OR/14/015 Swallow Sand - Revision history

Ajhil: /* Spatial predictions */

2022-05-04T09:32:50Z

Spatial predictions

← Older revision		Revision as of 10:32, 4 May 2022
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	Figure 2 below shows the conditional expectation of gravel, sand and mud across the Swallow Sand MCZ, and Figure 3 shows the 0.025 and 0.975 quantile which define a 95% confidence interval for mud content.		Figure 2 below shows the conditional expectation of gravel, sand and mud across the Swallow Sand MCZ, and Figure 3 shows the 0.025 and 0.975 quantile which define a 95% confidence interval for mud content.
	~~{{clear}}~~
	[[Image:14015 fig2.jpg\|thumb\|center\|500px\|'''Figure 2'''    Conditional Expectation of gravel, sand and mud content (proportions) across the Swallow Sand MCZ based on multiple realizations of the prediction distribution.]]

	[[Image:~~14015 fig3~~.jpg\|thumb\|center\|500px\|'''Figure 3'''    Upper (top) and lower (bottom) bounds of the 95% confidence interval for mud content (proportion) across the Swallow Sand MCZ based on multiple realizations of the prediction distribution.]]		[[Image:OR14015fig2.jpg\|thumb\|center\|500px\|'''Figure 2'''    Conditional Expectation of gravel, sand and mud content (proportions) across the Swallow Sand MCZ based on multiple realizations of the prediction distribution.]]
	~~{{clear}}~~
			[[Image:OR14015fig3.jpg\|thumb\|center\|500px\|'''Figure 3'''    Upper (top) and lower (bottom) bounds of the 95% confidence interval for mud content (proportion) across the Swallow Sand MCZ based on multiple realizations of the prediction distribution.]]

	SS_classes.dat dat is an ASCII format file. Each row corresponds to a node on the 250-m grid. The variables in each column of the file are tabulated below.		SS_classes.dat dat is an ASCII format file. Each row corresponds to a node on the 250-m grid. The variables in each column of the file are tabulated below.

Ajhil: /* Spatial predictions */

2022-05-04T09:32:05Z

Spatial predictions

← Older revision		Revision as of 10:32, 4 May 2022
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	Figure 4 below shows the most probable EUNIS class across the Swallow Sand MCZ, and the probability of the most probable class. Figure 5 shows the probability of each class.		Figure 4 below shows the most probable EUNIS class across the Swallow Sand MCZ, and the probability of the most probable class. Figure 5 shows the probability of each class.
	~~{{Clear}}~~
	~~[[Image:14015 fig4.jpg\|thumb\|center\|500px\|'''Figure 4'''    Most probable EUNIS class (top) across the Swallow Sand MCZ and probability of the most probable class (bottom).]]~~

	[[Image:~~14015 fig5~~.jpg\|thumb\|center\|500px\| '''Figure 5'''    Probability of finding each EUNIS class (top) across the Swallow Sand MCZ.]]		[[Image:OR14015fig4.jpg\|thumb\|center\|500px\|'''Figure 4'''    Most probable EUNIS class (top) across the Swallow Sand MCZ and probability of the most probable class (bottom).]]
	~~{{Clear}}~~
			[[Image:OR14015fig5.jpg\|thumb\|center\|500px\| '''Figure 5'''    Probability of finding each EUNIS class (top) across the Swallow Sand MCZ.]]

	Note that, while the class Sand and Muddy Sand is delineated as most probable across most of the MCZ, there is uncertainty attached to this, particularly in the south-western and central eastern parts of the zone. This reflects the subtle transitions of sediment texture shown in Figure 2. It also reflects the importance of short-range variability of sediment texture in this MCZ. The short-range variability of sediment texture is large, shown by the relative magnitude of the nugget component and spatially correlated component of the LMCR (see variance components in Table 4). This substantial short-range variability means that there is inevitably a good deal of uncertainty attached to spatial predictions made by kriging from what is, over most of the MCZ, a relatively coarse grid.		Note that, while the class Sand and Muddy Sand is delineated as most probable across most of the MCZ, there is uncertainty attached to this, particularly in the south-western and central eastern parts of the zone. This reflects the subtle transitions of sediment texture shown in Figure 2. It also reflects the importance of short-range variability of sediment texture in this MCZ. The short-range variability of sediment texture is large, shown by the relative magnitude of the nugget component and spatially correlated component of the LMCR (see variance components in Table 4). This substantial short-range variability means that there is inevitably a good deal of uncertainty attached to spatial predictions made by kriging from what is, over most of the MCZ, a relatively coarse grid.

