#  Begin METADATA documentation
------------------------------------------------------------------------------------------------------
#  DATASET_NAME:	hydrochem.mlcalt30sig.gsg.txt
#  DATASET_DESCRIPTION:	signature file used for maximum likelihood classifiaction to create hydrochemical regions of the glacial aquifer system
#			
#  DATA_SET FORMAT: ascii-text	
#  DATA_SET URL:
#
#-------------------------------------------------------------------------------------------------------- 
#  REPORT TITLE: Hydrochemical Regions of the Glacial Aquifer System, Northern
#                United States, and Their Environmental and Water-Quality 
#                Characteristics	
# 
#  REPORT SERIES/NUMBER: USGS Scientific Investigations Report 2008-5015     
#  REPORT URL:
#
#-------------------------------------------------------------------------------------------------------
#  NAWQA STUDY:	Principal Aquifer: Glacial aquifer system	
#
#-------------------------------------------------------------------------------------------------------
#
#  METADATA DATE:    August 21, 2008	
#  METADATA CONTACT: Terri Arnold	
#                    U.S. Geological Survey
#                    Illinois Water Science Center	
#                    1201 W University Ave, Suite 100	
#	 	     Urbana, IL 61801
#	
#                    	voice: (217)328-9702
#                    	fax:   (217)344-0082
#                    	email:  tlarnold@usgs.gov
#
#  SECONDARY CONTACT:  Kelly Warner
#                      Same address as above
#                    	
#                    	voice: (217)328-9739
#                    	fax:   (217)344-0082
#                    	email: klwarner@usgs.gov  
#
# ------------------------------------------------------------------------------------------------------
#  METADATA Data Description:  
#
#  ABSTRACT:  The information contained in this file is the signature or 
#       index file used in maximum likelihoon (supervised) classification (MLC)
#       to map hydrochemical regions of the glacial aquifer system based 
#       on clustered wells with similar water quality conditions and 
#       select environmental ancillary variables (listed in header of 
#       signature file). Environmental characteristics of each cluster 
#       were identified when the wells were spatially overlaid with the 
#       combined layers of environmental characteristics, creating the 
#       training sample. Multivariate statistics describing environmental 
#       characteristics for the training sample were stored in a text file 
#       called a signature or index file that the MLC then used to classify 
#       the glacial aquifer system into hydrochemical regions. The 
#       hydrochemical regions have the highest probability of matching 
#       the environmental characteristics of the corresponding cluster 
#       groups (the training sample).  See methods section in the published 
#       report SIR2008-5015 for details.
#       The data is used in SIR2008-5015 to characterize 
#       Hydrochemical regions of the Glacial Principal Aquifer 
#       as part of the U.S. Geological Survey, National Water-Quality 
#       Assessment Program.	
#
#  Basic documentation of dataset elements and history
#
#   Entity_and_Attribute_Overview:
#       The signature file is presented as it came out of ESRI ArcGIS 9.2
#
# Related_Spatial_and_Tabular_Data_Sets:  
#        hydrochem.gdb a spatial geodatabase that contains all of the 
#        layers used to create the signature file and the final 
#        hydrochemical regions and confidence map.
# ------------------------------------------------------------------------------------------------------
#
#  End METADATA documentation
#
#  BEGIN DATA: 
# ------------------------------------------------------------------------------------------------------
#	Signatures	Produced	by	ClassSig	from
#	Class-Grid	c:\docume~1\tlarnold\locals~1\temp\raster2			
#	and	Stack	c:\docume~1\tlarnold\locals~1\temp\tmp0000		
#	Number	of	selected	grids	
/*	9				
#	Layer-Number	Grid-name			
/*	1	precip	--	average annual precipitation	
/*	2	temerature	--	average annual temperature	
/*	3	nlcde	--	land cover (1990s)	
/*	4	slope	--	percent land surface slope	
/*	5	perg	--	vertical permeability	
/*	6	clay	--	percent clay in soil	
/*	7	surftxt	--	texture of surficial deposits	
/*	8	surfdep	--	type of surficial deposits	
/*	9	recharge	--	recharge	

