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164 lines (150 loc) · 7.86 KB
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"""
Created on 2025/11/24
@author: Minyu Guo
This code is adapted from the original implementation provided by Mousavi et al. (2020).
Its primary function involves filtering the input data through multiple conditional criteria, segmenting it into fixed-size batches, and performing data augmentation.
The probability of applying each augmentation method can be customized.
"""
import numpy as np
from detect_data_augmentation import DataGenerator
import pandas as pd
np.random.seed(3407)
def read(input_hdf5=None,
input_csv=None,
output_name=None,
input_dimention=(6000, 3),
batch_size=64,
shuffle=False,
label_type='triangle',
normalization_mode='std',
augmentation=False,
add_event_r=0,
shift_event_r=0.99,
add_noise_r=0.3,
drop_channel_r=0.5,
add_gap_r=0.3,
coda_ratio=1.4,
scale_amplitude_r=0.3,
pre_emphasis=False,
loss_weights=[0.05, 0.40, 0.55],
train_valid_test_split=[0.8, 0.2, 0],
mode=None):
args = {
"input_hdf5": input_hdf5,
"input_csv": input_csv,
"output_name": output_name,
"input_dimention": input_dimention,
"batch_size": batch_size,
"shuffle": shuffle,
"label_type": label_type,
"normalization_mode": normalization_mode,
"augmentation": augmentation,
"add_event_r": add_event_r,
"shift_event_r": shift_event_r,
"add_noise_r": add_noise_r,
"add_gap_r": add_gap_r,
"coda_ratio": coda_ratio,
"drop_channel_r": drop_channel_r,
"scale_amplitude_r": scale_amplitude_r,
"pre_emphasis": pre_emphasis,
"loss_weights": loss_weights,
"train_valid_test_split": train_valid_test_split,
}
params_training = {'file_name': str(args['input_hdf5']),
'dim': args['input_dimention'][0],
'batch_size': args['batch_size'],
'n_channels': args['input_dimention'][-1],
'shuffle': args['shuffle'],
'norm_mode': args['normalization_mode'],
'label_type': args['label_type'],
'augmentation': args['augmentation'],
'add_event_r': args['add_event_r'],
'add_gap_r': args['add_gap_r'],
'coda_ratio': args['coda_ratio'],
'shift_event_r': args['shift_event_r'],
'add_noise_r': args['add_noise_r'],
'drop_channe_r': args['drop_channel_r'],
'scale_amplitude_r': args['scale_amplitude_r'],
'pre_emphasis': args['pre_emphasis']}
params_validation = {'file_name': str(args['input_hdf5']),
'dim': args['input_dimention'][0],
'batch_size': args['batch_size'],
'n_channels': args['input_dimention'][-1],
'shuffle': False,
'norm_mode': args['normalization_mode'],
'label_type': args['label_type'],
'augmentation': False,
'add_event_r': args['add_event_r'],
'add_gap_r': args['add_gap_r'],
'coda_ratio': args['coda_ratio'],
'shift_event_r': args['shift_event_r'],
'add_noise_r': args['add_noise_r'],
'drop_channe_r': args['drop_channel_r'],
'scale_amplitude_r': args['scale_amplitude_r'],
'pre_emphasis': args['pre_emphasis']}
params_noise = {'file_name': str(args['input_hdf5']),
'dim': args['input_dimention'][0],
'batch_size': args['batch_size'],
'n_channels': args['input_dimention'][-1],
'shuffle': False,
'norm_mode': 'max',
'augmentation': True}
training_data, validation_data, test_data = _split_data(args, args["output_name"])
# training_noise, validation_noise, test_noise = _split_noise(args, args["output_name"])
# real_data_test = _split_real_data(args, args["output_name"])
# detect & pick training
# training = training_data + training_noise
# validation = validation_data + validation_noise
# test = test_data + test_noise
# print('noise', len(training_noise))
# np.random.shuffle(training)
# np.random.shuffle(validation)
# np.random.shuffle(test)
# np.save(args["output_name"] + '/total_test', test_data)
training_generator = DataGenerator(training_data, **params_training)
validation_generator = DataGenerator(validation_data, **params_validation)
test_generator = DataGenerator(test_data, **params_training)
if mode == 'test':
return test_generator
else:
return training_generator, validation_generator
def _split_data(args, save_dir):
df = pd.read_csv(args['input_csv'], low_memory=False)
# df = df[(df.trace_category == 'earthquake_local') & (df.source_distance_km.astype(float) < 110)
# & (df.snr_db.str.strip("[]").str.split().str[0].astype(float) > 45)
# & (df.snr_db.str.strip("[]").str.split().str[1].astype(float) > 45)
# & (df.snr_db.str.strip("[]").str.split().str[2].astype(float) > 45)
# & (df.source_depth_km.notna())]
df = df[(df.trace_category == 'earthquake_local') & (df.source_distance_km.astype(float) < 110)
& (df.snr_db.str.strip("[]").str.split().str[0].astype(float) > 20)
& (df.snr_db.str.strip("[]").str.split().str[1].astype(float) > 20)
& (df.snr_db.str.strip("[]").str.split().str[2].astype(float) > 20)
& (df.source_depth_km.notna())]
# df = df[(df.trace_category == 'earthquake_local') & (df.source_distance_km.astype(float) < 110)
# & (df.snr_db.str.strip("[]").str.split().str[0].astype(float) < 20)
# & (df.snr_db.str.strip("[]").str.split().str[1].astype(float) < 20)
# & (df.snr_db.str.strip("[]").str.split().str[2].astype(float) < 20)
# & (df.snr_db.str.strip("[]").str.split().str[0].astype(float) > 5)
# & (df.snr_db.str.strip("[]").str.split().str[1].astype(float) > 5)
# & (df.snr_db.str.strip("[]").str.split().str[2].astype(float) > 5)
# & (df.source_horizontal_uncertainty_km.notna())
# & (df.source_horizontal_uncertainty_km.astype(float) < 2)
# & (df.source_depth_km.notna())]
# df = df[(df.trace_category == 'earthquake_local') & (df.source_distance_km.astype(float) > 110)
# & (df.snr_db.str.strip("[]").str.split().str[0].astype(float) > 30)
# & (df.snr_db.str.strip("[]").str.split().str[1].astype(float) > 30)
# & (df.snr_db.str.strip("[]").str.split().str[2].astype(float) > 30)
# & (df.source_horizontal_uncertainty_km.notna())
# & (df.source_horizontal_uncertainty_km.astype(float) < 2)
# & (df.source_depth_km.notna())]
ev_list = df.trace_name.tolist()
np.random.shuffle(ev_list)
training = ev_list[:int(args['train_valid_test_split'][0] * len(ev_list))]
# np.random.shuffle(training)
validation = ev_list[int(args['train_valid_test_split'][0] * len(ev_list)):
int(args['train_valid_test_split'][0] * len(ev_list) + args['train_valid_test_split'][1] * len(
ev_list))]
test = ev_list[
int(args['train_valid_test_split'][0] * len(ev_list) + args['train_valid_test_split'][1] * len(ev_list)):]
# np.save(save_dir + '/test', test)
return training, validation, test