def get_ricker_data(list_of_power,
num_time_sample,
num_point_ricker_wavelet,
width_parameter_ricker_wavelet,
domain_time_sample,
noise_level):
num_trace = list_of_power.size
true_data_by_trace_time = data_gen.createCubedGaussianWhiteNoiseConvolvedWithRickerWavelet(
num_trace,
num_time_sample,
num_point_ricker_wavelet,
width_parameter_ricker_wavelet)
data_by_trace_time = np.zeros((num_trace,num_time_sample))
for idx_power in range(list_of_power.size):
attenuation_by_time_sample = np.power(domain_time_sample,
-list_of_power[idx_power])
additive_noise = np.random.randn(num_time_sample) * noise_level
data_by_trace_time[idx_power,:] = \
attenuation_by_time_sample * true_data_by_trace_time[idx_power,:] + additive_noise
return data_by_trace_time
have_ungained_signal = 1
true_data_by_trace_time = np.ones((num_trace,num_time_sample))
data_by_trace_time = np.zeros((num_trace,num_time_sample))
for idx_power in range(list_of_power.size):
data_by_trace_time[idx_power,:] = np.power(domain_time_sample,
-list_of_power[idx_power])
break
if case('noise_ricker_convolved'):
have_ungained_signal = 1
num_point_ricker_wavelet = 100
width_parameter_ricker_wavelet = 10
true_data_by_trace_time = data_gen.createCubedGaussianWhiteNoiseConvolvedWithRickerWavelet(
num_trace,
num_time_sample,
num_point_ricker_wavelet,
width_parameter_ricker_wavelet)
data_by_trace_time = np.zeros((num_trace,num_time_sample))
for idx_power in range(list_of_power.size):
attenuation_by_time_sample = np.power(domain_time_sample,
-list_of_power[idx_power])
data_by_trace_time[idx_power,:] = \
attenuation_by_time_sample * true_data_by_trace_time[idx_power,:]
break
if case('white_noise'):
have_ungained_signal = 1
true_data_by_trace_time = np.random.randn(num_trace,num_time_sample)
data_by_trace_time = np.zeros((num_trace,num_time_sample))
