def test_calculate_taper_function_zero_case(self):
'''
Test case when g_function is 0.0.
g_function is 0 when (obs - sel) < -100.0 * corner_mo
This scenario seems to occur when the selected moment is
significantly greater than the corner magnitude
'''
self.assertAlmostEqual(
0.0,
calculate_taper_function(moment_function(5.0),
moment_function(8.0),
moment_function(6.0), self.beta))
def test_calculate_taper_function_zero_case(self):
'''
Test case when g_function is 0.0.
g_function is 0 when (obs - sel) < -100.0 * corner_mo
This scenario seems to occur when the selected moment is
significantly greater than the corner magnitude
'''
self.assertAlmostEqual(0.0, calculate_taper_function(
moment_function(5.0),
moment_function(8.0),
moment_function(6.0),
self.beta))
def test_continuum_seismicity(self):
'''
Tests the function hmtk.strain.shift.Shift.continuum_seismicity -
the python implementation of the Subroutine Continuum Seismicity from
the Fortran 90 code GSRM.f90
'''
self.strain_model = GeodeticStrain()
# Define a simple strain model
test_data = {'longitude': np.zeros(3, dtype=float),
'latitude': np.zeros(3, dtype=float),
'exx': np.array([1E-9, 1E-8, 1E-7]),
'eyy': np.array([5E-10, 5E-9, 5E-8]),
'exy': np.array([2E-9, 2E-8, 2E-7])}
self.strain_model.get_secondary_strain_data(test_data)
self.model = Shift([5.66, 6.66])
threshold_moment = moment_function(np.array([5.66, 6.66]))
expected_rate = np.array([[-14.43624419, -22.48168502],
[-13.43624419, -21.48168502],
[-12.43624419, -20.48168502]])
np.testing.assert_array_almost_equal(
expected_rate,
np.log10(self.model.continuum_seismicity(
threshold_moment,
self.strain_model.data['e1h'],
self.strain_model.data['e2h'],
self.strain_model.data['err'],
BIRD_GLOBAL_PARAMETERS['OSRnor'])))
def test_calculate_taper_function(self):
'''
Tests the function to calculate the taper part of the Tapered
Gutenberg & Richter model with exhaustive data set
'''
obs_mo = moment_function(np.arange(5.0, 9.5, 1.0))
sel_mo = moment_function(np.arange(5.0, 9.5, 1.0))
obs_data = np.zeros([len(obs_mo), len(sel_mo)], dtype=float)
corner_mo = moment_function(8.5)
for iloc, obs in enumerate(obs_mo):
for jloc, sel in enumerate(sel_mo):
obs_data[iloc, jloc] = calculate_taper_function(
obs, sel, corner_mo, self.beta)
np.testing.assert_array_almost_equal(TAPER_FUNCTION_DATA,
np.log10(obs_data))
def test_moment_function(self):
'''
Tests the simple function to implement the Hanks & Kanamori (1979)
formula for an input magnitude
'''
expected_value = 18.05
self.assertAlmostEqual(expected_value, log10(moment_function(6.0)))
def _get_base_rates(self, base_params):
'''
Defines the base moment rate that should be assigned to places of
zero strain (i.e. Intraplate regions). In Bird et al (2010) this is
taken as basic rate of Intraplate events in GCMT catalogue above the
threshold magnitude
:param dict base_params:
Parameters needed for calculating the base rate. Requires:
'CMT_EVENTS': The number of CMT events
'area': Total area (km ^ 2) of the region class
'CMT_duration': Duration of reference catalogue
'CMT_moment': Moment rate from CMT catalogue
'corner_mag': Corner magnitude of Tapered G-R for region
'beta': Beta value of tapered G-R for distribution
'''
base_ipl_rate = base_params['CMT_EVENTS'] / (
base_params['area'] * base_params['CMT_duration'])
base_rate = np.zeros(self.number_magnitudes, dtype=float)
for iloc in range(0, self.number_magnitudes):
base_rate[iloc] = base_ipl_rate * calculate_taper_function(
base_params['CMT_moment'],
self.threshold_moment[iloc],
moment_function(base_params['corner_mag']),
base_params['beta'])
return base_rate
def test_continuum_seismicity(self):
# Tests the function hmtk.strain.shift.Shift.continuum_seismicity -
# the python implementation of the Subroutine Continuum Seismicity
# from the Fortran 90 code GSRM.f90
self.strain_model = GeodeticStrain()
# Define a simple strain model
test_data = {
'longitude': np.zeros(3, dtype=float),
'latitude': np.zeros(3, dtype=float),
