def afteran_decluster(
catalogue_matrix, window_opt=TDW_GARDNERKNOPOFF, time_window=60.):
'''AFTERAN declustering algorithm.
||(Musson, 1999, "Probabilistic Seismic Hazard Maps for the North Balkan
region", Annali di Geofisica, 42(6), 1109 - 1124) ||
:param catalog_matrix: eq catalog in a matrix format with these columns in
order: `year`, `month`, `day`, `longitude`,
`latitude`, `Mw`
:type catalog_matrix: numpy.ndarray
:keyword window_opt: method used in calculating distance and time windows
:type window_opt: string
:keyword time_window: Length (in days) of moving time window
:type time_window: positive float
:returns: **vcl vector** indicating cluster number, **vmain_shock catalog**
containing non-clustered events, **flagvector** indicating
which eq events belong to a cluster
:rtype: numpy.ndarray
'''
#Convert time window from days to decimal years
time_window = time_window / 365.
# Pre-processing steps are the same as for Gardner & Knopoff
# Get relevent parameters
mag = catalogue_matrix[:, 5]
neq = np.shape(catalogue_matrix)[0] # Number of earthquakes
# Get decimal year (needed for time windows)
year_dec = decimal_year(catalogue_matrix[:, 0], catalogue_matrix[:, 1],
catalogue_matrix[:, 2])
# Get space windows corresponding to each event
sw_space = time_dist_windows[window_opt].calc(mag)[0]
eqid = np.arange(0, neq, 1) # Initial Position Identifier
# Pre-allocate cluster index vectors
vcl = np.zeros((neq, 1), dtype=int)
flagvector = np.zeros((neq, 1), dtype=int)
# Sort magnitudes into descending order
id0 = np.flipud(np.argsort(mag, kind='heapsort'))
mag = mag[id0]
catalogue_matrix = catalogue_matrix[id0, :]
sw_space = sw_space[id0]
year_dec = year_dec[id0]
eqid = eqid[id0]
i = 0
clust_index = 0
while i < neq:
if vcl[i] == 0:
# Earthquake not allocated to cluster - perform calculation
# Perform distance calculation
mdist = haversine(catalogue_matrix[:, 3], catalogue_matrix[:, 4],
catalogue_matrix[i, 3], catalogue_matrix[i, 4])
# Select earthquakes inside distance window and not in cluster
vsel = np.logical_and(mdist <= sw_space[i], vcl == 0).flatten()
dtime = year_dec[vsel] - year_dec[i]
nval = np.shape(dtime)[0] # Number of events inside valid window
# Pre-allocate boolean array (all True)
vsel1 = _find_aftershocks(dtime, nval, time_window)
vsel2 = _find_foreshocks(dtime, nval, time_window, vsel1)
temp_vsel = np.copy(vsel)
temp_vsel[vsel] = np.logical_or(vsel1, vsel2)
if np.shape(np.nonzero(temp_vsel)[0])[0] > 1:
# Contains clustered events - allocate a cluster index
vcl[temp_vsel] = clust_index + 1
# Remove mainshock from cluster
vsel1[0] = False
# Assign markers to aftershocks and foreshocks
temp_vsel = np.copy(vsel)
temp_vsel[vsel] = vsel1
flagvector[temp_vsel] = 1
vsel[vsel] = vsel2
flagvector[vsel] = -1
clust_index += 1
i += 1
# Now have events - re-sort array back into chronological order
# Re-sort the data into original order
id1 = np.argsort(eqid, kind='heapsort')
eqid = eqid[id1]
catalogue_matrix = catalogue_matrix[id1, :]
vcl = vcl[id1]
flagvector = flagvector[id1]
# Now to produce a catalogue with aftershocks purged
vmain_shock = catalogue_matrix[np.nonzero(flagvector == 0)[0], :]
return vcl.flatten(), vmain_shock, flagvector.flatten()
def afteran_decluster(catalogue_matrix,
window_opt=TDW_GARDNERKNOPOFF,
time_window=60.):
'''AFTERAN declustering algorithm.
||(Musson, 1999, "Probabilistic Seismic Hazard Maps for the North Balkan
region", Annali di Geofisica, 42(6), 1109 - 1124) ||
:param catalog_matrix: eq catalog in a matrix format with these columns in
order: `year`, `month`, `day`, `longitude`,
`latitude`, `Mw`
:type catalog_matrix: numpy.ndarray
:keyword window_opt: method used in calculating distance and time windows
:type window_opt: string
:keyword time_window: Length (in days) of moving time window
:type time_window: positive float
:returns: **vcl vector** indicating cluster number, **vmain_shock catalog**
containing non-clustered events, **flagvector** indicating
which eq events belong to a cluster
:rtype: numpy.ndarray
'''
#Convert time window from days to decimal years
time_window = time_window / 365.
