mtuq.PolarityMisfit

class mtuq.PolarityMisfit(method='taup', taup_model='ak135', FK_database=None, FK_model=None, CPS_database=None, CPS_model=None, **parameters)

Bases: object

Polarity misfit function

Usage

Evaluating polarity misfit is a two-step procedure.

First, the user supplies parameters such as the method used to calculate predicted polarities (see below for detailed parameter descriptions):

polarity_misfit = PolarityMisfit(**parameters)

Second, the user invokes the misfit function:

values = polarity_misfit(data, greens, sources)

During misfit evaluation

• observed polarities are collected from the data argument, which can be either a list of integers or a Dataset with observed polarity values attached (see convention below for more information)

• predicted polarities are calculated from the greens argument (see parameter descriptions below for more information)

• a NumPy array of length len(sources) is returned, giving the number of mismatches between observed and predicted

Parameters

method (str)

• 'taup' Calculate polarities using Taup-P

• 'FK_metadata' Read polarities from FK database

• 'CPS_metadata' Read polarities from CPS database

• 'waveform' Determine polarity from full-waveform synthetics (not implemented yet)

Other input arguments that may be required, depending on the above

taup_model (str): Name of built-in ObsPy TauP model or path to custom ObsPy TauP model, required for method=taup

FK_database (str): Path to FK database, required for for method=FK_metadata.

Note

Convention : Positive vertical first motions are encoded as +1 and negative vertical first motions are encoded as -1. Unpicked or indeterminate first motions can be encoded as 0.

Public Methods

collect_attributes	Collect polarity-related attributes (used for beachball plots)
get_observed	Extracts observed polarities from data
get_predicted	Calculates predicted polarities