The interevent time fingerprint of triggering for induced seismicity Mark Naylor School of GeoSciences University of Edinburgh

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

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

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

Data Synthetic

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

Earthquake inter-event times Same aftershock properties, Vary rate Time Magnitude Time Magnitude (Touati, Naylor, Main and Christie, JGR, Submitted) Low seeding rate Higher seeding rate Masks 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?”

Low rate, IETs~Crossover dist High rate, IETs~Exponential Can interpret distributions in terms of rates in some defined region

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

Earthquake inter-event times Longer aftershock sequences, same rate Time Magnitude Time Magnitude (Touati, Naylor, Main and Christie, JGR, Submitted)

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

Poorly separated in space and time => Inversion will again overestimate background rate

Geophysical Research Letters Volume 38, Issue 21, L21302, 4 NOV 2011 DOI: /2011GL Volume 38, Issue 21,

Geophysical Research Letters Volume 38, Issue 21, L21302, 4 NOV 2011 DOI: /2011GL Volume 38, Issue 21,

Geophysical Research Letters Volume 38, Issue 21, L21302, 4 NOV 2011 DOI: /2011GL Volume 38, Issue 21, 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 Etna Intrusions

Colfiorifo, Umbria-Marche 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 -  – Modified GRF(M) ~ M -  exp(-M/  )  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  BIC for GR synthetic

Evolution of  BIC for mGR synthetic

GR mGR

Low b High b

Low  High 

Analysis of California…

Snapshots of Global CMT Catalogue not declustered Mean monthly event rate increasing (Running) Mean monthly moment rate increasing (Running)

Snapshots of Global CMT Beta converging Corner moment unconstrained We previously used  BIC 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 data Strain – brittle creep AE– brittle creep AE – Mnt Etna (preceeding eruption)