#	Type	Number of classes	Number of layers	nmber of parametric layers						
	1	5	9	9						
#	===================================================================									

#	Class ID	Number of Cells	Class Name							
	1	262								
#	Layers	1	2	3	4	5	6	7	8	9
#	Means									
		90.04635	9.15681	7.23282	0.82109	2.34872	22.0534	1.45802	7.72137	3.45074
#	Covariance									
	1	354.79029	21.90598	-1.45289	-1.60016	-22.63965	94.94445	-5.26156	-3.58408	19.48187
	2	21.90598	4.12829	0.17267	-0.10613	-3.26526	10.00033	-0.35358	-0.63549	0.31036
	3	-1.45289	0.17267	3.40536	-0.10066	-0.11184	0.97841	-0.03425	0.08045	-0.48279
	4	-1.60016	-0.10613	-0.10066	0.9287	-0.27604	-0.78155	0.09555	0.00798	0.31532
	5	-22.63965	-3.26526	-0.11184	-0.27604	7.72476	-19.01628	0.51892	0.52747	-0.51358
	6	94.94445	10.00033	0.97841	-0.78155	-19.01628	74.24506	-1.75745	-3.84587	0.25027
	7	-5.26156	-0.35358	-0.03425	0.09555	0.51892	-1.75745	0.45608	-0.41213	-0.21599
	8	-3.58408	-0.63549	0.08045	0.00798	0.52747	-3.84587	-0.41213	4.0715	0.13129
	9	19.48187	0.31036	-0.48279	0.31532	-0.51358	0.25027	-0.21599	0.13129	3.12956
#	-------------------------------------------------------------------									

#	Class ID	Number of Cells	Class Name							
	2	203								
#	Layers	1	2	3	4	5	6	7	8	9
#	Means									
		91.8937	8.74915	4.83744	1.0032	4.29985	16.02021	1.67488	7.39409	4.65653
#	Covariance									
	1	366.03386	10.50815	-12.88598	2.79259	-10.57403	39.57879	-2.17348	-16.54902	35.76263
	2	10.50815	3.12962	-0.01205	0.05423	-3.23924	6.88727	-0.31766	-1.90502	0.02026
	3	-12.88598	-0.01205	8.19622	0.49213	-2.60002	4.53718	-0.28084	1.72775	-1.43348
	4	2.79259	0.05423	0.49213	2.7187	-0.34704	-2.36504	0.03671	-0.02942	1.47878
	5	-10.57403	-3.23924	-2.60002	-0.34704	16.14415	-31.29986	0.92624	2.09523	1.09795
	6	39.57879	6.88727	4.53718	-2.36504	-31.29986	90.85884	-2.4066	-5.9322	-5.68779
	7	-2.17348	-0.31766	-0.28084	0.03671	0.92624	-2.4066	0.43833	0.0446	0.22067
	8	-16.54902	-1.90502	1.72775	-0.02942	2.09523	-5.9322	0.0446	7.47759	-0.50325
	9	35.76263	0.02026	-1.43348	1.47878	1.09795	-5.68779	0.22067	-0.50325	7.65934
#	-------------------------------------------------------------------									