Ajhil: /* Spatial predictions */

2022-05-04T08:39:15Z

Spatial predictions

← Older revision		Revision as of 09:39, 4 May 2022
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	\|+ Table 5    Structure of the SS_predictions.dat file.		\|+ Table 5    Structure of the SS_predictions.dat file.
	\| ! scope="col" style="width: 100px;" \| '''Column'''		\| ! scope="col" style="width: 100px;" \| '''Column'''
	\| ! scope="col" style="width: ~~600px~~;" \| '''Content'''		\| ! scope="col" style="width: 450px;" \| '''Content'''
	\|- style="vertical-align:top;"		\|- style="vertical-align:top;"
	\| 1		\| 1

Ajhil: /* Spatial analysis */

2022-05-04T08:38:49Z

Spatial analysis

← Older revision		Revision as of 09:38, 4 May 2022
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	\| ! scope="col" style="width: 125px;" \| Spatial correlation<br>model type		\| ! scope="col" style="width: 125px;" \| Spatial correlation<br>model type
	\| ! scope="col" style="width: 150px;" \| Distance parameter of<br>spatial model/metres		\| ! scope="col" style="width: 150px;" \| Distance parameter of<br>spatial model/metres
	\| ! scope="col" style="width: ~~150px~~;" colspan="3" \| Variance or Covariance Component		\| ! scope="col" style="width: 200px;" colspan="3" \| Variance or Covariance Component
	\|- style="vertical-align:top;"		\|- style="vertical-align:top;"
	\|		\|

Ajhil: /* Spatial analysis */

2022-05-04T08:38:22Z

Spatial analysis

← Older revision		Revision as of 09:38, 4 May 2022
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	\|+ Table 4    Parameters of the linear model of coregionalization<br>based on method-of-moments for Swallow Sand.		\|+ Table 4    Parameters of the linear model of coregionalization<br>based on method-of-moments for Swallow Sand.
	\|- style="vertical-align:top;"		\|- style="vertical-align:top;"
	\| ! scope="col" style="width: ~~125px~~;" \| Component		\| ! scope="col" style="width: 100px;" \| Component
	\| ! scope="col" style="width: 125px;" \| Spatial correlation<br>model type		\| ! scope="col" style="width: 125px;" \| Spatial correlation<br>model type
	\| ! scope="col" style="width: 150px;" \| Distance parameter of<br>spatial model/metres		\| ! scope="col" style="width: 150px;" \| Distance parameter of<br>spatial model/metres

