def load_seismic(filename, inlines, xlines):
"""
Load seismic amplitudes from .SEGY into numpy array.
Parameters
----------
filename : str
File pathname for .SEGY file.
inlines : list
List in form [start, stop, step] for data inlines.
xlines : list
List in form [start, stop, step] for data inlines.
Returns
-------
amplitude : array_like
Array of seismic amplitudes.
twt : array_like
Array of twt range for data.
"""
inl = np.arange(*inlines)
crl = np.arange(*xlines)
seis, header, trace_headers = segypy.readSegy(filename)
amplitude = seis.reshape(header['ns'], inl.size, crl.size)
lagtime = trace_headers['LagTimeA'][0] * -1
twt = np.arange(lagtime, header['dt'] / 1e3 * header['ns'] + lagtime,
header['dt'] / 1e3)
return amplitude, twt
def getData(dataDir):
'''
read segy npy txt
:param dataDir: file path
:return: data set
'''
fileList = os.listdir(dataDir) # 获取目标文件夹所有文件名的列表
Profile = []
for se in fileList:
if se.endswith(".npy"):
path = os.path.join(dataDir, se)
data = np.load(path)
Profile.append(data)
if se.endswith(".sgy"):
path = os.path.join(dataDir, se)
# Read Segy File
segypy.verbose = 1
[data, SH, STH] = segypy.readSegy(path, endian='>')
Profile.append(data)
if se.endswith(".txt"):
path = os.path.join(dataDir, se)
data = np.loadtxt(path)
Profile.append(data)
print("_____________Data loaded_____________")
return Profile
def read_segy_file(FileName):
filename = FileName
# Set verbose level
segypy.verbose = 1
SH = segypy.getSegyHeader(filename)
#%% Read Segy File
[Data, SH, STH] = segypy.readSegy(filename)
print('数组大小：', np.shape(Data))
return Data, SH, STH
def load_seismic(filename, inlines=[1300, 1502, 2], xlines=[1500, 2002, 2]):
inl = np.arange(*inlines)
crl = np.arange(*xlines)
seis, header, trace_headers = segypy.readSegy(filename)
amplitude = seis.reshape(header['ns'], inl.size, crl.size)
lagtime = trace_headers['LagTimeA'][0] * -1
twt = np.arange(lagtime, header['dt'] / 1e3 * header['ns'] + lagtime,
header['dt'] / 1e3)
return amplitude, twt
def load_segy(filename, min_inline, max_inline, step_inline, min_xline, max_xline, step_xline):
# Get only SegyHeader
SH = segypy.getSegyHeader(filename);
inl=np.arange(min_inline, max_inline, step_inline)
crl=np.arange(min_xline, max_xline, step_xline)
seis, header, trace_headers = segypy.readSegy(filename)
stack = seis.reshape(header['ns'], inl.size, crl.size)
return stack
def load_seismic(fname,
min_inline,
max_inline,
step_inline,
min_xline,
max_xline,
step_xline,
scaling=1e3):
inl = np.arange(min_inline, max_inline, step_inline)
crl = np.arange(min_xline, max_xline, step_xline)
seismic, header, trace_headers = segypy.readSegy(fname)
seismic = seismic.reshape(header['ns'], inl.size, crl.size)
lagtime = trace_headers['LagTimeA'][0] * -1
twt = np.arange(lagtime, header['dt'] / scaling * header['ns'] + lagtime,
header['dt'] / scaling)
return seismic, twt
import matplotlib as mpl
mpl.use('TKAgg')
import matplotlib.pyplot as plt
import segypy
import numpy as np
from sklearn.svm import SVR #调用支持向量机的分类回归函数SVR(支持向量回归)
filename = 'Model+GathEP-Z.sgy'
# Set verbose level
segypy.verbose = 1
SH = segypy.getSegyHeader(filename)
#%% Read Segy File
[Data, SH, STH] = segypy.readSegy(filename)
print(SH)
print(np.shape(Data))
RowNum, ColumNum = np.shape(Data) #获取模拟地震记录的行列数
print('数据的行数：', RowNum)
print('数据的列数：', ColumNum)
#Generator Random Noise
GaussianNoise = np.random.normal(0, 0.3, (RowNum, ColumNum)) #高斯白噪声(均值0，方差0.3)
# Generate sample data
FirstColumNoise = GaussianNoise[:, 0] #第一道白噪声
