A.6.14 R code for dot plot with encircling by (ggalt) library

Source data available on my GitHub.

1 # Part-1. Prepare data frame.
2 # step-1. read in table with data on geomorphology. create initial data frame
3 MDepths <- read.csv("Morphology.csv", header=TRUE, sep = ",")
4 # step-2. clean up data frame from NA values
5 MDF <- na.omit(MDepths)
6 row.has.na <- apply(MDF, 1, function(x) {any(is.na(x))) # check up data frame if there are any NA
7 sum(row.has.na) # sum up all NA, should be: [1] 0
8 head(MDF) # look up data frame
9 # Part 2: Generate Dot Plot with Encircling
10 # step-3 set up two areas 2 with encircling separately for each plot
11 crucial_igneous <- MDF[MDF$igneous_volc > 50 & MDF$igneous_volc <= 300 & MDF$profile > 5 & MDF$profile <= 25, ]
12 crucial_angles <- MDF[MDF$tg_angle > 0.00 & MDF$tg_angle <= 0.075 & MDF$profile > 5 & MDF$profile <= 22, ]
13
14 # step-4. draw plot nr. 1 for points distribution within areas of igneous volcanic zones by bathymetric profiles
15 g1 <- ggplot(MDF, aes(x = profile, y = igneous_volc)) +
16 geom_point(aes(x = profile, y = igneous_volc, colour = "igneous_volc"), size=3, alpha = .5, show.legend=TRUE)
17 + # Draw points
18 geom_segment(aes(x = profile, xend = profile, y=min(igneous_volc), yend=max(igneous_volc)), linetype="dashed",
19 size=0.1) + # Draw dashed lines
20 geom_encircle(aes(x = profile, y = igneous_volc), data = crucial_igneous, color="red", size=1, expand=0.05) + # encircle
21 geom.smooth(aes(x = profile, y = igneous_volc, colour = "Regression loess method"), method = loess, se = FALSE,
22 size = .3, linetype = "solid", span = 1, show.legend=TRUE) +
23 geom.smooth(aes(x = profile, y = igneous_volc, colour = "Regression lm method"), method = lm, formula = y ~
24 splines::bs(x, 3), se = FALSE, size=.3, linetype = "solid", show.legend=TRUE) +
25 # coord flip() +
26 scale_color_manual(values = c("Regression loess method" = "orange", "Regression lm method" = "blue", "igneous_
27 volc" = "royalblue1")) +
28 xlab("Profiles, Nr.") +
29 ylab("Observation Points within Igneous Areas") +
30 labs(title="Mariana Trench, Profiles Nr.1-25.",
31 subtitle = "Ranking Dot Plot. \nDistribution of Observation Points across Igneous Volcanic Areas",
32 caption = "Statistics Processing and Graphs: R Programming. Data Source: QGIS") +
33 theme(
34 plot-margin = margin(5, 10, 20, 5),
35 plot.title = element_text(margin = margin(t = 0, r = 20, b = 5, l = 0), family = "Kai", face = "bold", size =
36 12),
37 plot.subtitle = element_text(margin = margin(t = 10, r = 20, b = 10, l = 0), family = "Hei", face = "bold",
38 size = 10),
39 plot caption = element_text(face = 2, size = 6),
40 panelancolor=ggplot2::elementRect(fill = "white"),
41 axis.title.y = element_text(size = 8),
42 axis.title.x = element_text(size = 8),
43 axis.text.x = element_text(family = "Arial", face = 3, color = "gray24", size = 8, angle = 15),
44 axis.text.y = element_text(family = "Arial", face = 3, color = "gray24", size = 8, angle = 90),
45 legend.justification = "bottom",
46 legend position = "bottom",
47 legend.box.just = "right",
48 legend direction = "horizontal",
49 legend.box = "horizontal",
