Temporal occurrence models

Module openquake.hazardlib.tom contains implementations of probability density functions for earthquake temporal occurrence modeling.

class openquake.hazardlib.tom.PoissonTOM(time_span)

Poissonian temporal occurrence model.

Parameters:time_span – The time interval of interest, in years. Raises:ValueError – If time_span is not positive.

get_probability_no_exceedance(occurrence_rate, poes)

Compute and return, for a number of ground motion levels and sites, the probability that a rupture with annual occurrence rate given by occurrence_rate and able to cause ground motion values higher than a given level at a site with probability poes, does not cause any exceedance in the time window specified by the time_span parameter given in the constructor.

The probability is computed using the following formula

(1 - e ** (-occurrence_rate * time_span)) ** poes

Parameters:

• occurrence_rate – The average number of events per year.
• poes – 2D numpy array containing conditional probabilities the the a rupture occurrence causes a ground shaking value exceeding a ground motion level at a site. First dimension represent sites, second dimension intensity measure levels. poes can be obtained calling the method.

Returns:

2D numpy array containing probabilities of no exceedance. First dimension represents sites, second dimensions intensity measure levels.

get_probability_one_occurrence(occurrence_rate)

Calculate and return the probability of event to occur once within the time range defined by the constructor’s time_span parameter value.

get_probability_one_or_more_occurrences(occurrence_rate)

Calculate and return the probability of event to happen one or more times within the time range defined by constructor’s time_span parameter value.

Calculates probability as 1 - e ** (-occurrence_rate*time_span).

Parameters:occurrence_rate – The average number of events per year. Returns:Float value between 0 and 1 inclusive.

sample_number_of_occurrences(occurrence_rate)

Draw a random sample from the distribution and return a number of events to occur.

Method uses numpy random generator, which needs to be seeded outside of this method in order to get reproducible results.

Parameters:occurrence_rate – The average number of events per year. Returns:Sampled integer number of events to occur within model’s time span.