'exx': np.array([1E-9, 1E-8, 1E-7]),
'eyy': np.array([5E-10, 5E-9, 5E-8]),
'exy': np.array([2E-9, 2E-8, 2E-7])
}
self.strain_model.get_secondary_strain_data(test_data)
self.model = Shift([5.66, 6.66])
threshold_moment = moment_function(np.array([5.66, 6.66]))
expected_rate = np.array([[-14.43624419, -22.48168502],
[-13.43624419, -21.48168502],
[-12.43624419, -20.48168502]])
np.testing.assert_array_almost_equal(
expected_rate,
np.log10(
self.model.continuum_seismicity(
threshold_moment, self.strain_model.data['e1h'],
self.strain_model.data['e2h'],
self.strain_model.data['err'],
BIRD_GLOBAL_PARAMETERS['OSRnor'])))
def _get_base_rates(self, base_params):
'''
Defines the base moment rate that should be assigned to places of
zero strain (i.e. Intraplate regions). In Bird et al (2010) this is
taken as basic rate of Intraplate events in GCMT catalogue above the
threshold magnitude
:param dict base_params:
Parameters needed for calculating the base rate. Requires:
'CMT_EVENTS': The number of CMT events
'area': Total area (km ^ 2) of the region class
'CMT_duration': Duration of reference catalogue
'CMT_moment': Moment rate from CMT catalogue
'corner_mag': Corner magnitude of Tapered G-R for region
'beta': Beta value of tapered G-R for distribution
'''
base_ipl_rate = base_params['CMT_EVENTS'] / (
base_params['area'] * base_params['CMT_duration'])
base_rate = np.zeros(self.number_magnitudes, dtype=float)
for iloc in range(0, self.number_magnitudes):
base_rate[iloc] = base_ipl_rate * calculate_taper_function(
base_params['CMT_moment'],
self.threshold_moment[iloc],
moment_function(base_params['corner_mag']),
base_params['beta'])
return base_rate
def test_moment_function(self):
'''
Tests the simple function to implement the Hanks & Kanamori (1979)
formula for an input magnitude
'''
expected_value = 18.05
self.assertAlmostEqual(expected_value,
log10(moment_function(6.0)))
def test_calculate_taper_function(self):
'''
Tests the function to calculate the taper part of the Tapered
Gutenberg & Richter model with exhaustive data set
'''
obs_mo = moment_function(np.arange(5.0, 9.5, 1.0))
sel_mo = moment_function(np.arange(5.0, 9.5, 1.0))
obs_data = np.zeros([len(obs_mo), len(sel_mo)], dtype=float)
corner_mo = moment_function(8.5)
for iloc, obs in enumerate(obs_mo):
for jloc, sel in enumerate(sel_mo):
obs_data[iloc, jloc] = calculate_taper_function(obs,
sel,
corner_mo,
self.beta)
np.testing.assert_array_almost_equal(TAPER_FUNCTION_DATA,
np.log10(obs_data))
def __init__(self,
minimum_magnitude,
base_params=None,
region_parameter_file=None):
'''
Instantiate the class, retreive minimum moments, base rates and
regionalisation informaton
:param float/list/np.ndarray minimum_magnitude:
Target magnitudes for calculating the activity rates
:param dict base_params:
Regionalisation parameters for the background region type (in this
case the Bird et al. Intraplate class
:param str region_parameter_file:
To overwrite the default Bird et al (2007) classifcations the
regionalisation can be defined in a separate Yaml file
'''
base_params = base_params or IPL_PARAMS
self.strain = None
if isinstance(minimum_magnitude, float):
self.target_magnitudes = np.array([minimum_magnitude], dtype=float)
elif isinstance(minimum_magnitude, list):
self.target_magnitudes = np.array(minimum_magnitude, dtype=float)
elif isinstance(minimum_magnitude, np.ndarray):
self.target_magnitudes = minimum_magnitude
else:
raise ValueError('Minimum magnitudes must be float, list or array')
self.number_magnitudes = len(self.target_magnitudes)
self.threshold_moment = moment_function(self.target_magnitudes)
# Get the base rate from the input parameters
self.base_rate = self._get_base_rates(base_params)
# If a regionalisation parameter file is defined then read
# regionalisation from there - otherwise use Bird regionalisation
if region_parameter_file:
self.regionalisation = yaml.load(open(region_parameter_file, 'rt'))
else:
self.regionalisation = BIRD_GLOBAL_PARAMETERS
def __init__(self, minimum_magnitude, base_params=None,