50 legend.boxancolor = elementRect(colour = "honeydew4", size=0.2),
51 legendancolor = elementRect(fill = "white"),
52 legend.key.width = unit(1,"cm"),
53 legend.key height = unit(.5,"cm"),
54 legend-spacing.x = unit(.2,"cm"),
55 legend-spacing.y = unit(.1,"cm"),
56 legend.text = element_text(colour="black", size = 6, face=1),
57 legend.title = element_text(colour="black", size = 6, face=1))
58
g1
59 # step-5. draw plot nr. 2: increased steepness of the angles by the bathymetric profiles 1:25 (tangents)
60
g2 <- ggplot(MDF, aes(x = profile, y = tg_angle)) +
61 geom_point(aes(x = profile, y = tg_angle, colour = "tg angles"), size=3, alpha = .5, show.legend=TRUE) + #
62 Draw points
```r
56 geom_segment(aes(x = profile, xend = profile, y=min(tg_angle), yend=max(tg_angle)), linetype="dashed", size=0.1) + # Draw dashed lines
57
58 geom_encircle(aes(x = profile, y = tg_angle), data = crucial_angles, color="red", size=1, expand=0.05) + # encircle
59
60 geom.smooth(aes(x = profile, y = tg_angle, colour = "Regression loess method"), method = loess, se = FALSE, size = .3, linetype = "solid", span = 1, show.legend=TRUE) +
61
62 # coord flip() +
63
64 geom_segment(aes(x = 22, y = 0.075, xend = 23, yend = 0.079, color = "arrow"), size = .2, arrow = arrow(length = unit(0.1, "cm"))) + # draw arrow 1
65
66 geom_segment(aes(x = 22, y = 0.073, xend = 24.5, yend = 0.068, color = "arrow"), size = .2, arrow = arrow( length = unit(0.1, "cm"))) + # draw arrow 2
67
68 # subscript annotation text by arrow (here: number of bathymetric observations)
69
70 scale_color_manual(values = c("Regression loess method" = "orange", "Regression lm method" = "blue", "tg angles" = "purple", "arrow" = "red")) +
71
72 labs(title="Mariana Trench, Profiles Nr.1-25.",
73 subtitle=expression(paste("Ranking Dot Plot. 
\nVariation of Steepness Angles ", tg*degree*(A/H), " by profiles 1:25")),
74
75 scale_fill_brewer(palette = "RdBu") +
76
77 theme(
78 plot-margin = margin(5, 10, 20, 5),
79 plot.title = element_text(margin = margin(t = 0, r = 20, b = 5, l = 0), family = "Kai", face = "bold", size = 12),
80
81 plot.subtitle = element_text(margin = margin(t = 10, r = 20, b = 10, l = 0), family = "Hei", face = "bold", size = 10),
82
83 plotcaption = element_text(face = 2, size = 6),
84
85 panel_background=ggplot2::elementRect(fill = "white"),
86
87 axis.title.y = element_text(size = 8),
88 axis.title.x = element_text(size = 8),
89
90 axis.text.x = element_text(family = "Arial", face = 3, color = "gray24", size = 8, angle = 15),
91
92 axis.text.y = element_text(family = "Arial", face = 3, color = "gray24", size = 8, angle = 90),
93
94 legend.justification = "bottom",
95
96 legendNL = element_text(size = 8),
97
98 legend.box = element_text(size = 8),
99
100 img<-readJPEG("volcanoes.jpg")
101
102 rasterImage(img,1,1,10,10)
103
104 # optional step-8. dot plot via ggplot2.dotplot :
105
106 g1<- ggplot2.dotplot(data = MDF, xName='profile', yName='tg_angle',
107
108 xShowTitle=FALSE, yShowTitle=FALSE,
109 xTickLabelFont=c(8,"bold", "grey"),
110 yTickLabelFont=c(8,"bold", "grey"),
111 xtickLabelRotation=45, ytickLabelRotation=45)
```

