Commit fff3f52a authored by Sebastian Heimann's avatar Sebastian Heimann
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Update README.md

parent fb7cd11f
# Grond
# Grond has moved to a new home
A bootstrap-based probabilistic battering ram to explore solution spaces in
earthquake source parameter estimation problems.
## Installation
First, install [Pyrocko](http://pyrocko.org/docs/current/install/),
then install Grond:
```bash
git clone https://gitext.gfz-potsdam.de/heimann/grond.git
cd grond
sudo python setup.py install
```
## Updating an existing installation
```bash
cd grond # change to the directory to where you cloned grond initially
git pull origin master
sudo python setup.py install
```
## Basic setup of data
To use grond with your data we suggest the following folder stucture:
```
data
└── events
├── laquila2009
│   ├── event.txt
│   ├── insar
│   │   ├── dsc_LAquila2009_Envisat.npz
│   │   └── dsc_LAquila2009_Envisat.yml
│   └── waveforms
│   ├── raw
│   │   └── 2009-04-06_MW6.3_Central_Italy.421793.seed
│   └── stations.xml
```
## Basic usage
Grond can be run as a command line tool or by calling Grond's library functions
from a Python script. To get a brief description on available options of
Grond's command line tool, run `grond --help` or `grond <subcommand> --help`.
Once dataset and configuration are ready, the command
`grond go <configfile> <eventname>` starts the optimization algorithm for a
selected event. Before running the optimization, to debug problems with the
dataset and configuration, use `grond check <configfile> <eventname>`. To get a
list of event names available in a configured setup, run
`grond events <configfile>`. During the optimization, results are aggregated in
a directory, referred to in the configuration as `<rundir>`. To visualize the
results run `grond plot <plotnames> <rundir>`. The results can be exported in
various ways by running the subcommand `grond export <what> <rundir>`. Finally,
you may run `grond report <rundir>` to aggregate results to a browsable
summary, (by default) under the directory `reports`.
## Example configuration file
```yaml
%YAML 1.1
--- !grond.Config
# Path, where to store output (run-directories)
rundir_template: 'gruns/${problem_name}.run'
# -----------------------------------------------------------------------------
# Configuration section for dataset (input data)
# -----------------------------------------------------------------------------
dataset_config: !grond.DatasetConfig
# List of files with station coordinates
stations_stationxml_paths:
- 'events/${event_name}/responses-geofon.stationxml'
- 'events/${event_name}/responses-iris.stationxml'
# File with hypocenter information and possibly reference solution
events_path: 'events/${event_name}/prepared/event.txt'
# List of directories with raw waveform data
waveform_paths:
- 'events/${event_name}/raw'
# List of files with instrument response information
responses_stationxml_paths:
- 'events/${event_name}/responses-geofon.stationxml'
- 'events/${event_name}/responses-iris.stationxml'
# List with station/channel codes to exclude
#blacklist: ['STA','NET.STA', 'NET.STA.LOC', 'NET.STA.LOC.CHA']
# Same but using a file, one exclusion entry per line
blacklist_paths:
- 'events/${event_name}/blacklist.txt'
# Make available picks for forced trace alignment, file must be in Pyrocko's
# marker file format
#picks_paths: ['events/${event_name}/picks.markers']
# -----------------------------------------------------------------------------
# Configuration section for synthetic seismogram engine (configures where
# to look for GF stores)
# -----------------------------------------------------------------------------
engine_config: !grond.EngineConfig
# Whether to use GF store directories listed in ~/.pyrocko/config.pf
gf_stores_from_pyrocko_config: false
# List of directories with GF stores
gf_store_superdirs:
- 'gf_stores'
# -----------------------------------------------------------------------------
# Configuration section selecting data to be included in the data fitting
# procedure. This defines the objective function to be minimized in the
# optimization procedure. It can be composed of one or more contributions, each
# represented by a !grond.TargetConfig section.
# -----------------------------------------------------------------------------
target_groups:
- !grond.WaveformTargetGroup
# misfits are normalized within each normalization_family separately
normalization_family: 'td'
# Name of the group to which this contribution belongs
path: 'td.rayleigh'
# Minimum distance of stations to be considered
distance_min: 0e3
# Maximum distance of stations to be considered
distance_max: 1000e3
# List with names of channels to be considered
channels: ['Z']
