This
...
Note that his page is for internal sharing purposes only, it is likely inaccurate and out-of-date, and therefore it is advised to conduct researchers directly if you want more reliable information
...
3 hypo and 2 slip dist per source
...
automated based on PGV>5cm/s; 15kmRup, 5km land cutoff
...
19,604
(virtual + Geonet stations)
...
First implementation;
Focus on running workflow and comparison with empirically-derived hazard curves
...
hypo every 20 km along strike,
3 slip dist per source
...
19,604
(virtual + Geonet stations)
...
v18.5
...
as for v17.9
...
19,604
(virtual + Geonet stations)
...
...
is the front page for the Cybershake NZ project, describing the corresponding computational and scientific components.
An outline of the project, a table to compare the main features of different versions, and ideas for future implementations are presented here: Ouline
A step-by-step manual to run a version of Cybeshake is here: Manual
Other contents will be added until 5th Dec 2018......
...
v18p6
...
Magnitude-dependant number of realizations
one slip distribution per hypocentre
...
Pgv threshold of 2 cm/s
...
as for v17.9
...
27,481
(virtual + Geonet stations)
...
- Monte Carlo hypocentre realizations
- Variation in hypocentre location along the strike and dip directions
...
To do list:
Refer to this sub-page for the list of to-do items for Cyberhshake:
Ideas for future implementations (no particular order):
- Bootstrap sampling to understand how many ruptures are needed for a given source
- Source uncertainties (currently slip and hypo; but need to add uncertainty in G&P parametrization).
- Velocity model uncertainties (random pertubations).
- Explicit modelling of subduction zone sources in Cybershake
- Neural Net for GMM trained with CS and validation results in order to use for distributed seismicity
- New velocity model (i.e. with more basins)
- Velocity model with tomographic refinement
- Velocity model with site-specific 1D for HF method
- Logic tree for hazard to consider different ground motion models (both empirical and simulated). Weights for models are determined based on a neural net fit to the data in which all models start with uniform weight and the weights are then determined as a function of site location, magnitude, source to site distance etc. Location component can be part of a convNet.
- Ongoing improvements to the simulation code (topo, plasticity etc)
- Paper which shows the theoretical benefits of forward simulation and domain optimization in terms of minimum total computation vs. recriprocity.
- Consider other ERFs (i.e. not just Stirling et al 2012); UCERF3 method applied to NZ; RSQSim applied to NZ.
- Extraction of deagg, and gm selection for a conditional IM hazard/im value.