codes/R-14.r
A.6.15 R code for double-Y axis using (LatticeExtra) library to perform comparative
analysis of categories

Source data available on my GitHub.

1 # Part-1. prepare data frame.
2 # step-1. read in table with data on geomorphology. Generate initial data frame
3 M depths <- read.csv("Morphology.csv", header=TRUE, sep = ",")
4 # step-2. clean up data frame from NA values
5 MDF <- na.omit(M depths)
6 row.has.na <- apply(MDF, 1, function(x) {any(is.na(x))}) # check up if any NA area available
7 sum(row.has.na) # sum up all NA, should be: [1] 0
8 head(MDF)
9
10 # Part-2. Draw 2 plots Y on one axis X "Slope angle", "Sediment thickness"
11 library(latticeExtra)
12 # step-3. generate selection of data for new data frame (only 3 useful values)
13 set.seed(1)
14 profiles = MDF$s profile
15 Slope angle = MDF$slope angle
16 Sediment thickness = MDF$sedim thick
17 data=data.frame(profiles, Slope angle, Sediment thickness)
18 # step-4 two plots in one :
19 p<- xyplot(Slope angle + Sediment thickness ~ profiles, data, type = "l")
20 p
21 # step-5. plots 1 and 2 read into objects
22 obj1 <- xyplot(Slope angle ~ profiles, data, type = "l", lwd=1)
23 obj2 <- xyplot(Sediment thickness ~ profiles, data, type = "l", lwd=1)
24 # step-6. add 2nd axis Y:
25 doubleYScale(obj1, obj2, add.ylab2 = TRUE)
26 # step-7. add legend:
27 p<- doubleYScale(obj1, obj2, text = c("Slope angle", "Sediment thickness"), add.ylab2 = TRUE)
28
29 # Part-2b. Categories: "Slope angle", "Aspect degree" by bathymetric profiles
30 # step-8. generate selection of data for new data frame (only 3 useful values)
31 set.seed(1)
32 profiles = MDF$s profile
33 Slope angle = MDF$slope angle
34 Aspect degree = MDF$aspect degree
35 data=data.frame(profiles, Slope angle, Aspect degree)
36 p<- xyplot(Slope angle + Aspect degree ~ profiles, data, type = "l")
37 p
38 # step-9. plots 1 and 2 read into objects
39 obj1 <- xyplot(Slope angle ~ profiles, data, type = "l", lwd=1)
40 obj2 <- xyplot(Aspect degree ~ profiles, data, type = "l", lwd=1)
41 doubleYScale(obj1, obj2, add.ylab2 = TRUE)
42 # step-10. add legend:
43 p1 <- doubleYScale(obj1, obj2, text = c("Slope angle", "Aspect degree"), add.ylab2 = TRUE)
44
45 # Part-2c. Categories: "Slope angle", "Igneous volcanic areas" by bathymetric profiles
46 # step-10. generate selection of data for new data frame (only 3 useful values)
47 set.seed(1)
48 profiles = MDF$s profile
49 Slope angle = MDF$slope angle
50 Igneous volcan ic = MDF$igneous volc
51 data=data.frame(profiles, Slope angle, Igneous volcan ic)
52 # step-11 plots together:
53 p<- xyplot(Slope angle + Igneous volcan ic ~ profiles, data, type = "l")
54 p
55 # step-12. plots 1 and 2 read into objects, add 2nd axis Y:
56 obj1 <- xyplot(Slope angle ~ profiles, data, type = "l", lwd=1)
57 obj2 <- xyplot(Igneous volcan ic ~ profiles, data, type = "l", lwd=1)
58 doubleYScale(obj1, obj2, add.ylab2 = TRUE)
59 # step-13. add legend:
60 p2 <- doubleYScale(obj1, obj2, text = c("Slope angle", "Igneous volcanic"), add.ylab2 = TRUE)
61 p2

codes/R-15.r
A.6.16 R code for circular barplot by library tidyverse

Source data available on my GitHub.

