Frequently asked questions

Below you find a list for known errors, that may occur and how to work around:

1. Sample covariance contains Nan or Inf

The variations in the likelihood space are too large. When normalising the space this may result in inf or Nan. Please reduce the potential search interval for hyper parameters “h_*”, i.e. reduce and/or increase the upper and/or lower bounds in the project config_*.yaml, respectively.

2. Pytensor MKL support

RuntimeError: To use MKL 2018 with Pytensor you MUST set “MKL_THREADING_LAYER=GNU” in your environment.

add
export MKL_THREADING_LAYER=GNU
to your .bashrc

3. Slow compilation

No error will be thrown, but during “beat sample” the compilation of the forward model function ḿay take a long time. In such a case the default compilation flags of Pytensor may be overwritten. This may result in longer runtime.:

PYTENSOR_FLAGS=optimizer=fast_compile beat sample Projectpath
4. MPI rank always 0

ValueError: Specified more workers that sample in the posterior “8”, than there are total number of workers “0”

There is an inconsistency in the MPI executable that is used for starting the sampler and the library that was used to compile mpi4py.
Please make sure to use the same MPI library. One fix would be to remove all installations of MPI from the machine and install a fresh version.
5. GF store error BAD_REQUEST

This means that the geodetic (static) Green’s Function store does not cover the full depth-distance range of the source- receiver pairs. Please make sure that these ranges are wide enough in depths and distances!

6. pyrocko.gf.meta.OutOfBounds: out of bounds

This means that the seismic Green’s Function store does not cover the full depth-distance range of the source- receiver pairs. Please make sure that these ranges are wide enough in depths and distances! It also may occur when waveform-arrivals have been requested through the configuration at a time window where synthetics haven’t been calculated. E.g. S-waves ‘any_S’ are configured, but the GF store has not been computed including them.

7. Upgrading BEAT from beta

Unfortunately, some incompatibility arose from beta to version 1.0. Finite fault projects that have been sampled and created using the beta version will need to undergo some manual changes by the user to be loadable under version 1.0.

  1. Rename config_ffo.yaml to config_ffi.yaml

  2. Rename “$project_folder/ffo” to “$project_folder/ffi”

  3. To update the config file run:

    beat update $project_folder --mode=ffi  # (--diff to display updates first)

  4. According to the $datatypes that were included in the project, recreate the fault geometry with:

    beat build_gfs $project_folder --mode=ffi --force --datatypes=$datatypes
8. Cannot connect to display while working remotely

X forwarding needs to be activated in the ssh config! For linux:

  1. vi /etc/ssh/ssh_config

  2. set “X11Forwarding” to “yes”, save and close

  3. in the shell run:

    systemctl restart sshd   # could require sudo rights
9. HDF5surrogate escape

NameError: global name ‘exc’ is not defined

add:
export LC_ALL=en_AU.utf8
to your .bashrc
10. How can I load the sampling data?

Please use the following script and update your paths and the variable names that you like to have loaded.:

from beat import backend
from os import path as op

varnames=['strike', 'dip', 'rake']   # put any variable name there (naming needs to be identical as in for example stage_-1.pdf plot)
projectdir = '/path/to/project'      # path to your BEAT project

# this assumes you have run beat export first
mtrace = backend.load_multitrace(
    dirname=op.join(projectdir, 'ffi', 'results'),
    chains=[-1], varnames=varnames, backend='bin')

sampling_variables = {}
for varname in varnames:
    sampling_variables[varname] = mtrace.get_values(
        varname, combine=True, squeeze=True)

# this will give you a dictionary with numpy arrays with the variable name as keys
# you can plot these than using familiar plotting libraries like matplotlib or seaborn