Stochastic Kinematically Informed Earthquake Sequences
Kinematic / statistical earthquake sequence generation
skies is a python-based package designed to model three-dimensional earthquake sequences in time across geometrically complex fault systems. It's the code advertised by the paper, "Meade, B. J., (2024) A kinematic method for generating earthquake sequences, Computers and Geosciences"
Getting started
To set up a development conda environment, run the following commands in the skies folder.
conda config --prepend channels conda-forge
conda env create
conda activate skies
pip install --no-use-pep517 -e .A model run can be started with something like:
python ./../skies/skies_generate_sequence.py ./data/cascadia_params.json --n_time_steps 100000 --omori_rate_perturbation_exponent 2.0 --repl 1Input parameters:
| parameter name | example value | description |
|---|---|---|
area_scaling |
1.25 | Factor that expands rupture area from empirical rupture area |
b_value |
-1 | Gutenberg-Ricter $b$-value |
default_omori_decay_time |
10 | Controls length of Omori decay following earthquakes (smaller is longer Omori decay time) |
geometric_moment_rate_scale_factor |
1 | Scales rates of moment accumulation. Should always be 1 except for debugging |
initial_mesh_slip_deficit_scaling |
0 | UNSURE |
initial_slip_deficit_rate_file |
"rates.npy" | Geometric moment accumulation rates on a single mesh. This is just a single numpy array with one geometric moment rate component, generally dip-slip or strike-slip |
location_probability_amplitude_scale_factor |
1 | Location probability amplitude scale factor (leading coefficient in front of $\tanh$ for location probability, $\gamma^h_a$ in the paper) |
location_probability_data_scale_factor |
0.00001 | Location probability amplitude scale factor (coefficient that multiplies arguments to $\tanh$ for location probability, $\gamma^h_d$ in the paper) |
max_latitude |
52 | Plotting |
max_longitude |
231 | Plotting |
maximum_event_moment_magnitude |
9.5 | Maximum event magnitude (could also be limited by total mesh area) |
mesh_index |
0 | If the mesh_parameters_file_name file points to more than one mesh select the one specified by the index here |
mesh_parameters_file_name |
"mesh_parameters.json" | celeri style mesh parameters file |
min_contour_value |
0.1 | Plotting |
min_latitude |
38 | Plotting |
min_longitude |
239 | Plotting |
minimum_event_moment_magnitude |
5.0 | Minimum event magnitude (could also be limited by minimum mesh element area) |
minimum_probability |
1e-10 | Minimum event probability in time |
n_contour_levels |
10 | Plotting |
n_events_omori_history_effect |
100 | Number of more recent events that contribute to cumulative Omori effect (UNUSED?) |
n_grid_latitude |
500 | Plotting |
n_grid_longitude |
500 | Plotting |
n_time_steps |
1000000 | Number of time steps (not real time) |
omori_amplitude_scale_factor |
1e-8 | Omori numerator amplitude, $\beta_j$ in the paper |
omori_rate_perturbation_scale_factor |
1e-1 | Divides Omori time difference, $\tau_j$ in the paper (UNSURE) |
omori_rate_perturbation_exponent |
1.0 | Omori time difference exponent, $p_j$ in the paper |
plot_events_in_loop |
False | Plotting |
shear_modulus |
3e10 | Shear modulus |
time_probability_amplitude_scale_factor |
0.15 | Time probability amplitude scale factor (leading coefficient in front of $\tanh$ for time probability), $\gamma^t_a$ in the paper |
time_probability_data_scale_factor |
1e-12 | Time probability amplitude scale factor (coefficient that multiplies arguments to $\tanh$ for time probability), $\gamma^t_d$ in the paper |
time_probability_history_scale_factor |
1e12 | UNSURE |
time_step |
5e-5 | Time step duration (not real time) |
write_event_pickle_files |
0 | Write a pickle file for each earthquake |
repl |
0 | Drop into iPython REPL at end of run |
base_runs_folder |
"./runs" | Base output folder |
geometric_moment_nucleation_probability |
"low" | Should earthquake nucleate in regions of "high" or "low" geometric moment (NEED TO REVISE) |
Parameters that most directly control model
- Time probability equation:
- $\gamma_a^t$:
time_probability_amplitude_scale_factor - $\gamma_d^t$:
time_probability_data_scale_factor
- $\gamma_a^t$:
$$ p^t = \gamma_a^t \tanh \left( \gamma_d^t \left[r^\mathrm{a} + \sum\nolimits_j^{n(t_j \leq t)} { r^\mathrm{o} + r^\mathrm{r} } + \mathcal{A} \right] \right). $$
- Location probability equation:
- $\gamma_a^h$:
location_probability_amplitude_scale_factor - $\gamma_d^h$:
location_probability_data_scale_factor
- $\gamma_a^h$:
$$ p_i^h(t_k) = \gamma_a^h \tanh ( \gamma_d^h \left[m^\mathrm{a}_i - m^\mathrm{r}_i\right] ) $$
- Omori time decay equation:
- $\beta$:
omori_amplitude_scale_factor - $p$: Currently set to 1
- $\tau$:
default_omori_decay_time
- $\beta$:
$$ r^\mathrm{o}_j(t) = \frac{\beta_j}{1 + \frac{(t - t_j)^{p_j}}{\tau_j}} $$
- Moment reduction following slip events
- $\omega'$:
time_probability_history_scale_factor - $\beta'$:
omori_rate_perturbation_scale_factor - $\psi'$:
omori_rate_perturbation_exponent
- $\omega'$:
$$ r^\mathrm{r} = -\omega' \beta' \left[ \sum\nolimits_i m_i(t_j) \right] ^{\psi'} $$
Folder structure and file locations for applications
We assume that a project is arranged using the following folder structure:
project_name/
|
├── data/
| ├── mesh_parameters.json
│ ├── mesh_001.msh
│ ├── mesh_002.msh
│ └── mesh_NNN.msh
|
└── runs/
└── 2022_12_11_20_38_21/
├── 2022_12_11_20_38_21.hdf
├── probability_magnitude.png
├── probability_magnitude.pdf
├── random_state.pickle
├── mesh.pickle
├── time_series.pickle
├── initial_mesh_data.png
├── initial_mesh_data.pdf
├── 2022_12_11_20_38_21_mesh_geometry.vtk
├── params.json
├── 2022_12_11_20_38_21.log
├── model_segment.csv
├── model_block.csv
└── model_station.csv