ORCID Profile
0000-0003-1001-3676
Current Organisation
GNS Science Ltd
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Publisher: Wiley
Date: 12-2020
Publisher: Seismological Society of America (SSA)
Date: 02-2012
DOI: 10.1785/0120110064
Publisher: Seismological Society of America (SSA)
Date: 31-07-2013
DOI: 10.1785/0120120059
Publisher: Seismological Society of America (SSA)
Date: 22-04-2014
DOI: 10.1785/0120130120
Publisher: IEEE
Date: 05-2010
Publisher: IEEE
Date: 05-2010
Publisher: MDPI AG
Date: 05-07-2022
DOI: 10.3390/APP12136804
Abstract: Strain rates have been included in multiplicative hybrid modelling of the long-term spatial distribution of earthquakes in New Zealand (NZ) since 2017. Previous modelling has shown a strain rate model to be the most informative input to explain earthquake locations over a fitting period from 1987 to 2006 and a testing period from 2012 to 2015. In the present study, three different shear strain rate models have been included separately as covariates in NZ multiplicative hybrid models, along with other covariates based on known fault locations, their associated slip rates, and proximity to the plate interface. Although the strain rate models differ in their details, there are similarities in their contributions to the performance of hybrid models in terms of information gain per earthquake (IGPE). The inclusion of each strain rate model improves the performance of hybrid models during the previously adopted fitting and testing periods. However, the hybrid models, including strain rates, perform poorly in a reverse testing period from 1951 to 1986. Molchan error diagrams show that the correlations of the strain rate models with earthquake locations are lower over the reverse testing period than from 1987 onwards. Smoothed scatter plots of the strain rate covariates associated with target earthquakes versus time confirm the relatively low correlations before 1987. Moreover, these analyses show that other covariates of the multiplicative models, such as proximity to the plate interface and proximity to mapped faults, were better correlated with earthquake locations prior to 1987. These results suggest that strain rate models based on only a few decades of available geodetic data from a limited network of GNSS stations may not be good indicators of where earthquakes occur over a long time frame.
Publisher: MDPI AG
Date: 06-11-2020
DOI: 10.3390/E22111264
Abstract: ‘Every Earthquake a Precursor According to Scale’ (EEPAS) is a catalogue-based model to forecast earthquakes within the coming months, years and decades, depending on magnitude. EEPAS has been shown to perform well in seismically active regions like New Zealand (NZ). It is based on the observation that seismicity increases prior to major earthquakes. This increase follows predictive scaling relations. For larger target earthquakes, the precursor time is longer and precursory seismicity may have occurred prior to the start of the catalogue. Here, we derive a formula for the completeness of precursory earthquake contributions to a target earthquake as a function of its magnitude and lead time, where the lead time is the length of time from the start of the catalogue to its time of occurrence. We develop two new versions of EEPAS and apply them to NZ data. The Fixed Lead time EEPAS (FLEEPAS) model is used to examine the effect of the lead time on forecasting, and the Fixed Lead time Compensated EEPAS (FLCEEPAS) model compensates for incompleteness of precursory earthquake contributions. FLEEPAS reveals a space-time trade-off of precursory seismicity that requires further investigation. Both models improve forecasting performance at short lead times, although the improvement is achieved in different ways.
Publisher: MDPI AG
Date: 19-09-2022
DOI: 10.3390/GEOSCIENCES12090349
Abstract: Nearly 20 years ago, the observation that major earthquakes are generally preceded by an increase in the seismicity rate on a timescale from months to decades was embedded in the “Every Earthquake a Precursor According to Scale” (EEPAS) model. EEPAS has since been successfully applied to regional real-world and synthetic earthquake catalogues to forecast future earthquake occurrence rates with time horizons up to a few decades. When combined with aftershock models, its forecasting performance is improved for short time horizons. As a result, EEPAS has been included as the medium-term component in public earthquake forecasts in New Zealand. EEPAS has been modified to advance its forecasting performance despite data limitations. One modification is to compensate for missing precursory earthquakes. Precursory earthquakes can be missing because of the time-lag between the end of a catalogue and the time at which a forecast applies or the limited lead time from the start of the catalogue to a target earthquake. An observed space-time trade-off in precursory seismicity, which affects the EEPAS scaling parameters for area and time, also can be used to improve forecasting performance. Systematic analysis of EEPAS performance on synthetic catalogues suggests that regional variations in EEPAS parameters can be explained by regional variations in the long-term earthquake rate. Integration of all these developments is needed to meet the challenge of producing a global EEPAS model.
Publisher: Seismological Society of America (SSA)
Date: 21-11-2017
DOI: 10.1785/0120150228
Publisher: Society of Exploration Geophysicists
Date: 27-08-2018
Publisher: MDPI AG
Date: 31-10-2021
DOI: 10.3390/APP112110215
Abstract: The ‘Every Earthquake a Precursor According to Scale’ (EEPAS) medium-term earthquake forecasting model is based on the precursory scale increase (Ψ) phenomenon and associated scaling relations, in which the precursor magnitude MP is predictive of the mainshock magnitude Mm, precursor time TP and precursory area AP. In early studies of Ψ, a relatively low correlation between TP and AP suggested the possibility of a trade-off between time and area as a second-order effect. Here, we investigate the trade-off by means of the EEPAS model. Existing versions of EEPAS in New Zealand and California forecast target earthquakes of magnitudes M 4.95 from input catalogues with M 2.95. We systematically vary one parameter each from the EEPAS distributions for time and location, thereby varying the temporal and spatial scales of these distributions by two orders of magnitude. As one of these parameters is varied, the other is refitted to a 20-year period of each catalogue. The resulting curves of the temporal scaling factor against the spatial scaling factor are consistent with an even trade-off between time and area, given the limited temporal and spatial extent of the input catalogue. Hybrid models are formed by mixing several EEPAS models, with parameter sets chosen from points on the trade-off line. These are tested against the original fitted EEPAS models on a subsequent period of the New Zealand catalogue. The resulting information gains suggest that the space–time trade-off can be exploited to improve forecasting.
Publisher: Seismological Society of America (SSA)
Date: 27-10-2023
DOI: 10.1785/0120230080
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Sepideh J Rastin.