region_parameter_file=None):
'''
Instantiate the class, retreive minimum moments, base rates and
regionalisation informaton
:param float/list/np.ndarray minimum_magnitude:
Target magnitudes for calculating the activity rates
:param dict base_params:
Regionalisation parameters for the background region type (in this
case the Bird et al. Intraplate class
:param str region_parameter_file:
To overwrite the default Bird et al (2007) classifcations the
regionalisation can be defined in a separate Yaml file
'''
base_params = base_params or IPL_PARAMS
self.strain = None
if isinstance(minimum_magnitude, float):
self.target_magnitudes = np.array([minimum_magnitude], dtype=float)
elif isinstance(minimum_magnitude, list):
self.target_magnitudes = np.array(minimum_magnitude, dtype=float)
elif isinstance(minimum_magnitude, np.ndarray):
self.target_magnitudes = minimum_magnitude
else:
raise ValueError('Minimum magnitudes must be float, list or array')
self.number_magnitudes = len(self.target_magnitudes)
self.threshold_moment = moment_function(self.target_magnitudes)
# Get the base rate from the input parameters
self.base_rate = self._get_base_rates(base_params)
# If a regionalisation parameter file is defined then read
# regionalisation from there - otherwise use Bird regionalisation
if region_parameter_file:
self.regionalisation = yaml.load(open(region_parameter_file, 'rt'))
else:
self.regionalisation = BIRD_GLOBAL_PARAMETERS
def test_moment_magnitude_function(self):
'''
Tests the Hanks & Kanamori (1979) formula for an input moment
'''
self.assertAlmostEqual(6.0,
moment_magnitude_function(moment_function(6.0)))
'OCB': OCB_PARAMS,
'SUB': SUB_PARAMS,
'IPL': IPL_PARAMS}
# This value of 25.7474 is taken from Bird's analysis -
# TODO this needs to be generalised if integrating w/Modeller
CMT_DURATION_S = 25.7474 * SECS_PER_YEAR
# Apply SI conversion adjustments from Bird (2007)'s code
# TODO This is ugly - reconsider this (maybe require only inputs in SI)
for reg_type in BIRD_GLOBAL_PARAMETERS.keys():
reg = BIRD_GLOBAL_PARAMETERS[reg_type]
reg['corner_moment'] = moment_function(reg['corner_mag'])
if reg_type is not 'IPL':
reg['CMT_pure_event_rate'] = reg['CMT_EVENTS'] / CMT_DURATION_S
reg['length'] = 1000.0 * reg['length']
reg['velocity'] = (reg['velocity'] * 0.001) / SECS_PER_YEAR
reg['assumed_dip'] = reg['assumed_dip'] * RADIAN_CONV
reg['assumed_mu'] = reg['assumed_mu'] * 1.0E9
reg['coupled_thickness'] = reg['coupled_thickness'] * 1000.
reg['lithosphere'] = reg['lithosphere'] * 1000.
else:
reg['CMT_pure_event_rate'] = reg['CMT_EVENTS'] / reg['CMT_duration']
BIRD_GLOBAL_PARAMETERS[reg_type] = reg
STRAIN_VARIABLES = ['exx', 'eyy', 'exy', 'e1h', 'e2h', 'err', '2nd_inv',
'dilatation']
def test_moment_magnitude_function(self):
'''
Tests the Hanks & Kanamori (1979) formula for an input moment
'''
self.assertAlmostEqual(6.0,
moment_magnitude_function(moment_function(6.0)))
'OCB': OCB_PARAMS,
'SUB': SUB_PARAMS,
'IPL': IPL_PARAMS}
# This value of 25.7474 is taken from Bird's analysis -
# TODO this needs to be generalised if integrating w/Modeller
CMT_DURATION_S = 25.7474 * SECS_PER_YEAR
# Apply SI conversion adjustments from Bird (2007)'s code
# TODO This is ugly - reconsider this (maybe require only inputs in SI)
for reg_type in BIRD_GLOBAL_PARAMETERS:
reg = BIRD_GLOBAL_PARAMETERS[reg_type]
reg['corner_moment'] = moment_function(reg['corner_mag'])
if reg_type is not 'IPL':
reg['CMT_pure_event_rate'] = reg['CMT_EVENTS'] / CMT_DURATION_S
reg['length'] = 1000.0 * reg['length']
reg['velocity'] = (reg['velocity'] * 0.001) / SECS_PER_YEAR
reg['assumed_dip'] = reg['assumed_dip'] * RADIAN_CONV
reg['assumed_mu'] = reg['assumed_mu'] * 1.0E9
reg['coupled_thickness'] = reg['coupled_thickness'] * 1000.
reg['lithosphere'] = reg['lithosphere'] * 1000.
else:
reg['CMT_pure_event_rate'] = reg['CMT_EVENTS'] / reg['CMT_duration']
BIRD_GLOBAL_PARAMETERS[reg_type] = reg
STRAIN_VARIABLES = ['exx', 'eyy', 'exy', 'e1h', 'e2h', 'err', '2nd_inv',
'dilatation']
