コード例 #1
0
ファイル: fk.py プロジェクト: s-schneider/FK-Toolbox
def fk_reconstruct(st, slopes=[-10,10], deltaslope=0.05, slopepicking=False, smoothpicks=False, dist=0.5, maskshape=['boxcar',None], 
					method='denoise', solver="iterative",  mu=5e-2, tol=1e-12, fulloutput=False, peakinput=False, alpha=0.9):
	"""
	This functions reconstructs missing signals in the f-k domain, using the original data,
	including gaps, filled with zeros, and its Mask-array (see makeMask, and slope_distribution.
	If all traces are avaiable it is a useful method to de-noise the data.
	Uses the following cost function to minimize:

			J = ||dv - T FHmtx2D Yw Dv ||^{2}_{2} + mu^2 ||Dv||^{2}_{2}
			
			J := Cost function
			dv:= Column-wise-ordered long vector of the 2D signal d (columns: t-domain, rows: x-domain)
			DV:= Column-wise-ordered long vector of the	f-k-spectrum D ( columns: f-domain, rows: k-domain)
			Yw := Diagonal matrix built from the column-wise-ordered long vector of Mask
			T := Sampling matrix which maps the fully sampled desired seismic data to the available samples.
				 For de-noising problems T = I (identity matrix)
			mu := Trade-off parameter between misfit and model norm


	Minimizing is done via a method of the LSMR solver, de-noising (1-2 iterations), reconstruction(8-10) iterations.
	T FHmtx2D Yw Dv will be formed to one matrix A, so at the end the equation system that will be solved has the form:
			
							|   A    |		  | dv |
							|    	 | * Dv = |    |
							| mu * I |		  | 0  |


	:param st: Stream with missing traces, to be reconstructed or complete stream to be de-noised
	:type  st: obspy.core.stream.Stream

	:param slopes: Range of slopes to investigate for mask-function
	:type  slopes: list

	:param deltaslope: stepsize inbetween slopes.
	:type  deltaslope: float

	:param slopepicking: If True peaks of slopedistribution can be picked by hand.
	:type  slopepicking: bool

	:param smoothpicks: Determines the smoothing of the Slopedistribution, default off. If enabled the distribution ist smoothened by
						convoluting it with a boxcar of size smoothpicks.
	:type  smoothpicks: int

	:param dist: Minimum distance inbetween maximum picks.
	:type  dist: float

	:param maskshape: maskshape[0] describes the shape of the lobes of the mask. Possible inputs are:
				 -boxcar (default)
				 -taper
				 -butterworth

				  maskshape[1] is an additional attribute to the shape of taper and butterworth, for:
				 -taper: maskshape[1] = slope of sides
				 -butterworth: maskshape[1] = number of poles
				
				 e.g.: maskshape['taper', 2] produces a symmetric taper with slope of side = 2.


	:type  maskshape: list

	:param method: Desired fk-method, options are 'denoise' and 'interpolate'
	:type  method: string

	:param solver: Solver used for method. Options are 'lsqr' and 'iterative'.
				   If method is 'denoise' only the iterative solver is used.
	:type  solver: string
	
	:param mu:	Damping parameter for the solver
	:type  mu:	float

	:param tol: Tolerance for solver to abort iteration.
	:type  tol: float

	:param fulloutput: If True, the function additionally outputs FH, dv, Dv, Ts and Yw
	:type  fulloutput: bool

	:param peakinput: Chosen peaks of the distribution, insert here if the peaks are not to be meant to recalculated
	:type  peakinput: np.ndarray

	######  returns:

	:param st_rec: Stream with reconstructed signals on the missing traces
	:type  st_rec: obspy.core.stream.Stream
	
	## if fulloutput=True

	:param st_rec: Stream with reconstructed signals on the missing traces
	:type  st_rec: obspy.core.stream.Stream

	:param FH: 2DiFFT-matrix for column-wise ordered longvector of the f-k spectrum
	:type  FH: scipy.sparse.csc.csc_matrix

	:param dv: Column-wise ordered longvector of the t-x data
	:type  dv: numpy.ndarray
	
	:param Dv: Column-wise ordered longvector of the f-k spectrum of dv
	:type  Dv: numpy.ndarray

	:param Ts: Sampling-matrix, which maps desired to available data
	:type  Ts: scipy.sparse.dia.dia_matrix

	:param Yw: Diagonal matrix constructed of the column-wise ordered longvector of the mask-matrix
	:type  Yw: scipy.sparse.dia.dia_matrix

	Example:
				from obspy import read as read_st
				import sipy
				
				stream = read_st("../data/synthetics_uniform/SUNEW.QHD")

