The analysers.noise_analyser module

class grond.analysers.noise_analyser.analyser.NoiseAnalyser(nwindows, pre_event_noise_duration, check_events, phase_def, statistic, mode, cutoff, cutoff_exception_on_high_snr)[source]

From the pre-event station noise variance-based trace weights are formed.

By default, the trace weights are the inverse of the noise variance. The correlation of the noise is neglected. Optionally, using a the gCMT global earthquake catalogue, the station data are checked for theoretical phase arrivals of M>5 earthquakes. In case of a very probable contamination the trace weights are set to zero. In case global earthquake phase arrivals are within a 30 min time window before the start of the set pre-event noise window, only a warning is thrown.

It is further possible to disregard data with a noise level exceeding the median by a given cutoff factor. These weights are set to 0. This can be done exclusively (mode='weeding') such that noise weights are either 1 or 0, or in combination with weighting below the median-times-cutoff noise level (mode='weighting').

class grond.analysers.noise_analyser.analyser.NoiseAnalyserConfig(**kwargs)[source]

Configuration parameters for the pre-event noise analysis.


int, default: 1

number of windows for trace splitting


float, default: 0.0

Total length of noise trace in the analysis


str, default: 'P'

Onset of phase_def used for upper limit of window


bool, default: False

check the GlobalCMT for M>5 earthquakes that produce phase arrivals contaminating and affecting the noise analysis


str (pyrocko.guts.StringChoice), default: 'var'

Set weight to inverse of noise variance (var) or standard deviation (std).


str (pyrocko.guts.StringChoice), default: 'weighting'

Generate weights based on inverse of noise measure (weighting), or discrete on/off style in combination with cutoff value (weeding).


float, optional

Set weight to zero, when noise level exceeds median by the given cutoff factor.


float, optional

Exclude from cutoff when max amplitude exceeds standard deviation times this factor.