Events detection

This tutorial shows how to detect and event.

STA/LTA ratio

# specific das_ice function
import das_ice.io as di_io
import das_ice.signal.filter as di_filter
import das_ice.signal.picker as di_picker
import das_ice.processes as di_p
import das_ice.plot as di_plt

# classic librairy
import xarray as xr
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from tqdm import trange
%matplotlib inline

from dask.distributed import Client,LocalCluster

cluster = LocalCluster(n_workers=8)
client = Client(cluster)
client.dashboard_link

'http://127.0.0.1:8787/status'

Loading data

ds=xr.Dataset()
ds['velocity']=di_io.dask_Terra15('*.hdf5', chunks="auto")

d_optic_bottom=2505 #m
depth_bh=97 #m

ds['distance']=-1*(np.abs(ds.distance-d_optic_bottom)-depth_bh)
ds = ds.sortby('distance')

dvel_decimated=di_filter.decimate(ds.velocity,9)

dvel_decimated

<xarray.DataArray 'velocity' (time: 137414, distance: 618)> Size: 340MB
dask.array<getitem, shape=(137414, 618), dtype=float32, chunksize=(6012, 618), chunktype=numpy.ndarray>
Coordinates:
  * distance  (distance) float64 5kB -1.733e+03 -1.729e+03 ... 93.72 96.19
  * time      (time) datetime64[ns] 1MB 2024-08-29T05:04:48.597790 ... 2024-0...
Attributes:
    client_fn_applied:
    dT:                  0.000147915811144255
    dx:                  4.083809535485629
    frame_shape:         [1082  618]
    nT:                  1082
    nx:                  618
    recorder_id:         T311542
    trigger_start_time:  -1.0

xarray.DataArray

'velocity'

• time: 137414
• distance: 618

• dask.array<chunksize=(6012, 618), meta=np.ndarray>

  Array Chunk Bytes 323.95 MiB 14.17 MiB Shape (137414, 618) (6012, 618) Dask graph 23 chunks in 7 graph layers Data type float32 numpy.ndarray

• Coordinates: (2)
  • distance
    (distance)
    float64
    -1.733e+03 -1.729e+03 ... 96.19

    array([-1733.354665, -1729.270855, -1725.187046, ...,    92.108198,
              93.724183,    96.192007])

  • time
    (time)
    datetime64[ns]
    2024-08-29T05:04:48.597790 ... 2...

    array(['2024-08-29T05:04:48.597790000', '2024-08-29T05:04:48.599121242',
           '2024-08-29T05:04:48.600452484', ..., '2024-08-29T05:07:51.525125188',
           '2024-08-29T05:07:51.526456431', '2024-08-29T05:07:51.527787673'],
          dtype='datetime64[ns]')

• Indexes: (2)
  • distance
    PandasIndex

    PandasIndex(Index([ -1733.354664737774, -1729.2708552022884,  -1725.187045666803,
           -1721.1032361313173, -1717.0194265958316,  -1712.935617060346,
           -1708.8518075248603, -1704.7679979893746,  -1700.684188453889,
           -1696.6003789184033,
           ...
             77.38894516278378,   79.85676901784518,   81.47275469826945,
             83.94057855333085,   85.55656423375513,   88.02438808881652,
              89.6403737692408,   92.10819762430219,   93.72418330472647,
             96.19200715978786],
          dtype='float64', name='distance', length=618))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex([   '2024-08-29 05:04:48.597790',
                   '2024-08-29 05:04:48.599121242',
                   '2024-08-29 05:04:48.600452484',
                   '2024-08-29 05:04:48.601783726',
                   '2024-08-29 05:04:48.603114969',
                   '2024-08-29 05:04:48.604446211',
                   '2024-08-29 05:04:48.605777453',
                   '2024-08-29 05:04:48.607108696',
                   '2024-08-29 05:04:48.608439938',
                   '2024-08-29 05:04:48.609771180',
                   ...
                   '2024-08-29 05:07:51.515806492',
                   '2024-08-29 05:07:51.517137735',
                   '2024-08-29 05:07:51.518468977',
                   '2024-08-29 05:07:51.519800219',
                   '2024-08-29 05:07:51.521131462',
                   '2024-08-29 05:07:51.522462704',
                   '2024-08-29 05:07:51.523793946',
                   '2024-08-29 05:07:51.525125188',
                   '2024-08-29 05:07:51.526456431',
                   '2024-08-29 05:07:51.527787673'],
                  dtype='datetime64[ns]', name='time', length=137414, freq=None))

• Attributes: (8)

  client_fn_applied :

  dT :

  0.000147915811144255

  dx :

  4.083809535485629

  frame_shape :

  [1082 618]

  nT :

  1082

  nx :

  618

  recorder_id :

  T311542

  trigger_start_time :

  -1.0

dsf=di_filter.bandpass(dvel_decimated,100,200)

STA/LTA

start_date = pd.to_datetime('2024-08-29T05:05:30.00')
end_date = pd.to_datetime('2024-08-29T05:06:30.00')
dsf_crop=dsf.sel(time=slice(start_date,end_date))

len_sta=0.2 #second
len_lta=2 #second
sta_lta=di_picker.sta_lta(dsf_crop,len_sta,len_lta)

Find event in sta/lta signal

event=di_picker.find_event(sta_lta,10,5)

Show STA/LTA event pick

id=10
fig=di_plt.viz_two_time_series(dsf_crop[:,id],event[:,id])

start_date_fig = pd.to_datetime('2024-08-29T05:05:33.80')
end_date_fig = pd.to_datetime('2024-08-29T05:05:34.30')

# Update the x-axis range
fig.update_xaxes(range=[start_date_fig, end_date_fig])
fig.show("png")