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Simple machine learning tool in Python (>=3.7) computing an anomaly score of seismic waveform amplitudes. By using a pre-trained Isolation forest model, the program can be used for identification of outliers in semismic data, assign robustness weights, or check instruments and metadata errors

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# <img align="left" height="30" src="https://www.gfz-potsdam.de/fileadmin/gfz/medien_kommunikation/Infothek/Mediathek/Bilder/GFZ/GFZ_Logo/GFZ-Logo_eng_RGB.svg"> Sdaas <img align="right" height="50" src="https://www.gfz-potsdam.de/fileadmin/gfz/GFZ_Wortmarke_SVG_klein_en_edit.svg">

|Jump to: | [Installation](#installation) | [Usage](#usage) |  [Maintenance](#maintenance) | [Citation](#citation) |
| - | - | - | - | - |

Simple, general and flexible tool for the identification of anomalies in 
seismic waveform amplitude, e.g.:

- recording artifacts (e.g., anomalous noise, peaks, gaps, spikes)
 - sensor problems (e.g. digitizer noise)
 - metadata field errors (e.g. wrong stage gain in StationXML)

**For any waveform analyzed, the program computes an amplitude anomaly 
score in [0, 1] representing 
the degree of belief of a waveform to be an outlier**. The score
can be used:
 - in any processing pipeline to
   - pre-filter malformed data via a user-defined threshold
   - assign robustness weights
 - as station installation / metadata checker, exploiting the scores
   temporal trends. See e.g. Channel (a) in the figure: the abrupt onset/offset of persistently high anomaly scores roughly between March and May 2018 clearly indicates an installation problem that has been fixed

![anomaly_scores_image](https://github.com/rizac/sdaas/blob/6b1dca95f4a5f931874c4aaedd278f4692dc0f96/outlierspaper-img008.png?raw=true)
Anomaly scores for four different channels (a) to (d). Each dot represents a recorded waveform segment of variable length

Notes:

This program uses a machine learning algorithm specifically designed
for outlier detection (Isolation forest) where

- scores <= 0.5 can be safely interpreted in all applications as "no significant anomaly" (see Isolation Forest original paper - Liu et al. 2008 - for theoretical details)
    
  - extreme score values are virtually impossible [by design](https://scikit-learn.org/stable/modules/calibration.html).
    This has to be considered when setting a user defined threshold T to 
    discard malformed waveforms. In many application, setting T between 0.7 and 
    0.75 has proven to be a good compromise between 
    [precision and recall (F1 score)](https://scikit-learn.org/stable/modules/model_evaluation.html#precision-recall-f-measure-metrics)

- (Disclaimer) "False positives", i.e. relatively high anomaly scores even for well formed recordings have been sometimes observed in two specific cases: 
    
      - recordings from stations with extremely and abnormaly low noise level (e.g. borhole installations)
      - recordings containing strong and close earthquakes. This is not a problem to check metadata errors, as the scores trend of several recordings from a given sensor / station will not be affected (except maybe for few sparse slightly higer scores), but has to be considered when filtering out segments in specific studies (e.g. with strong motion data): in this cases, setting a higher threshold is advisable. A model trained for accelerometers only (usually employed with these kind of recordings) is under study
   
<!--
<img align="right" width="27%" src="outlierspaper-img004.png"><img align="right"  width="29%" src="outlierspaper-img005.png">

Simple program to compute amplitude anomaly scores (in [0, 1]) of seismic 
data and metadata.
Given a set of waveforms and their metadata, it removes the waveform response
and returns the anomaly score of the waveforms amplitudes.

This program can be used to filter out a set of malformed waveforms,
assign robustness weights
or to check the correctness of the metadata fields (e.g. Station inventory xml)
by checking the anomaly score on a set of station recordings. 
-->

## Installation

Always work within a virtual environment. From a terminal, in the directory
where you cloned this repository (last argument of `git clone`),