# Pre-processing steps are the same as for Gardner & Knopoff
# Get relevent parameters
mag = catalogue_matrix[:, 5]
neq = np.shape(catalogue_matrix)[0] # Number of earthquakes
# Get decimal year (needed for time windows)
year_dec = decimal_year(catalogue_matrix[:, 0], catalogue_matrix[:, 1],
catalogue_matrix[:, 2])
# Get space windows corresponding to each event
sw_space = time_dist_windows[window_opt].calc(mag)[0]
eqid = np.arange(0, neq, 1) # Initial Position Identifier
# Pre-allocate cluster index vectors
vcl = np.zeros((neq, 1), dtype=int)
flagvector = np.zeros((neq, 1), dtype=int)
# Sort magnitudes into descending order
id0 = np.flipud(np.argsort(mag, kind='heapsort'))
mag = mag[id0]
catalogue_matrix = catalogue_matrix[id0, :]
sw_space = sw_space[id0]
year_dec = year_dec[id0]
eqid = eqid[id0]
i = 0
clust_index = 0
while i < neq:
if vcl[i] == 0:
# Earthquake not allocated to cluster - perform calculation
# Perform distance calculation
mdist = haversine(catalogue_matrix[:, 3], catalogue_matrix[:, 4],
catalogue_matrix[i, 3], catalogue_matrix[i, 4])
# Select earthquakes inside distance window and not in cluster
vsel = np.logical_and(mdist <= sw_space[i], vcl == 0).flatten()
dtime = year_dec[vsel] - year_dec[i]
nval = np.shape(dtime)[0] # Number of events inside valid window
# Pre-allocate boolean array (all True)
vsel1 = _find_aftershocks(dtime, nval, time_window)
vsel2 = _find_foreshocks(dtime, nval, time_window, vsel1)
temp_vsel = np.copy(vsel)
temp_vsel[vsel] = np.logical_or(vsel1, vsel2)
if np.shape(np.nonzero(temp_vsel)[0])[0] > 1:
# Contains clustered events - allocate a cluster index
vcl[temp_vsel] = clust_index + 1
# Remove mainshock from cluster
vsel1[0] = False
# Assign markers to aftershocks and foreshocks
temp_vsel = np.copy(vsel)
temp_vsel[vsel] = vsel1
flagvector[temp_vsel] = 1
vsel[vsel] = vsel2
flagvector[vsel] = -1
clust_index += 1
i += 1
# Now have events - re-sort array back into chronological order
# Re-sort the data into original order
id1 = np.argsort(eqid, kind='heapsort')
eqid = eqid[id1]
catalogue_matrix = catalogue_matrix[id1, :]
vcl = vcl[id1]
flagvector = flagvector[id1]
# Now to produce a catalogue with aftershocks purged
vmain_shock = catalogue_matrix[np.nonzero(flagvector == 0)[0], :]
return vcl.flatten(), vmain_shock, flagvector.flatten()
def gardner_knopoff_decluster(
catalog_matrix, window_opt=TDW_GARDNERKNOPOFF, fs_time_prop=0):
"""
Gardner Knopoff algorithm.
:param catalog_matrix: eq catalog in a matrix format with these columns in
order: `year`, `month`, `day`, `longitude`,
`latitude`, `Mw`
:type catalog_matrix: numpy.ndarray
:keyword window_opt: method used in calculating distance and time windows
:type window_opt: string
:keyword fs_time_prop: foreshock time window as a proportion of
aftershock time window
:type fs_time_prop: positive float
:returns: **vcl vector** indicating cluster number, **vmain_shock catalog**
containing non-clustered events, **flagvector** indicating
which eq events belong to a cluster
:rtype: numpy.ndarray
"""
# Get relevent parameters
m = catalog_matrix[:, 5]
neq = np.shape(catalog_matrix)[0] # Number of earthquakes
# Get decimal year (needed for time windows)
year_dec = decimal_year(
catalog_matrix[:, 0], catalog_matrix[:, 1], catalog_matrix[:, 2])
# Get space and time windows corresponding to each event
sw_space, sw_time = time_dist_windows[window_opt].calc(m)
eqid = np.arange(0, neq, 1) # Initial Position Identifier
# Pre-allocate cluster index vectors
vcl = np.zeros(neq, dtype=int)
# Sort magnitudes into descending order
id0 = np.flipud(np.argsort(m, kind='heapsort'))
m = m[id0]
catalog_matrix = catalog_matrix[id0, :]
sw_space = sw_space[id0]
sw_time = sw_time[id0]
year_dec = year_dec[id0]
eqid = eqid[id0]
flagvector = np.zeros(neq, dtype=int)
