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| Titel |
Earthquake and failure forecasting in real-time: A Forecasting Model Testing Centre |
| VerfasserIn |
Rosa Filgueira, Malcolm Atkinson, Andrew Bell, Ian Main, Steven Boon, Philip Meredith |
| Konferenz |
EGU General Assembly 2013
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 15 (2013) |
| Datensatznummer |
250072293
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| Zusammenfassung |
| Across Europe there are a large number of rock deformation laboratories, each of which runs
many experiments. Similarly there are a large number of theoretical rock physicists who
develop constitutive and computational models both for rock deformation and changes in
geophysical properties. Here we consider how to open up opportunities for sharing
experimental data in a way that is integrated with multiple hypothesis testing. We present a
prototype for a new forecasting model testing centre based on e-infrastructures
for capturing and sharing data and models to accelerate the Rock Physicist (RP)
research.
This proposal is triggered by our work on data assimilation in the NERC EFFORT
(Earthquake and Failure Forecasting in Real Time) project, using data provided by the NERC
CREEP 2 experimental project as a test case. EFFORT is a multi-disciplinary collaboration
between Geoscientists, Rock Physicists and Computer Scientist. Brittle failure of the crust is
likely to play a key role in controlling the timing of a range of geophysical hazards, such as
volcanic eruptions, yet the predictability of brittle failure is unknown. Our aim is to
provide a facility for developing and testing models to forecast brittle failure in
experimental and natural data. Model testing is performed in real-time, verifiably
prospective mode, in order to avoid selection biases that are possible in retrospective
analyses.
The project will ultimately quantify the predictability of brittle failure, and how this
predictability scales from simple, controlled laboratory conditions to the complex,
uncontrolled real world. Experimental data are collected from controlled laboratory
experiments which includes data from the UCL Laboratory and from Creep2 project which
will undertake experiments in a deep-sea laboratory. We illustrate the properties of the
prototype testing centre by streaming and analysing realistically noisy synthetic
data, as an aid to generating and improving testing methodologies in imperfect
conditions. The forecasting model testing centre uses a repository to hold all the
data and models and a catalogue to hold all the corresponding metadata. It allows
to:
Data transfer:
Upload experimental data: We have developed FAST (Flexible
Automated Streaming Transfer) tool to upload data from RP laboratories
to the repository. FAST sets up data transfer requirements and selects
automatically the transfer protocol. Metadata are automatically created and
stored.
Web data access:
Create synthetic data: Users can choose a generator and supply
parameters. Synthetic data are automatically stored with corresponding
metadata.
Select data and models: Search the metadata using criteria design
for RP. The metadata of each data (synthetic or from laboratory) and
models are well-described through their respective catalogues accessible
by the web portal.
Upload models: Upload and store a model with associated metadata.
This provide an opportunity to share models. The web portal solicits and
creates metadata describing each model.
Run model and visualise results: Selected data and a model
to be submitted to a High Performance Computational resource hiding
technical details. Results are displayed in accelerated time and stored
allowing retrieval, inspection and aggregation.
The forecasting model testing centre proposed could be integrated into EPOS. Its expected
benefits are:
Improved the understanding of brittle failure prediction and its scalability to
natural phenomena.
Accelerated and extensive testing and rapid sharing of insights.
Increased impact and visibility of RP and GeoScience research.
Resources for education and training.
A key challenge is to agree the framework for sharing RP data and models. Our work is
provocative first step. |
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