Ajhil at 08:37, 4 May 2022

2022-05-04T08:37:35Z

← Older revision		Revision as of 09:37, 4 May 2022
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	{{OR/14/015}}		{{OR/14/015}}
	==Data==		==Data==
	The data used are particle size analyses from the verification survey carried out by Cefas in 2012 (Ware, 2012<ref name="Ware 2012">WARE, S. 2012 Swallow Sand rMCZ Survey Report. Survey Report: C5650. Issue date: 16 Nov 2012. </ref>). The data were collected by 0.1 m<sup>2</sup> Hamon grab from 103 locations on a pre-planned survey grid. Exploratory analysis of the data showed that sample stations 465 and 467 have duplicate coordinates. Because such duplicated data cannot be used in the methodology of Lark et al. (2012)<ref name="Lark 2012">LARK, R M, DOVE, D, GREEN, S, RICHARDSON, A E, STEWART, H, and STEVENSON, A. 2012. Spatial prediction of seabed sediment texture classes by cokriging from a legacy database of point observations. ''Sedimentary Geology'', Vol. 281, 35–49. </ref> these two observations were excluded, leaving 101 observations. This would generally be regarded as sufficient for spatial modelling (Webster and Oliver, 1992<ref name="Webster">WEBSTER, R, and OLIVER, M A. 1992. Sample adequately to estimate variograms of soil properties. ''Journal of Soil Science'', Vol. 43, 177–192. </ref>). All analyses were therefore completed on this data set, since it is preferable to use data which are collected on a common support (the size and shape of the sample volume) and produced by a consistent analytical methodology.		The data used are particle size analyses from the verification survey carried out by Cefas in 2012 (Ware, 2012<ref name="Ware 2012">WARE, S. 2012 Swallow Sand rMCZ Survey Report. Survey Report: C5650. Issue date: 16 Nov 2012.</ref>). The data were collected by 0.1 m<sup>2</sup> Hamon grab from 103 locations on a pre-planned survey grid. Exploratory analysis of the data showed that sample stations 465 and 467 have duplicate coordinates. Because such duplicated data cannot be used in the methodology of Lark et al. (2012)<ref name="Lark 2012">LARK, R M, DOVE, D, GREEN, S, RICHARDSON, A E, STEWART, H, and STEVENSON, A. 2012. Spatial prediction of seabed sediment texture classes by cokriging from a legacy database of point observations. ''Sedimentary Geology'', Vol. 281, 35–49.</ref> these two observations were excluded, leaving 101 observations. This would generally be regarded as sufficient for spatial modelling (Webster and Oliver, 1992<ref name="Webster">WEBSTER, R, and OLIVER, M A. 1992. Sample adequately to estimate variograms of soil properties. ''Journal of Soil Science'', Vol. 43, 177–192.</ref>). All analyses were therefore completed on this data set, since it is preferable to use data which are collected on a common support (the size and shape of the sample volume) and produced by a consistent analytical methodology.

	The data provided are percent by mass of gravel (particles diameter >~~ ~~2 mm), mud (particles diameter <0.063 mm) and sand (particles 2 mm > diameter >0.063 mm).		The data provided are percent by mass of gravel (particles diameter >2 mm), mud (particles diameter <0.063 mm) and sand (particles 2 mm > diameter >0.063 mm).

	===Exploratory data analysis and transformation===		===Exploratory data analysis and transformation===

Ajhil at 08:35, 4 May 2022

2022-05-04T08:35:36Z

← Older revision		Revision as of 09:35, 4 May 2022
Line 4:		Line 4:
	The data used are particle size analyses from the verification survey carried out by Cefas in 2012 (Ware, 2012<ref name="Ware 2012">WARE, S. 2012 Swallow Sand rMCZ Survey Report. Survey Report: C5650. Issue date: 16 Nov 2012. </ref>). The data were collected by 0.1 m<sup>2</sup> Hamon grab from 103 locations on a pre-planned survey grid. Exploratory analysis of the data showed that sample stations 465 and 467 have duplicate coordinates. Because such duplicated data cannot be used in the methodology of Lark et al. (2012)<ref name="Lark 2012">LARK, R M, DOVE, D, GREEN, S, RICHARDSON, A E, STEWART, H, and STEVENSON, A. 2012. Spatial prediction of seabed sediment texture classes by cokriging from a legacy database of point observations. ''Sedimentary Geology'', Vol. 281, 35–49. </ref> these two observations were excluded, leaving 101 observations. This would generally be regarded as sufficient for spatial modelling (Webster and Oliver, 1992<ref name="Webster">WEBSTER, R, and OLIVER, M A. 1992. Sample adequately to estimate variograms of soil properties. ''Journal of Soil Science'', Vol. 43, 177–192. </ref>). All analyses were therefore completed on this data set, since it is preferable to use data which are collected on a common support (the size and shape of the sample volume) and produced by a consistent analytical methodology.		The data used are particle size analyses from the verification survey carried out by Cefas in 2012 (Ware, 2012<ref name="Ware 2012">WARE, S. 2012 Swallow Sand rMCZ Survey Report. Survey Report: C5650. Issue date: 16 Nov 2012. </ref>). The data were collected by 0.1 m<sup>2</sup> Hamon grab from 103 locations on a pre-planned survey grid. Exploratory analysis of the data showed that sample stations 465 and 467 have duplicate coordinates. Because such duplicated data cannot be used in the methodology of Lark et al. (2012)<ref name="Lark 2012">LARK, R M, DOVE, D, GREEN, S, RICHARDSON, A E, STEWART, H, and STEVENSON, A. 2012. Spatial prediction of seabed sediment texture classes by cokriging from a legacy database of point observations. ''Sedimentary Geology'', Vol. 281, 35–49. </ref> these two observations were excluded, leaving 101 observations. This would generally be regarded as sufficient for spatial modelling (Webster and Oliver, 1992<ref name="Webster">WEBSTER, R, and OLIVER, M A. 1992. Sample adequately to estimate variograms of soil properties. ''Journal of Soil Science'', Vol. 43, 177–192. </ref>). All analyses were therefore completed on this data set, since it is preferable to use data which are collected on a common support (the size and shape of the sample volume) and produced by a consistent analytical methodology.

