seisscan.do_ls

seisscan.do_ls(st, channel, subnetworks=[], w=1.0, dt=0.1, max_lag=0.1, pos='end', method=0, dask_client=None)

Create characteristic function (Local Similarity).

Parameters

st: ObsPy.Stream

Waveform stream.

channel: str

Channel code.

subnetworks: list

station clusters

w: float

A win length in seconds. Default is 1.0 seconds.

dt: float

Sampling interval (seconds) of the returned characteristic function. Default is 0.1 second.

max_lag: float

Maximum allowed lag in seconds. Default is 0.1 second.

pos: str

Position of the max normalized cross-correlation value in each window. Possible values are ‘start’, ‘mid’ or ‘end’. Default value is ‘end’.

method: int

One of the integer (0, 1, 2, 3). It determines type of normalized cross-correlation (cor). If method = 0, it returns cor. If method = 1, it returns |cor|. If method = 2, it returns cor*cor. If method = 3, it returns cor*|cor|. Default method is 0.

dask_client: dask.Client

A dask client for parallel processing. Default is None.

Returns

st_r: ObsPy.Stream

Waveform stream for reference stations.

st_s: ObsPy.Stream

Waveform stream for secondary stations.

st_pcc: ObsPy.Stream

Peak cross-correlation stream.

st_dpcc: ObsPy.Stream

Differentiated peak cross-correlation stream.

st_ls: ObsPy.Stream

Local similarity stream.

st_dls ObsPy.Stream

Differentiated local similarity stream.

Example

>>> import seisscan as ss
>>>
>>> event_dict, st, inventory, subnetworks, model_name = ss.read_example()
>>>
>>> st_r, st_s, st_pcc, st_dpcc, st_ls, st_dls = do_ls(st, "DPZ", subnetworks=subnetworks)