				#Example around PP.
				stream_org = st.copy()
				d = sipy.util.array_util.stream2array(stream_org)
				ArrayData = np.zeros((d.shape[0], 300))
				for i, trace in enumerate(d):
					ArrayData[i,:]=trace[400:700]
				stream = sipy.util.array_util.array2stream(ArrayData, stream_org)
	
				dssa = sipy.filter.fk.fk_reconstruct(stream, mu=5e-2, method='interpolate')
				
				stream_ssa = sipy.util.array_util.array2stream(dssa, stream)

				sipy.util.fkutil.plot(stream_ssa)

	Author: S. Schneider, 2016
	Reference:	Mostafa Naghizadeh, Seismic data interpolation and de-noising in the frequency-wavenumber
				domain, 2012, GEOPHYSICS
	"""

	# Prepare data.
	st_tmp 		= st.copy()
	ArrayData	= stream2array(st_tmp, normalize=False)
	ADT 		= ArrayData.copy().transpose()

	fkData 		= np.fft.fft2(ArrayData)
	fkDT 		= np.fft.fft2(ADT)

	# Look for missing Traces
	recon_list 	= []

	for i, trace in enumerate(st_tmp):
		try:
			if trace.stats.zerotrace == 'True':
				recon_list.append(i)
		except AttributeError:
			if sum(trace.data) == 0. :
				recon_list.append(i)
		except:
			continue
	print(recon_list)

	# Calculate mask-function W.
	try:	
		if peakinput.any():
			peaks = peakinput
	except:
		print("Calculating slope distribution...\n")
		M, prange, peaks = slope_distribution(fkData, slopes, deltaslope, peakpick=None, mindist=dist, smoothing=smoothpicks, interactive=slopepicking)
		if fulloutput:
			kin = 'n'
			while kin in ('n', 'N'):
				plt.figure()
				plt.title('Magnitude-Distribution')
				plt.xlabel('Slope in fk-domain')
				plt.ylabel('Magnitude of slope')
				plt.plot(prange, M)
				plt.plot(peaks[0], peaks[1]/peaks[1].max()*M.max(), 'ro')
				plt.show()
				kin = raw_input("Use picks? (y/n) \n")
				if kin in ['y' , 'Y']:
					print("Using picks, continue \n")
				elif kin in ['n', 'N']:
					print("Don't use picks, please re-pick \n")
					M, prange, peaks = slope_distribution(fkData, slopes, deltaslope, peakpick=None, mindist=dist, smoothing=smoothpicks, interactive=True)
	
	print("Creating mask function with %i significant linear events \n" % len(peaks[0]) )
	W = makeMask(fkData, peaks[0], maskshape)

	# If fulloutput is desired, a bunch of messages and user interaction appears.
	if fulloutput:
		plt.figure()
		plt.subplot(3,1,1)
		plt.gca().yaxis.set_major_locator(plt.NullLocator())
		plt.gca().xaxis.set_major_locator(plt.NullLocator())
		plt.title("fk-spectrum")
		plt.imshow(abs(np.fft.fftshift(fkData)), aspect='auto', interpolation='none')
		plt.subplot(3,1,2)
		plt.gca().yaxis.set_major_locator(plt.NullLocator())
		plt.gca().xaxis.set_major_locator(plt.NullLocator())
		plt.title("Mask-function")
		plt.imshow(np.fft.fftshift(W), aspect='auto', interpolation='none')
		plt.subplot(3,1,3)
		plt.gca().yaxis.set_major_locator(plt.NullLocator())
		plt.gca().xaxis.set_major_locator(plt.NullLocator())
		plt.title("Applied mask-function")
		plt.imshow(abs(np.fft.fftshift(W*fkData)), aspect='auto', interpolation='none')
		plt.show()
		kin = raw_input("Use Mask? (y/n) \n")
		if kin in ['y' , 'Y']:
			print("Using Mask, continue \n")
		elif kin in ['n', 'N']:
			msg="Don't use Mask, exit"
			raise IOError(msg)

	# Checking for number of iteration and reconstruction behavior.
	maxiter=None
	interpol = False
	if isinstance(method, str):
		if method in ("denoise"):
				maxiter = 2
				recon_list = []
		elif method in ("interpolate"):
				maxiter = 10
				interpol = True

	elif isinstance(method, int):
		maxiter=method

	print("maximum %i" %maxiter)
	if solver in ("lsqr", "leastsquares", "ilsmr", "iterative", "cg", "fmin"):
		pocs = False
		# To keep the order it would be better to transpose W to WT
		# but for creation of Y, WT has to be transposed again,
		# so this step can be skipped.
		Y 	= W.reshape(1,W.size)[0]
		Yw 	= sparse.diags(Y)