# How to weight stations from this contribution in the global misfit
weight: 1.0
# Subsection on how to fit the traces
misfit_config: !grond.WaveformMisfitConfig
# Frequency band [Hz] of acausal filter (flat part of frequency taper)
fmin: 0.01
fmax: 0.05
# Factor defining fall-off of frequency taper
# (zero at fmin/ffactor, fmax*ffactor)
ffactor: 1.5
# Time window to include in the data fitting. Times can be defined offset
# to given phase arrivals. E.g. '{stored:begin}-100' would mean 100 s
# before arrival of the phase named 'begin', which must be defined in the
# travel time tables in the GF store.
tmin: '{stored:anyP_no_Pdiff}'
tmax: '{vel_surface:2.5}'
# Align traces by picks (will lose some control on origin time and
# location). Define the synthetic phasename, for which a travel time table
# must be available in the GF store,
#pick_synthetic_traveltime: 'anyP_no_Pdiff'
# and the name of the picks to use in the picks file (defined in
# dataset_config)
#pick_phasename: 'P'
# How to fit the data (available choices: 'time_domain',
# 'frequency_domain', 'absolute', 'envelope', 'cc_max_norm')
domain: 'time_domain'
# allow for some time-shifting of individual traces, maximum shift [s]
tautoshift_max: 4.0
# whether to penalise time-shifting (0.0 for no penalty)
autoshift_penalty_max: 0.0
# exponent of the norm used when comparing traces, 1 or 2
norm_exponent: 1
# How to interpolate the Green's functions (available choices:
# 'nearest_neighbor', 'multilinear'). Note that the GFs have to be densely
# sampled when using interpolation other than nearest_neighbor.
interpolation: 'nearest_neighbor'
# Name of GF store to use
store_id: 'global_2s'
# A second contribution to the misfit function (for descriptions, see above)
- !grond.WaveformTargetGroup
normalisation_family: 'td'
path: 'td.love'
distance_min: 0e3
distance_max: 1000e3
channels: [T]
weight: 1.0
#limit: 20
misfit_config: !grond.WaveformMisfitConfig
fmin: 0.01
fmax: 0.05
ffactor: 1.5
tmin: 'stored:anyP_no_Pdiff'
tmax: 'vel_surface:2.5'
domain: time_domain
tautoshift_max: 4.0
autoshift_penalty_max: 0.0
norm_exponent: 1
interpolation: nearest_neighbor
store_id: 'global_2s'
# -----------------------------------------------------------------------------
# Definition of the problem to be solved - source model, parameter space, and
# global misfit configuration settings.
# -----------------------------------------------------------------------------
problem_config: !grond.CMTProblemConfig
# Name used when creating output directory
name_template: 'timedomain_${event_name}'
# Definition of model parameter space to be searched in the optimization
ranges:
# Time relative to hypocenter origin time [s]
time: '-10 .. 10 | add'
# Centroid location with respect to hypocenter origin [m]
north_shift: '-40e3 .. 40e3'
east_shift: '-40e3 .. 40e3'
depth: '0 .. 50e3'
# Range of magnitudes to allow
magnitude: '4.0 .. 7.0'
# Relative moment tensor component ranges (don't touch)
rmnn: '-1.41421 .. 1.41421'
rmee: '-1.41421 .. 1.41421'
rmdd: '-1.41421 .. 1.41421'
rmne: '-1 .. 1'
rmnd: '-1 .. 1'
rmed: '-1 .. 1'
# Source duration range [s]
duration: '0. .. 0.'
# Clearance distance around stations (no models with origin closer than this
# distance to any station are produced by the sampler)
distance_min: 0.
# Type of moment tensor to restrict to (choices: 'full', 'deviatoric')
mt_type: 'deviatoric'
# Whether to apply automatic weighting to balance the effects of geometric
# spreading etc.
apply_balancing_weights: true
# Under what norm to combine targets into the global misfit
# (exponent of norm, 1 or 2)
norm_exponent: 1
# -----------------------------------------------------------------------------
# Configuration of the optimization procedure. The following example setup will
# run a Bayesian bootstrap optimization (BABO).
# -----------------------------------------------------------------------------
optimizer_config: !grond.HighScoreOptimizerConfig
# Number of bootstrap realizations to be tracked simultaneously in the
# optimization
nbootstrap: 100
sampler_phases:
- !grond.UniformSamplerPhase
# Number of iterations to operate in 'uniform' phase
niterations: 1000
- !grond.DirectedSamplerPhase
# Number of iterations to operate in 'directed' phase
niterations: 10000
# Multiplicator for width of sampler distribution to start with
scatter_scale_begin: 2.0
# Multiplicator for width of sampler distribution at end of this phase
scatter_scale_end: 0.5
# -----------------------------------------------------------------------------
# Configuration of pre-optimization analysis phase. E.g. balancing weights are
# determined during this phase.
# -----------------------------------------------------------------------------
analyser_config: !grond.AnalyserConfig
# Number of iterations (number of models to forward model in the analysis,
# larger number -> better statistics)
niterations: 1000
```
* [Documentation](https://pyrocko.org/grond/)
* [Repository](https://github.com/pyrocko/grond/)
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