1. create a virtual environment (once). **Be sure you use Python>=3.7 (Type `python3 --version` to check)**:

```bash
    python3 -m venv .env
    ```

2. activate it (**to be done also every time you use this program**):
    ```bash
    source .env/bin/activate
    ```
   (then to deactivate, simply type ... `deactivate` on the terminal). 
   
   Update `pip` and `setuptools` (not mandatory, but in rare cases it
   could prevent errors during the installation):
   ```
   pip install --upgrade pip setuptools
   ```

3. Install the program (one line command):

with [requirements file](https://pip.pypa.io/en/stable/user_guide/#requirements-files):
    ```
    pip install -r ./requirements.txt && pip install -e .
    ```
    
    or standard (use this mainly if you install sdaas on an already existing 
    virtualenv and you are concerned about breaking existing code):
    
    ```
    pip install "numpy>=1.15.4" && pip install -e .
    ```
    
    Notes:
    
    - The "standard" install actually checks the `setup.py` file 
      and avoids overwriting libraries already matching the required version. 
      The downside is that you might use library version that were not tested
    
    - `-e` is optional. With -e, you can update the installed program to the 
      latest release by simply issuing a `git pull`
      
    - although used to train, test and generate the underlying model,
      `scikit learn` is not required for Security & maintainability 
      limitations. If you want to install it, type 
      `pip install scikit-learn>=0.21.3` or, in the
      standard installation you can include scikit learn
      with `pip install .[dev]` instead of `pip install .`

<details>
      <summary>Reported scikit learn installation problems (click for details)</summary>
      
      Due to the specific version to be installed,
      scikit might have problems installing. 
      
      Few hints here:
      - you might need to preinstall `cython` (`pip install cython`)
      - you might need to `brew install libomp`, set the follwing env variables:
        ```
        export CC=/usr/bin/clang;export CXX=/usr/bin/clang++;export CPPFLAGS="$CPPFLAGS -Xpreprocessor -fopenmp";export CFLAGS="$CFLAGS -I/usr/local/opt/libomp/include";export CXXFLAGS="$CXXFLAGS -I/usr/local/opt/libomp/include";export LDFLAGS="$LDFLAGS -Wl,-rpath,/usr/local/opt/libomp/lib -L/usr/local/opt/libomp/lib -lomp"
        ```
      - and then install with the following flags:
        ```
        pip install --verbose --no-build-isolation "scikit-learn==0.21.3"
        ```
      
      **(For any further detail, see
      [scikit-learn installation page](https://scikit-learn.org/dev/developers/advanced_installation.html))**
      
      </details>

#### Run tests (optional)

```bash
python -m unittest -fv
```

(`-f` is optional and means: stop at first failure, `-v`: verbose)

## Usage

`sdaas` can be used as command line application or as library in your Python code

### As command line application

After activating your virtual environment (see above) you can access the program as
command line application in your terminal by typing `sdaas`. The application
can compute the score(s) of a single miniSEED file, a directory of miniSEED files, or 
a FDSN url ([dataselect or station](https://www.fdsn.org/webservices/) url).

**Please type `sdaas --help` for details and usages not covered in the examples below,
such as computing scores from locally stored files**

**Examples**

Compute scores of waveforms fetched from a FDSN URL:

```bash
>>> sdaas "http://geofon.gfz-potsdam.de/fdsnws/dataselect/1/query?net=GE&sta=EIL&cha=BH?&start=2019-01-01T00:00:00&end=2019-01-01T00:02:00" -v
[████████████████████████████████████████████████████████████████████████████████████████████████████████]100%  0d 00:00:00
id start end anomaly_score
GE.EIL..BHN 2019-01-01T00:00:09.100 2019-01-01T00:02:12.050 0.45
GE.EIL..BHE 2019-01-01T00:00:22.350 2019-01-01T00:02:00.300 0.45
GE.EIL..BHZ 2019-01-01T00:00:02.250 2019-01-01T00:02:03.550 0.45
```
*(note: The paremeter `-v` / verbose 
prints additional info before the scores table)*

Compute scores from randomly selected segments of a given station and channel,
and provide also a user-defined threshold (parameter `-th`) which will also 
append a column "class_label" (1 = outlier - assigned to the scores 
greater than the threshold and 0 = inlier)