	The data provided are percent by mass of gravel (particles diameter~~ ~~> 2 mm), mud (particles diameter <~~ ~~0.063 mm) and sand (particles 2 mm > diameter >~~ ~~0.063 mm).		The data provided are percent by mass of gravel (particles diameter > 2 mm), mud (particles diameter <0.063 mm) and sand (particles 2 mm > diameter >0.063 mm).

	===Exploratory data analysis and transformation===		===Exploratory data analysis and transformation===

Ajhil: /* Data */

2022-04-29T10:37:43Z

Data

← Older revision		Revision as of 11:37, 29 April 2022
Line 2:		Line 2:
	{{OR/14/015}}		{{OR/14/015}}
	==Data==		==Data==
	The data used are particle size analyses from the verification survey carried out by Cefas in 2012 (Ware, 2012<ref name="Ware 2012">WARE, S. 2012 Swallow Sand rMCZ Survey Report. Survey Report: C5650. Issue date: 16 Nov 2012. </ref>). The data were collected by 0.1 m<sup>2</sup> Hamon grab from 103 locations on a pre-planned survey grid. Exploratory analysis of the data showed that sample stations 465 and 467 have duplicate coordinates. Because such duplicated data cannot be used in the methodology of Lark et al. (2012)<ref name="Lark 2012">LARK, R M, DOVE, D, GREEN, S, RICHARDSON, A E, STEWART, H, and STEVENSON, A. 2012. Spatial prediction of seabed sediment texture classes by cokriging from a legacy database of point observations. ''Sedimentary Geology'', Vol. 281, 35–49. </ref> these two observations were excluded, leaving 101 observations. This would generally be regarded as sufficient for spatial modelling (Webster and Oliver, 1992<ref name="Webster">WEBSTER, R, and OLIVER, M A. 1992. Sample adequately to estimate variograms of soil properties. ''Journal of Soil Science'', Vol. 43, 177–192. </ref>). All analyses were therefore completed on this data set, since it is preferable to use data which are collected on a common support (the size and shape of the sample volume) and produced by a consistent analytical methodology.		The data used are particle size analyses from the verification survey carried out by Cefas in 2012 (Ware, 2012<ref name="Ware 2012">WARE, S. 2012 Swallow Sand rMCZ Survey Report. Survey Report: C5650. Issue date: 16 Nov 2012. </ref>). The data were collected by 0.1 m<sup>2</sup> Hamon grab from 103 locations on a pre-planned survey grid. Exploratory analysis of the data showed that sample stations 465 and 467 have duplicate coordinates. Because such duplicated data cannot be used in the methodology of Lark et al. (2012)<ref name="Lark 2012">LARK, R M, DOVE, D, GREEN, S, RICHARDSON, A E, STEWART, H, and STEVENSON, A. 2012. Spatial prediction of seabed sediment texture classes by cokriging from a legacy database of point observations. ''Sedimentary Geology'', Vol. 281, 35–49. </ref> these two observations were excluded, leaving 101 observations. This would generally be regarded as sufficient for spatial modelling (Webster and Oliver, 1992<ref name="Webster">WEBSTER, R, and OLIVER, M A. 1992. Sample adequately to estimate variograms of soil properties. ''Journal of Soil Science'', Vol. 43, 177–192. </ref>). All analyses were therefore completed on this data set, since it is preferable to use data which are collected on a common support (the size and shape of the sample volume) and produced by a consistent analytical methodology.

