def compute_gradient(self):
""" Sequential event gradient computation, reduces storage requirement.
"""
# generate synthetic data
output_dir = join(self.getpath, 'traces', 'syn')
self.generate_data(model_dir=PATH.MODEL, output_dir=output_dir, save_wavefield=True)
# prepare adjoint sources
preprocess.prepare_eval_grad(self.getpath)
# compute event gradient
self.evaluate_gradient(model_dir=PATH.MODEL)
def eval_func(self, path='', export_traces=False):
""" Evaluates misfit function by carrying out forward simulation and
comparing observations and synthetics.
"""
unix.cd(self.getpath)
self.import_model(path)
self.forward()
preprocess.prepare_eval_grad(self.getpath)
self.export_residuals(path)
if export_traces:
self.export_traces(path, prefix='traces/syn')
def eval_func(self, path='', export_traces=False):
""" Evaluates misfit function by carrying out forward simulation and
comparing observations and synthetics.
"""
unix.cd(self.getpath)
self.import_model(path)
self.forward()
unix.mv(self.data_wildcard, 'traces/syn')
preprocess.prepare_eval_grad(self.getpath)
self.export_residuals(path)
if export_traces:
self.export_traces(path, prefix='traces/syn')