```bash
>>> sdaas "http://geofon.gfz-potsdam.de/fdsnws/station/1/query?net=GE&sta=EIL&cha=BH?&start=2019-01-01" -v -th 0.7
[██████████████████████████████████████████████████████████]100%  0d 00:00:00
id start end anomaly_score class_label
GE.EIL..BHN 2019-01-01T00:00:09.100 2019-01-01T00:02:12.050 0.45 0
GE.EIL..BHN 2019-10-16T18:48:11.700 2019-10-16T18:51:02.300 0.83 1
GE.EIL..BHN 2020-07-31T13:37:19.299 2020-07-31T13:39:41.299 0.45 0
GE.EIL..BHN 2021-05-16T08:25:38.100 2021-05-16T08:28:03.150 0.53 0
GE.EIL..BHN 2022-03-01T03:14:23.300 2022-03-01T03:17:01.750 0.45 0
GE.EIL..BHE 2019-01-01T00:00:22.350 2019-01-01T00:02:00.300 0.45 0
GE.EIL..BHE 2019-10-16T18:48:18.050 2019-10-16T18:51:09.650 0.83 1
GE.EIL..BHE 2020-07-31T13:37:08.599 2020-07-31T13:39:28.499 0.45 0
GE.EIL..BHE 2021-05-16T08:25:49.150 2021-05-16T08:28:14.800 0.49 0
GE.EIL..BHE 2022-03-01T03:14:26.050 2022-03-01T03:16:41.900 0.45 0
GE.EIL..BHZ 2019-01-01T00:00:02.250 2019-01-01T00:02:03.550 0.45 0
GE.EIL..BHZ 2019-10-16T18:48:24.800 2019-10-16T18:50:47.300 0.45 0
GE.EIL..BHZ 2020-07-31T13:37:08.249 2020-07-31T13:39:30.199 0.45 0
GE.EIL..BHZ 2021-05-16T08:25:47.250 2021-05-16T08:28:10.850 0.47 0
GE.EIL..BHZ 2022-03-01T03:14:40.800 2022-03-01T03:16:53.900 0.45 0
```

Compute scores from randomly selected segments of a given station and channel,
but aggregating scores per channel and returning their median:

```bash
>>> sdaas "http://geofon.gfz-potsdam.de/fdsnws/station/1/query?net=GE&sta=EIL&cha=BH?&start=2019-01-01" -v -agg median
[██████████████████████████████████████████████████████████]100%  0d 00:00:00
id start end median_anomaly_score
GE.EIL..BHN 2019-01-01T00:00:09.100 2022-03-01T03:17:45.950 0.46
GE.EIL..BHE 2019-01-01T00:00:22.350 2022-03-01T03:17:48.700 0.45
GE.EIL..BHZ 2019-01-01T00:00:02.250 2022-03-01T03:17:39.300 0.45
```

Same as above, but save the scores table to CSV via the parameter `-sep` and
`>` (normal redirection of the standard output `stdout` to file)

```bash
>>> sdaas "http://geofon.gfz-potsdam.de/fdsnws/station/1/query?net=GE&sta=EIL&cha=BH?&start=2019-01-01" -v -sep "," > /path/to/myfile.csv
[████████████████████████████████████████████████████████████]100%  0d 00:00:00
```
*in this case, providing `-v` / verbose will also redirect the header row to
CSV. Note that only the scores table is output to `stdout`, everything else is 
printed to `stderr` and thus should still be visible on the terminal, as in the 
example above*

### As library in your Python code

This software can also be used as library in Python code (e.g. Jupyter Notebook)
to work with [ObsPy](https://docs.obspy.org/) objects (ObsPy is already included 
in the installation): assuming you have one or more 
[Stream](https://docs.obspy.org/packages/autogen/obspy.core.stream.Stream.html)
or [Trace](https://docs.obspy.org/packages/autogen/obspy.core.trace.Trace.html),
with relative [Inventory](https://docs.obspy.org/packages/obspy.core.inventory.html), 
then

Compute the scores in a stream or iterable of traces (e.g. list. tuple, generator),
returning the score of each Trace:

```python
from obspy.core.inventory.inventory import read_inventory
from obspy.core.stream import read

from sdaas.core import traces_scores

# Load a Stream object and its inventory
# (use as example the test directory of the package):
stream = read('./tests/data/GE.FLT1..HH?.mseed')
inventory = read_inventory('./tests/data/GE.FLT1.xml')

# Compute the Stream anomaly score (3 scores, one for each Trace):
output = traces_scores(stream, inventory)
```
Then `output` is:
```
[0.45729656,  0.45199387,  0.45113142]
```

Compute the scores in a stream or iterable of traces, getting also the traces id (by 
default the tuple `(seed_id, start, end)`, where seed_id is the 
[Trace SEED identifier](https://docs.obspy.org/packages/autogen/obspy.core.trace.Trace.get_id.html)):