	The data provided are percent by mass of gravel (particles diameter > ~~2mm~~), mud (particles diameter < 0.063 mm) and sand (particles 2 mm > diameter > 0.063 mm).		The data provided are percent by mass of gravel (particles diameter > 2 mm), mud (particles diameter < 0.063 mm) and sand (particles 2 mm > diameter > 0.063 mm).

	===Exploratory data analysis and transformation===		===Exploratory data analysis and transformation===

Ajhil: /* Spatial predictions */

2022-04-29T10:36:19Z

Spatial predictions

← Older revision		Revision as of 11:36, 29 April 2022
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	<span id="Table 6"></span>		<span id="Table 6"></span>
	\|+ Table 6    Structure of the SS_classes.dat file.		\|+ Table 6    Structure of the SS_classes.dat file.
			\| '''Column'''
	\| Column		\| '''Content'''
	\| Content		\|- style="vertical-align:top;"
	\|-
	\| 1		\| 1
	\| x-coordinate, UTM29N		\| x-coordinate, UTM29N
	\|-		\|- style="vertical-align:top;"
	\| 2		\| 2
	\| y-coordinate, UTM29N		\| y-coordinate, UTM29N
Line 239:		Line 238:
	\| 3		\| 3
	\| Most probable EUNIS class:<br>1 Coarse<br>2 Mixed<br>3 Mud and sandy mud<br>4 Sand and muddy sand		\| Most probable EUNIS class:<br>1 Coarse<br>2 Mixed<br>3 Mud and sandy mud<br>4 Sand and muddy sand
	\|-		\|- style="vertical-align:top;"
	\| 4		\| 4
	\| Probability of most probable class		\| Probability of most probable class
	\|-		\|- style="vertical-align:top;"
	\| 5		\| 5
	\| Probability of class Coarse		\| Probability of class Coarse
	\|-		\|- style="vertical-align:top;"
	\| 6		\| 6
	\| Probability of class Mixed		\| Probability of class Mixed
	\|-		\|- style="vertical-align:top;"
	\| 7		\| 7
	\| Probability of class Mud and sandy mud		\| Probability of class Mud and sandy mud
	\|-		\|- style="vertical-align:top;"
	\| 8		\| 8
	\| Probability of class Mud and muddy sand		\| Probability of class Mud and muddy sand
	\|-		\|- style="vertical-align:top;"
	\| 9		\| 9
	\| Entropy of the class probabilities (-1 times the expected value of the log probability over the distribution)		\| Entropy of the class probabilities (-1 times the expected value of the log probability over the distribution)

Ajhil: /* Spatial predictions */

2022-04-29T10:34:25Z

Spatial predictions

← Older revision		Revision as of 11:34, 29 April 2022
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	Figure 2 below shows the conditional expectation of gravel, sand and mud across the Swallow Sand MCZ, and Figure 3 shows the 0.025 and 0.975 quantile which define a 95% confidence interval for mud content.		Figure 2 below shows the conditional expectation of gravel, sand and mud across the Swallow Sand MCZ, and Figure 3 shows the 0.025 and 0.975 quantile which define a 95% confidence interval for mud content.
	{{clear}}		{{clear}}
	[[Image:14015 fig2.jpg\|thumb\|center\|500px\|'''Figure 2''' Conditional Expectation of gravel, sand and mud content (proportions) across the Swallow Sand MCZ based on multiple realizations of the prediction distribution.]]		[[Image:14015 fig2.jpg\|thumb\|center\|500px\|'''Figure 2'''    Conditional Expectation of gravel, sand and mud content (proportions) across the Swallow Sand MCZ based on multiple realizations of the prediction distribution.]]

