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		Fast double-difference cross-correlation (FDTCC)
						Authors: Min Liu & Miao Zhang
						mliu@cuhk.edu.hk & miao.zhang@dal.ca
1.Usage
	FDTCC -C(ife/ifd/ifp) -W(wb/wa/wf/wbs/was/wfs) -D(delta/threshold/thre_SNR/thre_shift) -G(trx/trh/tdx/tdh) 
	-B(low/high) -F(f)
--------------------------------------explanation----------------------------------------
	-C: specify the path of event.sel, dt.ct and phase.dat (1: yes, 0: default names)
	-W: waveform window length before and after picks and their maximum shift length
	-D: sampling interval, CC threshold, SNR threshold, maximum abs(t1-t2) of the two picks
	-G: ranges and grids in horizontal direction and depth (in traveltime table)
	-F: input data format (0: continuous data; 1: event segments)
	-B: waveform bandpass filtering (e.g., 2/8; -1/-1: no filter applied).
  	   	SAC name format: date/net.sta.comp, e.g., 20210101/AA.BBBB.HHZ
                       or eventID/net.sta.comop, e.g., 8/AA.BBBB.HHZ).
	staDir: stations directory
   		tttDir: travel-time directory
   		wavDir: waveform directory
   		eveDir: event.sel (optional)
   		dctDir: dt.ct (optional)       
	paDir:	phast.dat (optional)
2.Input file
	phase.dat		# in hypoDD format
	event.sel 		# in hypoDD format
	dt.ct     		# in hypoDD format
	station.dat		# in REAL format
	waveform data		# raw data
	travel-time table	# in REAL format

3.Outfile
	dt.cc			# can be used in hypoDD, Growclust and tomoDD directly

4.Demo
	$bash runFCC.sh
	
If you use FDTCC in research that you submit, please cite the two papers:
	1) Multistage Nucleation of the 2021 Yangbi MS 6.4 Earthquake and Its Foreshocks, Journal of Geophysical Research: Solid Earth, 2022, https://doi.org/10.1029/2022JB024091.
	2) Investigation of the 2013 Eryuan, Yunnan, China MS 5.5 Earthquake Sequence: Aftershock Migration, Seismogenic Structure and Hazard Implication, Tectonophysics, 2022, https://doi.org/10.1016/j.tecto.2022.229445

FDTCC is integrated into the earthquake detection and location workflow - LOC-FLOW, more details please see our SRL paper:  
	LOC-FLOW: An End-to-End Machine-Learning-Based High-Precision Earthquake Location Workflow, Seismological Research Letters, 2022, https://doi.org/10.1785/0220220019

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