```python
from obspy.core.inventory.inventory import read_inventory
from obspy.core.stream import read

from sdaas.core import traces_idscores

# Load a Stream object and its inventory
# (use as example the test directory of the package):
stream = read('./tests/data/GE.FLT1..HH?.mseed')
inventory = read_inventory('./tests/data/GE.FLT1.xml')

# Compute the Stream anomaly score:
output = traces_idscores(stream, inventory)
```
Then `output` is:
```
([('GE.FLT1..HHE', datetime.datetime(2011, 9, 3, 16, 38, 5, 550001), datetime.datetime(2011, 9, 3, 16, 42, 12, 50001)), ('GE.FLT1..HHN', datetime.datetime(2011, 9, 3, 16, 38, 5, 760000), datetime.datetime(2011, 9, 3, 16, 42, 9, 670000)), ('GE.FLT1..HHZ', datetime.datetime(2011, 9, 3, 16, 38, 8, 40000), datetime.datetime(2011, 9, 3, 16, 42, 9, 670000))], array([ 0.45729656,  0.45199387,  0.45113142]))
```

Same as above, with custom traces id (their SEED identifier only):

```python
from obspy.core.inventory.inventory import read_inventory
from obspy.core.stream import read

from sdaas.core import traces_idscores

# Load a Stream object and its inventory
# (use as example the test directory of the package):
stream = read('./tests/data/GE.FLT1..HH?.mseed')
inventory = read_inventory('./tests/data/GE.FLT1.xml')

# Compute the Stream anomaly score:
output = traces_idscores(stream, inventory, idfunc=lambda t: t.get_id())
```
Then `output` is:
```
(['GE.FLT1..HHE', 'GE.FLT1..HHN', 'GE.FLT1..HHZ'], array([ 0.45729656,  0.45199387,  0.45113142]))
```

You can also compute scores and ids from iterables of streams (e.g., when 
reading from files)...

```python
from sdaas.core import streams_scores
from sdaas.core import streams_idscores
```

... or from a single trace:

```python
from sdaas.core import trace_score
```

For instance, to compute the anomaly score of several streams
(for each stream and for each trace therein, return the trace anomaly score):

```python
from obspy.core.inventory.inventory import read_inventory
from obspy.core.stream import read

from sdaas.core import streams_scores

# Load a Stream objects and its inventory
# (use as example the test directory of the package
# and mock a list of streams by loading twice the same Stream):
streams = [read('./tests/data/GE.FLT1..HH?.mseed'),
           read('./tests/data/GE.FLT1..HH?.mseed')]
inventory = read_inventory('./tests/data/GE.FLT1.xml')

# Compute Streams scores:
output = streams_scores(streams, inventory)
```
Then `output` is:
```
[ 0.45729656  0.45199387  0.45113142  0.45729656  0.45199387  0.45113142]
```

Same as above, computing the features and the scores separately for more 
control:

```python
from obspy.core.inventory.inventory import read_inventory
from obspy.core.stream import read

from sdaas.core import trace_features, aa_scores

# Load a Stream object and its inventory
# (use as example the test directory of the package
# and mock a list of streams by loading twice the same Stream):
streams = [read('./tests/data/GE.FLT1..HH?.mseed'),
           read('./tests/data/GE.FLT1..HH?.mseed')]
inventory = read_inventory('./tests/data/GE.FLT1.xml')

# Compute Streams scores:
feats = []
for stream in streams:
    for trace in stream:
        feats.append(trace_features(trace, inventory))
output = aa_scores(feats)
```
Then `output` is:
```
[0.45729656, 0.45199387, 0.45113142, 0.45729656, 0.45199387, 0.45113142]
```

## Maintenance

Although scikit learn is not used anymore for
[maintainability limitations](https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations), 
you can always consult the [README](./sdaas/core/models) 
explaining how to manage create new scikit-learn models.

## Citation

**Software:**

> Zaccarelli, Riccardo (2022). sdas - a Python tool computing an amplitude anomaly score of seismic data and metadata using simple machine‐Learning models. GFZ Data Services. https://doi.org/10.5880/GFZ.2.6.2023.009

**Research article:**

> Riccardo Zaccarelli, Dino Bindi, Angelo Strollo; Anomaly Detection in Seismic Data–Metadata Using Simple Machine‐Learning Models. Seismological Research Letters 2021; 92 (4): 2627–2639. doi: https://doi.org/10.1785/0220200339

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