def make_benchmark_data(path):
"""Generate a SEG-Y file with specific geometry so that CDP gathers contain the same number of
traces and construct survey objects for benchmark.
"""
# The geometry defined below should be changed only together with survey filtering parameters
# to ensure that after filtering all the gathers \ supergathers have the same number of traces
make_prestack_segy(path,
fmt=1,
survey_size=(400, 400),
sources_step=(5, 5),
receivers_step=(5, 5),
activation_dist=(50, 50),
bin_size=(10, 10))
# Load headers and add synthetic FirstBreak times
sur = Survey(path,
header_index=['INLINE_3D', 'CROSSLINE_3D'],
header_cols='offset',
name='raw')
sur.headers['FirstBreak'] = np.random.randint(0, 3000, len(sur.headers))
def edge_lines_filter(line, num_lines):
return (line >= line.min() + num_lines) & (line <=
line.max() - num_lines)
# Drop three lines of CDPs from each side of the survey, since they have less traces than central ones
survey = (sur.filter(edge_lines_filter, 'CROSSLINE_3D',
num_lines=3).filter(edge_lines_filter,
'INLINE_3D',
num_lines=3))
sg_survey = survey.generate_supergathers((3, 3), (1, 1), (0, 0))
# Drop one line of supergathers from each side of the survey, since they have less traces than central ones
sg_survey = (sg_survey.filter(edge_lines_filter,
'SUPERGATHER_CROSSLINE_3D',
num_lines=1).filter(edge_lines_filter,
'SUPERGATHER_INLINE_3D',
num_lines=1))
return survey, sg_survey
def test_load_traces_after_mmap_reconstruction(self, load_segy,
init_limits, load_limits,
traces_pos):
"""Compare loaded traces with the actual ones after the memory map is reconstructed."""
path, trace_data = load_segy
survey = Survey(path,
header_index="TRACE_SEQUENCE_FILE",
limits=init_limits,
use_segyio_trace_loader=False,
bar=False)
# The number of traces will change after filter. Memory map is reconstructed after copy and must remember
# original data shape.
survey = survey.filter(lambda tsf: tsf % 2 == 1,
"TRACE_SEQUENCE_FILE",
inplace=True).copy()
# load_limits take priority over init_limits
limits = init_limits if load_limits is None else load_limits
trace_data = trace_data[traces_pos, limits]
loaded_data = survey.load_traces(traces_pos, limits=load_limits)
assert np.allclose(loaded_data, trace_data)
