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- 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.
Verification process:
Fault rupture (SRF) files:
- Source plot on the map
- Length-dependent hypocenter location (done)
- Different slip distribution realizations (done)
Velocity model (VM) files:
- Velocity model plot on the map (done)
- Rotation and reduction in size to minimize the ocean coverage (done)
- Generate a code to check VM files (done)
- Check if given VM parameter (folder path) exists
- .p, .s, .d and params_vel.py files must exist
- warns if model_params/coords/bounds etc don't exist using params_vel sufx in filename
- params_vel matching hh/xlen vs nx for x, y, z
- file size = nx * ny * nz * 4 bytes, checked for .p, .s, .d
- if numpy available: checks first xz slice for >0 and not NaN in .p, .s, .d
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