	[[Image:14015 fig3.jpg\|thumb\|center\|500px\|'''Figure 3''' Upper (top) and lower (bottom) bounds of the 95% confidence interval for mud content (proportion) across the Swallow Sand MCZ based on multiple realizations of the prediction distribution.]]		[[Image:14015 fig3.jpg\|thumb\|center\|500px\|'''Figure 3'''    Upper (top) and lower (bottom) bounds of the 95% confidence interval for mud content (proportion) across the Swallow Sand MCZ based on multiple realizations of the prediction distribution.]]
	{{clear}}		{{clear}}

	SS_classes.dat dat is an ASCII format file. Each row corresponds to a node on the 250-m grid. The variables in each column of the file are tabulated below.		SS_classes.dat dat is an ASCII format file. Each row corresponds to a node on the 250-m grid. The variables in each column of the file are tabulated below.

			<center>
	{\| class="wikitable"		{\| class="wikitable"
	\|+ Table 6 Structure of the SS_classes.dat file.		<span id="Table 6"></span>
			\|+ Table 6    Structure of the SS_classes.dat file.

	\| Column		\| Column
Line 254:		Line 256:
	\|-		\|-
	\| 9		\| 9
	\| Entropy of the class probabilities (-1 times the expected value of the log probability over the distribution)/		\| Entropy of the class probabilities (-1 times the expected value of the log probability over the distribution)
	\|}		\|}
			</center>

	Figure 4 below shows the most probable EUNIS class across the Swallow Sand MCZ, and the probability of the most probable class. Figure 5 shows the probability of each class.		Figure 4 below shows the most probable EUNIS class across the Swallow Sand MCZ, and the probability of the most probable class. Figure 5 shows the probability of each class.
	{{Clear}}		{{Clear}}
	[[Image:14015 fig4.jpg\|thumb\|~~left~~\|500px\|'''Figure 4''' Most probable EUNIS class (top) across the Swallow Sand MCZ and probability of the most probable class (bottom).]]		[[Image:14015 fig4.jpg\|thumb\|center\|500px\|'''Figure 4'''    Most probable EUNIS class (top) across the Swallow Sand MCZ and probability of the most probable class (bottom).]]

	[[Image:14015 fig5.jpg\|thumb\|~~left~~\|500px\| '''Figure 5''' Probability of finding each EUNIS class (top) across the Swallow Sand MCZ.]]		[[Image:14015 fig5.jpg\|thumb\|center\|500px\| '''Figure 5'''    Probability of finding each EUNIS class (top) across the Swallow Sand MCZ.]]
	{{Clear}}		{{Clear}}
	Note that, while the class Sand and Muddy Sand is delineated as most probable across most of the MCZ, there is uncertainty attached to this, particularly in the south-western and central eastern parts of the zone. This reflects the subtle transitions of sediment texture shown in Figure 2. It also reflects the importance of short-range variability of sediment texture in this MCZ. The short-range variability of sediment texture is large, shown by the relative magnitude of the nugget component and spatially correlated component of the LMCR (see variance components in Table 4). This substantial short-range variability means that there is inevitably a good deal of uncertainty attached to spatial predictions made by kriging from what is, over most of the MCZ, a relatively coarse grid.		Note that, while the class Sand and Muddy Sand is delineated as most probable across most of the MCZ, there is uncertainty attached to this, particularly in the south-western and central eastern parts of the zone. This reflects the subtle transitions of sediment texture shown in Figure 2. It also reflects the importance of short-range variability of sediment texture in this MCZ. The short-range variability of sediment texture is large, shown by the relative magnitude of the nugget component and spatially correlated component of the LMCR (see variance components in Table 4). This substantial short-range variability means that there is inevitably a good deal of uncertainty attached to spatial predictions made by kriging from what is, over most of the MCZ, a relatively coarse grid.