		# Initialize arrays for cost-function.
		dv 	= ADT.transpose().reshape(1, ADT.size)[0]
		Dv	= fkDT.transpose().reshape(1, fkDT.size)[0]
	
		T = np.ones((ArrayData.shape[0], ArrayData.shape[1]))
		T[recon_list] = 0.
		T = T.reshape(1, T.size)[0]

		Ts = sparse.diags(T)
	

		# Create sparse-matrix with iFFT operations.	
		print("Creating iFFT2 operator as a %ix%i matrix ...\n" %(fkDT.shape[0]*fkDT.shape[1], fkDT.shape[0]*fkDT.shape[1]))	

		FH = create_iFFT2mtx(fkDT.shape[0], fkDT.shape[1])
		print("... finished\n")

		# Create model matrix A.
		print("Creating sparse %ix%i matrix A ...\n" %(FH.shape[0], FH.shape[1]))	
		A =  Ts.dot(FH.dot(Yw))
		print("Starting reconstruction...\n")

		if solver in ("lsqr", "leastsquares"):
			print(" ...using iterative least-squares solver...\n")
			x = sparse.linalg.lsqr(A, dv, mu, atol=tol, btol=tol, conlim=tol, iter_lim=maxiter)
			print("istop = %i \n" % x[1])
			print("Used iterations = %i \n" % x[2])
			print("residual Norm ||x||_2 = %f \n " % x[8])
			print("Misfit ||Ax - b||_2= %f \n" % x[4]) 
			print("Condition number = %f \n" % x[6])
			
			Dv_rec = x[0]

		elif solver in ("ilsmr", "iterative"):
			print(" ...using iterative LSMR solver...\n")
			x = sparse.linalg.lsmr(A,dv,mu, atol=tol, btol=tol, conlim=tol, maxiter=maxiter)
			print("istop = %i \n" % x[1])
			print("Used iterations = %i \n" % x[2])
			print("Misfit = %f \n " % x[3])
			print("Modelnorm = %f \n" % x[4])
			print("Condition number = %f \n" % x[5])
			print("Norm of Dv = %f \n" % x[6]) 
			Dv_rec = x[0]

		elif solver in ("cg"):
			A 		= Ts.dot(FH.dot(Yw))
			Ah 		= A.conjugate().transpose()
			madj 	= Ah.dot(dv)
			E 		= mu * sparse.eye(A.shape[0])
			B 		= A + E
			Binv 	= sparse.linalg.inv(B)
			x 		= sparse.linalg.cg(Binv, madj, maxiter=maxiter)
			Dv_rec 	= x[0]

		elif solver in ('fmin'):
			A 		= Ts.dot(FH.dot(Yw))
			global arg1
			global arg2
			global arg3
			arg1 = dv
			arg2 = A
			arg3 = mu

			def J(x):
				COST = np.linalg.norm(arg1 - arg2.dot(x), 2)**2. + arg3*np.linalg.norm(x,2)**2.
				return COST 

			Dv_rec = sp.optimize.fmin_cg(J, x0=Dv, maxiter=10)			

		data_rec = np.fft.ifft2(Dv_rec.reshape(fkData.shape)).real

	elif solver in ("pocs"):
		pocs=True
		threshold = abs( (fkData*W.astype('complex').max()) ) 

		for i in range(maxiter):
			data_tmp 								= ArrayData.copy()
			fkdata 									= np.fft.fft2(data_tmp) * W.astype('complex')
			fkdata[ np.where(abs(fkdata) < threshold)] 	= 0. + 0j
			threshold = threshold * alpha
			#if i % 10 == 0.:
			#	plt.imshow(abs(fkdata), aspect='auto', interpolation='none')
			#	plt.savefig("%s.png" % i)
			data_tmp 								= np.fft.ifft2(fkdata).real.copy()
			ArrayData[recon_list] 					= data_tmp[recon_list]
	
		data_rec = ArrayData.copy()
	else:
		print("No solver or method specified.")
		return

