def computeTravelTimes(self, slowness, allSensors=True):
"""Compute the travel times and fill data and time matrix
for later use of response and Jacobian, respectively.
For response only active sources are needed, for Jacobian we need all.
"""
# mesh = self.mesh() # better but for now input mesh
mesh = self.mesh_
param_markers = np.unique(mesh.cellMarkers())
param_count = len(param_markers)
if len(slowness) == mesh.cellCount():
self.mapModel(slowness)
elif len(slowness) == param_count:
# map the regions in the mesh to slowness
slow_map = pg.stdMapF_F()
min_reg_num = min(param_markers)
for i, si in enumerate(slowness):
slow_map.insert(float(i+min_reg_num), si)
mesh.mapCellAttributes(slow_map)
else:
raise ValueError("Wrong no of parameters. Mesh size: {}, no "
"of regions: {}, and number of slowness values:"
"{}".format(self.mesh().cellCount(), param_count,
len(slowness)))
times = pg.RVector(self.nNodes, 0.)
upTags = np.zeros(self.nNodes)
downTags = np.zeros(mesh.nodeCount())
for iSource in range(self.nSensors):
# initial condition (reset vectors)
times *= 0.0
upTags *= 0
downwind = set()
source = data.sensorPosition(iSource)
cell = self.mesh_.findCell(source)
# fill in nodes around source using local smoothness
for i, n in enumerate(cell.nodes()):
times[n.id()] = cell.attribute() * n.pos().distance(source)
upTags[n.id()] = 1
for i, n in enumerate(cell.nodes()):
tmpNodes = pg.commonNodes(n.cellSet())
for nn in tmpNodes:
if not upTags[nn.id()] and not downTags[nn.id()]:
downwind.add(nn)
downTags[nn.id()] = 1
while len(downwind) > 0: # start fast marching
fastMarch(self.mesh_, downwind, times, upTags, downTags)
self.dataMatrix[iSource] = pg.interpolate(mesh, times,
data.sensorPositions())
self.timeMatrix[iSource] = pg.interpolate(mesh, times,
self.midPoints)
sensor_idx = data("g")[data("s") == iSource]
cell = mesh.findCell(source)
for i, n in enumerate(cell.nodes()):
times[n.id()] = cell.attribute() * n.pos().distance(source)
upTags[n.id()] = 1
for i, n in enumerate(cell.nodes()):
tmpNodes = pg.commonNodes(n.cellSet())
for nn in tmpNodes:
if not upTags[nn.id()] and not downTags[nn.id()]:
downwind.add(nn)
downTags[nn.id()] = 1
# start fast marching
tic = time.time()
while len(downwind) > 0:
fastMarch(mesh, downwind, times, upTags, downTags)
print(time.time() - tic, "s")
# compare with analytical solution along the x axis
x = np.arange(0., 100., 0.5)
t = pg.interpolate(mesh, times, pg.asvector(x), x * 0., x * 0.)
tdirect = x / v[0] # direct wave
alfa = asin(v[0] / v[1]) # critically refracted wave angle
xreflec = tan(alfa) * zlay * 2. # first critically refracted
trefrac = (x - xreflec) / v[1] + xreflec * v[1] / v[0]**2
tana = np.where(trefrac < tdirect, trefrac, tdirect) # minimum of both
print("min(dt)=",
min(t - tana) * 1000,
"ms max(dt)=",
max(t - tana) * 1000,
def response(self, slowness):
"""
Response function. Returns the result of the forward calculation.
Uses the shot- and sensor positions specified in the data container.
"""
mesh = self.mesh()
param_markers = np.unique(mesh.cellMarker())
param_count = len(param_markers)
if len(slowness) == mesh.cellCount():
self.mapModel(slowness)
elif len(slowness) == param_count:
# map the regions in the mesh to slowness
slow_map = pg.stdMapF_F()
min_reg_num = min(param_markers)
for i, si in enumerate(slowness):
slow_map.insert(float(i+min_reg_num), si)
mesh.mapCellAttributes(slow_map)
else:
raise ValueError("Wrong no of parameters. Mesh size: {}, no "
"of regions: {}, and number of slowness values:"
"{}".format(self.mesh().cellCount(), param_count,
len(slowness)))
data = self.data()
n_data = data.size()
t_fmm = np.zeros(n_data)
idx = 0
for source_idx in [0]: # np.unique(data("s")):
# initialize source position and trvel time vector
n_sensors = np.sum(data("s") == source_idx)
# maybe not always same number of sensors
source = data.sensorPosition(int(source_idx))
times = pg.RVector(mesh.nodeCount(), 0.)
# initialize sets and tags
# upwind, downwind = set(), set()
downwind = set()
upTags = np.zeros(mesh.nodeCount())
downTags = np.zeros(mesh.nodeCount())
# define initial condition
cell = mesh.findCell(source)
for i, n in enumerate(cell.nodes()):
times[n.id()] = cell.attribute() * n.pos().distance(source)
upTags[n.id()] = 1
for i, n in enumerate(cell.nodes()):
tmpNodes = pg.commonNodes(n.cellSet())
for nn in tmpNodes:
if not upTags[nn.id()] and not downTags[nn.id()]:
downwind.add(nn)
downTags[nn.id()] = 1
# start fast marching
while len(downwind) > 0:
fastMarch(mesh, downwind, times, upTags, downTags)
self.timefields[source_idx] = np.array(times)
sensor_idx = data("g")[data("s") == source_idx]
t_fmm[idx:idx+n_sensors] = np.array(
[times[mesh.findNearestNode(data.sensorPosition(int(i)))]
for i in sensor_idx])
idx += n_sensors
return t_fmm
cell = mesh.findCell(source)
for i, n in enumerate(cell.nodes()):
times[n.id()] = cell.attribute() * n.pos().distance(source)
upTags[n.id()] = 1
for i, n in enumerate(cell.nodes()):
tmpNodes = pg.commonNodes(n.cellSet())
for nn in tmpNodes:
if not upTags[nn.id()] and not downTags[nn.id()]:
downwind.add(nn)
downTags[nn.id()] = 1
# start fast marching
tic = time.time()
while len(downwind) > 0:
fastMarch(mesh, downwind, times, upTags, downTags)
print(time.time() - tic, "s")
# compare with analytical solution along the x axis
x = np.arange(0., 100., 0.5)
t = pg.interpolate(mesh, times, x, x * 0., x * 0.)
tdirect = x / v[0] # direct wave
alfa = asin(v[0] / v[1]) # critically refracted wave angle
xreflec = tan(alfa) * zlay * 2. # first critically refracted
trefrac = (x - xreflec) / v[1] + xreflec * v[1] / v[0]**2
tana = np.where(trefrac < tdirect, trefrac, tdirect) # minimum of both
print("min(dt)=",
min(t - tana) * 1000, "ms max(dt)=",
max(t - tana) * 1000, "ms")