#Begin cluster identification
clust_index = 0
for i in range(0, neq - 1):
if vcl[i] == 0:
# Find Events inside both fore- and aftershock time windows
dt = year_dec - year_dec[i]
vsel = np.logical_and(dt >= (-sw_time[i] * fs_time_prop),
dt <= sw_time[i],
flagvector == 0)
# Of those events inside time window, find those inside distance
# window
vsel1 = haversine(
catalog_matrix[vsel, 3], catalog_matrix[vsel, 4],
catalog_matrix[i, 3], catalog_matrix[i, 4]) <= sw_space[i]
vsel[vsel] = vsel1
temp_vsel = np.copy(vsel)
temp_vsel[i] = False
if any(temp_vsel):
# Allocate a cluster number
vcl[vsel] = clust_index + 1
flagvector[vsel] = 1
# For those events in the cluster before the main event,
# flagvector is equal to -1
temp_vsel[dt >= 0.0] = False
flagvector[temp_vsel] = -1
flagvector[i] = 0
clust_index += 1
# Re-sort the catalog_matrix into original order
id1 = np.argsort(eqid, kind='heapsort')
eqid = eqid[id1]
catalog_matrix = catalog_matrix[id1, :]
vcl = vcl[id1]
flagvector = flagvector[id1]
# Now to produce a catalogue with aftershocks purged
vmain_shock = catalog_matrix[np.nonzero(flagvector == 0)[0], :]
return vcl, vmain_shock, flagvector
def gardner_knopoff_decluster(catalog_matrix,
window_opt=TDW_GARDNERKNOPOFF,
fs_time_prop=0):
"""
Gardner Knopoff algorithm.
:param catalog_matrix: eq catalog in a matrix format with these columns in
order: `year`, `month`, `day`, `longitude`,
`latitude`, `Mw`
:type catalog_matrix: numpy.ndarray
:keyword window_opt: method used in calculating distance and time windows
:type window_opt: string
:keyword fs_time_prop: foreshock time window as a proportion of
aftershock time window
:type fs_time_prop: positive float
:returns: **vcl vector** indicating cluster number, **vmain_shock catalog**
containing non-clustered events, **flagvector** indicating
which eq events belong to a cluster
:rtype: numpy.ndarray
"""
# Get relevent parameters
m = catalog_matrix[:, 5]
neq = np.shape(catalog_matrix)[0] # Number of earthquakes
# Get decimal year (needed for time windows)
year_dec = decimal_year(catalog_matrix[:, 0], catalog_matrix[:, 1],
catalog_matrix[:, 2])
# Get space and time windows corresponding to each event
sw_space, sw_time = time_dist_windows[window_opt].calc(m)
eqid = np.arange(0, neq, 1) # Initial Position Identifier
# Pre-allocate cluster index vectors
vcl = np.zeros(neq, dtype=int)
# Sort magnitudes into descending order
id0 = np.flipud(np.argsort(m, kind='heapsort'))
m = m[id0]
catalog_matrix = catalog_matrix[id0, :]
sw_space = sw_space[id0]
sw_time = sw_time[id0]
year_dec = year_dec[id0]
eqid = eqid[id0]
flagvector = np.zeros(neq, dtype=int)
#Begin cluster identification
clust_index = 0
for i in range(0, neq - 1):
if vcl[i] == 0:
# Find Events inside both fore- and aftershock time windows
dt = year_dec - year_dec[i]
vsel = np.logical_and(dt >= (-sw_time[i] * fs_time_prop),
dt <= sw_time[i], flagvector == 0)
# Of those events inside time window, find those inside distance
# window
vsel1 = haversine(catalog_matrix[vsel, 3], catalog_matrix[vsel, 4],
catalog_matrix[i, 3],
catalog_matrix[i, 4]) <= sw_space[i]
vsel[vsel] = vsel1
temp_vsel = np.copy(vsel)
temp_vsel[i] = False
if any(temp_vsel):
# Allocate a cluster number
vcl[vsel] = clust_index + 1
flagvector[vsel] = 1
# For those events in the cluster before the main event,
# flagvector is equal to -1
temp_vsel[dt >= 0.0] = False
flagvector[temp_vsel] = -1
flagvector[i] = 0
clust_index += 1
# Re-sort the catalog_matrix into original order
id1 = np.argsort(eqid, kind='heapsort')
eqid = eqid[id1]
catalog_matrix = catalog_matrix[id1, :]
vcl = vcl[id1]
flagvector = flagvector[id1]
# Now to produce a catalogue with aftershocks purged
vmain_shock = catalog_matrix[np.nonzero(flagvector == 0)[0], :]
return vcl, vmain_shock, flagvector