Inference for extreme earthquake magnitudes accounting for a time-varying measurement process

02/01/2021
by   Zak Varty, et al.
0

Investment in measuring a process more completely or accurately is only useful if these improvements can be utilised during modelling and inference. We consider how improvements to data quality over time can be incorporated when selecting a modelling threshold and in the subsequent inference of an extreme value analysis. Motivated by earthquake catalogues, we consider variable data quality in the form of rounded and incompletely observed data. We develop an approach to select a time-varying modelling threshold that makes best use of the available data, accounting for uncertainty in the magnitude model and for the rounding of observations. We show the benefits of the proposed approach on simulated data and apply the method to a catalogue of earthquakes induced by gas extraction in the Netherlands. This more than doubles the usable catalogue size and greatly increases the precision of high magnitude quantile estimates. This has important consequences for the design and cost of earthquake defences. For the first time, we find compelling data-driven evidence against the applicability of the Gutenberg-Richer law to these earthquakes. Furthermore, our approach to automated threshold selection appears to have much potential for generic applications of extreme value methods.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

08/07/2020

From the power law to extreme value mixture distributions

The power law is useful in describing count phenomena such as network de...
06/10/2020

A different approach for choosing a threshold in peaks over threshold

Abstract In Extreme Value methodology the choice of threshold plays an i...
02/25/2019

A changepoint approach for the identification of financial extreme regimes

Inference over tails is usually performed by fitting an appropriate limi...
05/21/2019

L-moments for automatic threshold selection in extreme value analysis

In extreme value analysis, sensitivity of inference to the definition of...
04/17/2019

Estimation and uncertainty quantification for extreme quantile regions

Estimation of extreme quantile regions, spaces in which future extreme e...
09/28/2020

Calibration methods for spatial Data

In an environmental framework, extreme values of certain spatio-temporal...
10/23/2020

Geostatistical models for zero-inflated data and extreme values

Understanding the spatial distribution of animals, during all their life...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.