#	Class ID	Number of Cells	Class Name							
	3	236								
#	Layers	1	2	3	4	5	6	7	8	9
#	Means									
		90.9975	7.50227	6.41949	1.19491	6.43219	11.72993	1.92373	7.20339	5.55085
#	Covariance									
	1	485.86609	29.89292	-14.73141	13.27535	-37.87518	35.41077	-1.5361	-18.88724	70.45246
	2	29.89292	4.43281	-0.75096	0.30533	-3.39037	6.5865	0.13548	-2.66899	3.12985
	3	-14.73141	-0.75096	6.21902	-0.45379	0.29346	1.58517	-0.17638	1.01219	-3.3103
	4	13.27535	0.30533	-0.45379	2.98548	-2.2955	1.74999	-0.12481	-0.14746	2.37654
	5	-37.87518	-3.39037	0.29346	-2.2955	18.97737	-31.88564	0.42739	2.97295	-3.63091
	6	35.41077	6.5865	1.58517	1.74999	-31.88564	84.6769	-0.74756	-6.47681	-3.60399
	7	-1.5361	0.13548	-0.17638	-0.12481	0.42739	-0.74756	0.36863	-0.70783	-0.24261
	8	-18.88724	-2.66899	1.01219	-0.14746	2.97295	-6.47681	-0.70783	7.12441	-1.40234
	9	70.45246	3.12985	-3.3103	2.37654	-3.63091	-3.60399	-0.24261	-1.40234	14.10574
#	-------------------------------------------------------------------									

#	Class ID	Number of Cells	Class Name							
	4	234								
#	Layers	1	2	3	4	5	6	7	8	9
#	Means									
		66.80368	8.96241	6.95299	0.98587	3.20357	19.72603	1.91026	7.67521	1.738
#	Covariance									
	1	791.94663	2.25739	-10.20774	-7.10054	-31.50526	176.45764	-11.42303	-30.69444	47.25977
	2	2.25739	5.01666	-0.2832	0.66961	-1.15104	1.48825	0.01048	-0.93	0.74232
	3	-10.20774	-0.2832	4.66302	-0.08788	-0.27175	0.582	-0.00853	0.65849	-0.90473
	4	-7.10054	0.66961	-0.08788	2.00005	-0.34767	-3.65921	0.1684	0.49459	0.38267
	5	-31.50526	-1.15104	-0.27175	-0.34767	12.12326	-30.33468	0.82024	3.02058	-0.10161
	6	176.45764	1.48825	0.582	-3.65921	-30.33468	126.15587	-3.6847	-13.64906	1.12997
	7	-11.42303	0.01048	-0.00853	0.1684	0.82024	-3.6847	0.80307	-0.23957	-0.54171
	8	-30.69444	-0.93	0.65849	0.49459	3.02058	-13.64906	-0.23957	8.47775	-1.10709
	9	47.25977	0.74232	-0.90473	0.38267	-0.10161	1.12997	-0.54171	-1.10709	5.22165
#	-------------------------------------------------------------------									

#	Class ID	Number of Cells	Class Name							
	5	223								
#	Layers	1	2	3	4	5	6	7	8	9
#	Means									
		118.16881	9.12186	4.88341	2.4524	6.39893	7.08863	2.13901	6.4843	12.02924
#	Covariance									
	1	318.97003	13.10541	-13.80723	4.71796	-3.95105	-16.78591	1.42455	-8.23925	77.92764
	2	13.10541	2.78854	-0.72937	-1.75976	0.50205	0.3191	0.49534	-0.73003	2.76904
	3	-13.80723	-0.72937	7.59896	0.409	-0.77844	3.24687	-0.13237	-0.35319	-3.34537
	4	4.71796	-1.75976	0.409	11.60424	-2.22633	0.11842	-0.26364	0.46168	1.17846
	5	-3.95105	0.50205	-0.77844	-2.22633	15.4963	-12.96673	-0.19431	-1.92596	-1.23957
	6	-16.78591	0.3191	3.24687	0.11842	-12.96673	31.48011	0.08576	2.02602	-4.94296
	7	1.42455	0.49534	-0.13237	-0.26364	-0.19431	0.08576	0.36347	0.063	0.64928
	8	-8.23925	-0.73003	-0.35319	0.46168	-1.92596	2.02602	0.063	9.1698	-0.45535
	9	77.92764	2.76904	-3.34537	1.17846	-1.23957	-4.94296	0.64928	-0.45535	29.17932
