Seismicity Rate Change as a Tool to Investigate Delayed and Remote Triggering of the 2010–2011 Canterbury Earthquake Sequence, New Zealand

ABSTRACT Crustal earthquakes in low-strain-rate regions are rare in the human life span but can generate disastrous consequences when they occur. Such was the case in the Canterbury earthquake sequence that began in 2010 and eventually led to almost 200 fatalities. Our study explores this earthquake sequence’s origins by producing an enhanced earthquake catalog in the Canterbury Plains and Otago, South Island, New Zealand. We investigate seismicity rate changes from 2005 to before the 2010 Mw 7.2 Darfield earthquake. During this time, major subduction-zone earthquakes, such as the 2009 Mw 7.8 Dusky Sound earthquake, created measurable coseismic and postseismic strain in the region. We use template matching to expand the catalog of earthquakes in the region, and use a support vector machine classifier to remove false positives and poor detections. We then compare the newly obtained seismicity rates with the coseismic and postseismic crustal strain fields, and find that seismicity rate and crustal strain are positively correlated in the low-stress, low-seismicity region of the northern Canterbury Plains. In contrast, near fast-moving plate-boundary faults, the seismicity rate changes rise without much change in the strain rate. Our analysis reveals a substantial seismicity rate decrease in the western rupture area of the Darfield earthquake, which we infer to be an effect of coseismic and postseismic deformation caused by the Dusky Sound earthquake. We show in low-strain-rate regions, stress perturbation of a few kPas creates substantial seismicity rate change. However, the implication that such seismic quiescence is responsible for the nucleation of the Darfield earthquake requires further studies.

Seismic Hazard Function Mapping Using Estimated Horizontal Crustal Strain Off West Coast Northern Sumatra

A seismic hazard study and analysis of the megathrust source off the west coast of North Sumatra, Indonesia, were conducted based on the estimated horizontal crustal strain using the surface displacement data. This area was selected due to the availability of pre- and co-seismic Global Positioning System (GPS) data for the 2005 Nias–Simeulue Mw 8.6 event. This study aimed to estimate the seismic hazard function (SHF), which is expressed as peak ground acceleration (PGA) versus probability of exceedance (PE), for a 500 years return period using GPS data. The source area model of the Mw 8.6 event is determined based on the co-seismic GPS data. The horizontal crustal strain of the source area is estimated using least square prediction employing local covariance functions based on the horizontal displacement data. The Mw 8.6 return period is estimated by dividing the sum of the co-seismic seismic moment by the pre-seismic seismic moment based on GPS data. The seismicity rate model above a magnitude of completeness is then estimated assuming the b-value of 1 obtained on the previous study’s earthquake catalog data in the region. We show that the SHF based on the study area’s horizontal crustal strain is higher than the one based on earthquake catalogs and estimated geological sliprate data. This discrepancy is associated with the static stress increase (Coulomb failure stress, CFS) of about 0.25 bar imparted by the 2004 Aceh Mw 9.1 event that occurred in the north of the study region. We interpreted that the increase of the SHF was due to the increase in the region’s stress load, which was well documented by the GPS data.

Crustal strain-rate fields estimated from GNSS data with a Bayesian approach and its correlation to seismic activity

<p>We proposed a new Bayesian approach to estimate continuous crustal strain-rate fields from spatially discrete displacement-rate data, based on Global Navigation Satellite System (GNSS) observations, under the prior constraint on spatial flatness of the strain-rate fields. The optimal values of the hyperparameters in the model of strain-rate fields are determined by using Akaike's Bayesian Information Criterion. A methodological merit of this approach is that, by introducing a two-layer Delaunay tessellation technique, the time-consuming computation of strain rates can be omitted through the model estimation process. We applied the Bayesian approach to GNSS displacement-rate data in Mainland China and examined the correlation between the estimated strain-rate fields and seismic activity by using Molchan’s Error Diagram. The results show that the increase rate of maximum shear strain is positively correlated with the occurrence of earthquakes, indicating the strain rate can be used to augment probability earthquake models for background seismicity forecasting.</p>

How Heavy Rain and Drought Influence California Crustal Strain

New research using continuous GPS data reveals how multiyear precipitation patterns can amplify the effects of hydrological loading on crustal deformation.