plt.figure(6)
plt.plot(FirstColumNoise) #显示第一道高斯白噪声
print('第一道噪声shape:', np.shape(FirstColumNoise))
NumberOfDots = int(np.shape(FirstColumNoise)[0]) #获得第一道噪音的长度（点数）
print('第一道噪声的长度（点数）：', NumberOfDots)
#!/usr/bin/env python # testsegy.py import segypy filename='ld0077_file_0126.sgy' filename='shotgather.sgy' # Set verbose level segypy.verbose=1 #%% Read only SegyHeader and SegyTraceHeader SH = segypy.getSegyHeader(filename) STH = segypy.getAllSegyTraceHeaders(SH) #%% Read Segy File [Data,SH,STH]=segypy.readSegy(filename) #%% Plot Segy filwe scale=1e-9 # wiggle plot segypy.wiggle(Data,SH,1e-9) # image plot segypy.image(Data,SH,scale)
import segypy filename = './data/L12S.sgy' #filename='mini.sgy' #filename='data_IEEE.segy'; #filename='data_IBM_REV1.segy'; #filename='data_IBM_REV0.segy'; #filename='data_1byteINT.segy'; #filename='data_2byteINT.segy'; #filename='data_4byteINT.segy'; # Set verbose level segypy.verbose = 1 # Read Segy File Data, SH, STH = segypy.readSegy(filename, '<') import pylab #imshow(Data) pylab.figure(1) pylab.imshow(Data) pylab.show() exit(0) filename_out = 'testout.segy' SH['DataSampleFormat'] = 5 SH['SegyFormatRevisionNumber'] = 100 segypy.writeSegyStructure(filename_out, Data, SH, STH) exit(0) segypy.wiggle(Data, SH, 2, .1, .1)
noiseList = []
residualList = []
i = 0
# load test data
for seisData in os.listdir(os.path.join(args.set_dir, set_cur)):
if seisData.endswith(".txt"):
test_data = np.loadtxt(
os.path.join(args.set_dir, set_cur, seisData))
if seisData.endswith(".npy"):
test_data = np.load(
os.path.join(args.set_dir, set_cur, seisData))
if seisData.endswith("sgy"):
[test_data, SH,
STH] = segypy.readSegy(os.path.join(args.set_dir, set_cur,
seisData),
endian='>')
test_data = np.array(test_data)
print(np.shape(test_data))
data_size = np.shape(test_data)
# to save result and noise profile
result = np.zeros(np.shape(test_data))
noiseProfile = np.zeros(np.shape(test_data))
# normalization
Norm = Sample_maker_forDN.Normalization_Func(test_data)
test_data = Norm.Normalization_perTrace()
# test_data = Norm.standardization()
seisDataList.append(test_data)
# import struct import segypy filename='ld0057_file_00095.sgy'; #filename='mini.sgy' #filename='data_IEEE.segy'; #filename='data_IBM_REV1.segy'; #filename='data_IBM_REV0.segy'; #filename='data_1byteINT.segy'; #filename='data_2byteINT.segy'; #filename='data_4byteINT.segy'; # Read Segy File Data,SH,STH=segypy.readSegy(filename); exit filename_out='testout.segy'; SH['DataSampleFormat']=5; SH['SegyFormatRevisionNumber']=100; segypy.writeSegyStructure(filename_out,Data,SH,STH); segypy.wiggle(Data,SH,2,.1,.1) f_ieee='data_IEEE.segy'; f_ibm='data_IBM_REV1.segy';
from keras.utils import plot_model from keras.optimizers import Adam from PIL import Image import tensorflow from IPython.display import SVG from keras.utils.vis_utils import model_to_dot import random os.chdir(r'R:\mowais\SPROJ') # Set verbose level segypy.verbose = 1 # Raw Data filename3 = 'Beg_IL20-100andXL1-110.sgy' SH = segypy.getSegyHeader(filename3) [Raw, SH, STH] = segypy.readSegy(filename3) #Enhanced Data filename1 = 'beg_export - TensorBasedGeometric - Enhanced Coherence.sgy' SH = segypy.getSegyHeader(filename1) [Data, SH, STH] = segypy.readSegy(filename1) #Min-to-Max Scaling maxy = np.max(Raw) mini = np.min(Raw) RawSD = (Raw - mini) / (maxy - mini) maxy = np.max(Data) mini = np.min(Data) DataSD = (Data - mini) / (maxy - mini)