	

	if interpol:
		st_rec = st.copy()
		for i in recon_list:
			st_rec[i].data = data_rec[i,:]
			st_rec[i].stats.zerotrace = 'reconstructed'


	else:
		st_rec = array2stream(data_rec, st)

	if fulloutput and not pocs:
		return st_rec, FH, dv, Dv, Dv_rec, Ts, Yw, W
	else:
		return st_rec
コード例 #2
0
ファイル: fk.py プロジェクト: s-schneider/FK-Toolbox
def fk_filter(st, inv=None, event=None, ftype='eliminate', fshape=['butterworth', 4, 4], phase=None, polygon=4, normalize=True, stack=False,
					slopes=[-3,3], deltaslope=0.05, slopepicking=False, smoothpicks=False, dist=0.5, maskshape=['boxcar',None], 
					order=4., peakinput=False, eval_mean=1, fs=25):
	"""
	Import stream, the function applies an 2D FFT, removes a certain window around the
	desired phase to surpress a slownessvalue corresponding to a wavenumber and applies an 2d iFFT.
	To fill the gap between uneven distributed stations use array_util.gaps_fill_zeros(). A method to interpolate the signals in the
	fk-domain is beeing build, also a method using a norm minimization method.
	Alternative is an nonequidistant 2D Lombard-Scargle transformation.

	param st: Stream
	type st: obspy.core.stream.Stream

	param inv: inventory
	type inv: obspy.station.inventory.Inventory

	param event: Event
	type event: obspy.core.event.Event

	param ftype: type of method, default is 'eliminate-polygon', possible inputs are:
				 -eliminate
				 -extract
				 -eliminate-polygon
				 -extract-polygon
				 -mask
				 -fk

	type ftype: string

	param fshape: fshape[0] describes the shape of the fk-filter in case of ftype is 'eliminate' or 'extract'. Possible inputs are:
				 -spike (default)
				 -boxcar
				 -taper
				 -butterworth

				  fshape[1] is an additional attribute to the shape of taper and butterworth, for:
				 -taper: fshape[1] = slope of sides
				 -butterworth: fshape[1] = number of poles

				  fshape[3] describes the length of the filter shape, respectivly wavenumber corner points around k=0,
				
				 e.g.: fshape['taper', 2, 4] produces a symmetric taper with slope of side = 2, where the signal is reduced about 50% at k=+-2


	type  fshape: list

	param phase: name of the phase to be investigated
	type  phase: string

	param polygon: number of vertices of polygon for fk filter, only needed 
				   if ftype is set to eliminate-polygon or extract-polygon.
				   Default is 12.
	type  polygon: int
	
	param normalize: normalize data to 1
	type normalize: bool

	param SSA: Force SSA algorithm or let it check, default:False
	type SSA: bool

	param eval_mean: number of linear events used to calculate the average of the area in the fk domain.

	returns:	stream_filtered, the filtered stream.
			


	References: Yilmaz, Thomas

	Author: S. Schneider 2016

	 This program is free software: you can redistribute it and/or modify
	 it under the terms of the GNU General Public License as published
	 by the Free Software Foundation, either version 3 of the License, or
	 any later version.

	 This program is distributed in the hope that it will be useful,
	 but WITHOUT ANY WARRANTY; without even the implied warranty of
	 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
	 GNU General Public License for more details: http://www.gnu.org/licenses/
	"""

	# Convert format and prepare Variables.

	# Check for Data type of variables.
	if not type(st ) == Stream:
		print( "Wrong input type of stream, must be obspy.core.stream.Stream" )
		raise TypeError

	if len(fshape) ==  1:
		fshape = [fshape[0], None, None]
	
	st_tmp = st.copy()
	ArrayData = stream2array(st_tmp, normalize)
	
	ix = ArrayData.shape[0]
	iK = int(math.pow(2,nextpow2(ix)))
	
	try:
		yinfo = epidist2nparray(attach_epidist2coords(inv, event, st_tmp))
		dx = (yinfo.max() - yinfo.min() + 1) / yinfo.size
		k_axis = np.fft.fftfreq(iK, dx)	

	except:
		try:
			ymax = st_tmp[0].stats.distance
			ymin = st_tmp[0].stats.distance
			for trace in st_tmp:
				if trace.stats.distance > ymax: ymax = trace.stats.distance
				if trace.stats.distance < ymin: ymin = trace.stats.distance

			dx = (ymax - ymin + 1) / len(st_tmp)
			k_axis = np.fft.fftfreq(iK, dx)

		except:
			print("\nNo inventory or event-information found. \nContinue without specific distance and wavenumber information.")
			yinfo=None
			dx=None
			k_axis=None

	it     = ArrayData.shape[1]
	iF     = int(math.pow(2,nextpow2(it)))
	dt     = st_tmp[0].stats.delta
	f_axis = np.fft.fftfreq(iF,dt)



	# Calc mean diff of each epidist entry if it is reasonable
	# do a partial stack and apply filter.


	"""
	2D Frequency-Space / Wavenumber-Frequency Filter #########################################################
	"""

	# 2D f-k Transformation 
	# Note array_fk has f on the x-axis and k on the y-axis!!!
	# For interaction the conj.-transposed Array is shown!!!


