the Marks

(Touati, Naylor and Main, Physical review letters, 102, 168501)

?

Data

Synthetic

Earthquake inter-event timesBase case

Dependentevent pairs

Independentevent pairs

Time

Mag

nitu

de

(Touati, Naylor, Main and Christie, JGR, Submitted)

Earthquake inter-event timesSame aftershock properties, Vary rate

Time

Mag

nitu

de

Time

Mag

nitu

de

(Touati, Naylor, Main and Christie, JGR, Submitted)

Low seeding rate

Higher seeding rateMasks correlated event pairs

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

Low rate, IETs~Crossover dist

High rate, IETs~Exponential

“Stationarity filter?”

Implication:Inversion for background

rate

Loss of correlations due to overlap fools inversion into predicting higher background rates

Space can also help identify clustering

But I will consider 3 cases which are spatially localised

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

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

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

Does fluid injection suppress local seismicity?

But, what about the non-stationary periods?Here we can’t easily compare high and low rate conditions

Vesuvius EtnaIntrusions

Colfiorifo, Umbria-Marche

1997-1998 sequence

(Italy)

Standard ML ETAS inversion and simulation

Resample with uncertainty constrained using the long term rate

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?

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)

Evolution of DBIC for GR synthetic

Evolution of DBIC for mGR synthetic

GR mGR

Low b

High b

Low q

High q

Analysis of California…

Snapshots of Global CMT

• Beta converging• Corner moment

unconstrained • We previously used DBIC to discriminate models (Main et al 2008)

Snapshots of Global CMT

• Beta converging• Corner moment

unconstrained • Confidence intervals

defined by sampling likelihood space

Global CMT (Lower cutoff 5.75 Mw)

Comparison with GR bootstrap

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?

Sensitivity of Global CMT to cutoff

Kilauea and Mauna Loa

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

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

FFM vs GLM: Synthetic

FFM vs GLM: Real dataStrain – brittle creep AE– brittle creep AE – Mnt Etna

(preceeding eruption)