	# Decide when to use SSA to fill the gaps, calc mean distance of each epidist entry
	# if it differs too much --> SSA


	if ftype in ("eliminate"):
		if phase:
			if not isinstance(event, Event) and not isinstance(inv, Inventory):
				msg='For alignment on phase calculation inventory and event information is needed, not found.'
				raise IOError(msg)

			st_al = alignon(st_tmp, inv, event, phase)
			ArrayData = stream2array(st_al, normalize)
			array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
			array_filtered_fk = line_set_zero(array_fk, shape=fshape)

		else:
			array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
			array_filtered_fk = line_set_zero(array_fk, shape=fshape)

	elif ftype in ("extract"):
		if phase:
			if not isinstance(event, Event) and not isinstance(inv, Inventory):
				msg='For alignment on phase calculation inventory and event information is needed, not found.'
				raise IOError(msg)

			st_al = alignon(st_tmp, inv, event, phase)
			ArrayData = stream2array(st_al, normalize)
			array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
			array_filtered_fk = line_cut(array_fk, shape=fshape)

		else:
			array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
			array_filtered_fk = line_cut(array_fk, shape=fshape)

	
	elif ftype in ("eliminate-polygon"):
		array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
		if phase:
			if not isinstance(event, Event) and not isinstance(inv, Inventory):
				msg='For alignment on phase calculation inventory and event information is needed, not found.'
				raise IOError(msg)
			st_al = alignon(st_tmp, inv, event, phase)
			ArrayData = stream2array(st_al, normalize)
			array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
			array_filtered_fk = _fk_eliminate_polygon(array_fk, polygon, ylabel=r'frequency domain f in Hz', \
													  yticks=f_axis, xlabel=r'wavenumber domain k in $\frac{1}{^{\circ}}$', xticks=k_axis, eval_mean=eval_mean, fs=fs)

		else:
			array_filtered_fk = _fk_eliminate_polygon(array_fk, polygon, ylabel=r'frequency domain f in Hz', \
													  yticks=f_axis, xlabel=r'wavenumber domain k in $\frac{1}{^{\circ}}$', xticks=k_axis, eval_mean=eval_mean, fs=fs)


	elif ftype in ("extract-polygon"):
		array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
		if phase:
			if not isinstance(event, Event) and not isinstance(inv, Inventory):
				msg='For alignment on phase calculation inventory and event information is needed, not found.'
				raise IOError(msg)

			st_al = alignon(st_tmp, inv, event, phase)
			ArrayData = stream2array(st_al, normalize)
			array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
			array_filtered_fk = _fk_extract_polygon(array_fk, polygon, ylabel=r'frequency domain f in Hz', \
												yticks=f_axis, xlabel=r'wavenumber domain k in $\frac{1}{^{\circ}}$', xticks=k_axis, eval_mean=eval_mean, fs=fs)
		else:
			array_filtered_fk = _fk_extract_polygon(array_fk, polygon, ylabel=r'frequency domain f in Hz', \
												yticks=f_axis, xlabel=r'wavenumber domain k in $\frac{1}{^{\circ}}$', xticks=k_axis, eval_mean=eval_mean, fs=fs)


	elif ftype in ("mask"):
		array_fk = np.fft.fft2(ArrayData)
		M, prange, peaks = slope_distribution(array_fk, slopes, deltaslope, peakpick=None, mindist=dist, smoothing=smoothpicks, interactive=slopepicking)
		W = makeMask(array_fk, peaks[0], maskshape)
		array_filtered_fk =  array_fk * W
		array_filtered = np.fft.ifft2(array_filtered_fk)
		stream_filtered = array2stream(array_filtered, st_original=st.copy())
		return stream_filtered, array_fk, W


	elif ftype in ("fk"):
		if phase:
			if not isinstance(event, Event) and not isinstance(inv, Inventory):
				msg='For alignment on phase calculation inventory and event information is needed, not found.'
				raise IOError(msg)

			st_al = alignon(st_tmp, inv, event, phase)
			ArrayData = stream2array(st_al, normalize)
			array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
			### BUILD DOUBLE TAPER ###
			#array_filtered_fk = 

		else:
			array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
			### BUILD DOUBLE TAPER ###
			#array_filtered_fk = 


	else:
		print("No type of filter specified")
		raise TypeError

	array_filtered = np.fft.ifft2(array_filtered_fk, s=(iK,iF)).real


	# Convert to Stream object.
	array_filtered = array_filtered[0:ix, 0:it]
	stream_filtered = array2stream(array_filtered, st_original=st.copy())

	return stream_filtered