1 # Part-1. Prepare data frame.
2 # step-1. read in table with data. create initial data frame. clean up from NA
3 MDepths <- read.csv("Morphology.csv", header=TRUE, sep = ",")
4 MDF <- na.omit(MDepths)
5 row.has.na <- apply(MDF, 1, function(x){any(is.na(x))})
6 sum(row.has.na)
7 head(MDF)
8 # step-2. merge categories by groups into classes (here: tectonics, depths, angles)
9 MDFt <- melt(setDT(MDF), measure = patterns("^plate"), value.name = c("tectonics"))
10 head(MDFt)
11 # step-3 create short data frame from 3 values (value - length of the 'flower' petal within the diagram)
12 data<- data.frame(
13 id = MDFt$profile, # numbers as factor value, i.e. just as sequence 1:25
14 individual = paste("Profile", seq(1,25), sep=""),
15 group = MDFt$variable, # here: 4 tectonic plates
16 value = MDFt$tectonics) # here: value of the sediment thickness layer (length of the petal of circle)
17 levels(MDFt$variable) = c("Philippine", "Pacific", "Mariana", "Caroline") # implicitly rename tectonic plates
to be shown on the axis X
18
19 # Order data:
20 data = data %>% arrange(group, value)
21 # step-4 create empty column to add some space at the end of each group
22 empty_bar=3
23 to_add = data.frame( matrix(NA, empty_bar*nlevels(data$group), ncol(data)))
24 colnames(to_add) = colnames(data)
25 to_add$group=rep(levels(data$group), each=empty_bar)
26 data=rbind(data, to_add)
27 data=data %>% arrange(group)
28 data$id=seq(1, nrow(data))
29
30 # Part-2.
31 # step-5 create tables for each petal getting the names from the data frame and the y position of each label
32 label_data = data
33 number_of_bar=nrow label_data) # -4 calculate the ANGLE of the labels
34 angle = 90 - 360 * (label_data$id-0.5) /number_of_bar # I substract 0.5 because the letter must have the angle
of the center of the bars. Not extreme right(1) or extreme left (0)
35 label_data$hjust<-ifelse(angle < -90, 1, 0) # distribute labels on right and left
36 label_data$angle<-ifelse(angle < -90, angle+180, angle) # flip tables to make them readable
37
38 # Part-3 Draw the circle
39 # step-6. prepare a data frame for the base lines
40 base_data=data %>%
41 group_by(group) %>%
42 summarize(start=min(id), end=max(id) - empty_bar) %>%
43 rowwise() %>%
44 mutate(title=mean(c(start, end)))
45 # step-7. prepare a data frame for grid (scales)
46 grid_data = base_data
47 grid_data$end = grid_data$end[ c( nrow(grid_data), 1:nrow(grid_data)-1)] + 1
48 grid_data$start = grid_data$start - 1
49 grid_data=grid_data[-1,]
50 # step-8.
51 p <- ggplot(data, aes(x = as.factor(id), y = value, fill = group)) +
52 geom_bar(aes(x = as.factor(id), y = value, fill = group), stat="identity", alpha=0.5) +
53 # scale_fill_distiller(palette = "Set1") +
54 scale_fill_manual(values = c("purple", "deeppink", "blue", "cyan")) +
55 # step-9. Add a val=100/75/50/25 lines to make sure barplots are over it.
56 # here: draw come ticks of scale in the empty spaces between the petals from 25 to 100, further up to 500 by
100.
57 geom_segment(data=grid_data, aes(x = end, y = 500, xend = start, yend = 500), colour = "grey", alpha=1, size
=0.3 , inherit.aes = FALSE ) +
58 geom_segment(data=grid_data, aes(x = end, y = 400, xend = start, yend = 400), colour = "grey", alpha=1, size
=0.3 , inherit.aes = FALSE ) +
59 geom_segment(data=grid_data, aes(x = end, y = 300, xend = start, yend = 300), colour = "grey", alpha=1, size
=0.3 , inherit.aes = FALSE ) +
```r
60 geom_segment(data=grid_data, aes(x = end, y = 200, xend = start, yend = 200), colour = "grey", alpha=1, size = 0.3, inherit.aes = FALSE) +
61
62 geom_segment(data=grid_data, aes(x = end, y = 100, xend = start, yend = 100), colour = "grey", alpha=1, size = 0.3, inherit.aes = FALSE) +
63
64 geom_segment(data=grid_data, aes(x = end, y = 80, xend = start, yend = 80), colour = "grey", alpha=1, size=0.3, inherit.aes = FALSE) +
65
66 geom_segment(data=grid_data, aes(x = end, y = 60, xend = start, yend = 60), colour = "grey", alpha=1, size=0.3, inherit.aes = FALSE) +
67
68 # step-10. add annotations of the scale ticks in one of the bars showing the value of each 100/75/50/25 lines
69 annotate("text", x = rep(max(data$iid),9), y = c(20, 40, 60, 80, 100, 200, 300, 400, 500), label = c("20", "40", "60", "80", "100", "200", "300", "400", "500"), color="grey", size = 2, angle=0, fontface="bold", hjust = 1) +
70 # here: ylim = 550, because all values of the tectonics do not overstep 550 (i.e. outer circle)
71 # value -50 - diamcircleleeter of the inner circle.
72 #theme_minimal() +
73 theme(
74 legendnerposition = "none",
75 axis.text = elementBlanks,
76 axis.title = elementBlanks,
77 plot.title = element_text(margin = margin(t = 0, r = 0, b = 0, l = 0), size = 10, face = "bold"),
78 plotmargin = unit(rep(-1,4), "cm")) +
79 coord_polar() +
80
81 geom_text(data = label_data, aes(x = id, y = value+10, label = individual, hjust=hjust), color="black", fontface="bold", alpha=0.6, size=2.0, angle = label_data$angle, inherit.aes = FALSE) +
82 # Add base line information
83
84 geom_segment(data = base_data, aes(x = start, y = -5, xend = end, yend = -5), colour = "black", alpha=0.8, size = 0.6, inherit.aes = FALSE) +
85
86 geom_text(data = base_data, aes(x = title, y = -18, label = group), hjust=c(1,1,0,0), colour = "black", alpha = 0.8, size=3, fontface="bold", inherit.aes = FALSE)
87
88 p
```

codes/R-16.r
A.6.18 R code for multiple panels by groups (tectonic plates, depths, slope angles)

Source data available on my GitHub.

1
# Part-1. generate data frame.
2
# step-1.
3 MDepths <- read.csv("DepthTect.csv", header=TRUE, sep = ",")
4 MDF1 <- na.omit(MDepths)
5 row.has.na <- apply(MDF1, 1, function(x){any(is.na(x))}) # check up NA
6 sum(row.has.na) # sum up NA: [1] 0
7 head(MDF1) # look up clean data frame
8
# step-2. merge groups of categories by classes (here: tectonic plates, depths, slope angles)
9 DFDT = melt(setDT(MDF1), measure = patterns("^profile", "^tectonics", "^tg"), value.name = c("depth", "")
"tectonics", "trench_angle"))
10
head(DFDT)
11
# step-3. Multiple panels by groups: y ~ x | group generate multi-plot
12 p<- xyplot(depth ~ variable | tectonics,
13 group = tectonics, data = DFDT,
14 type = c("p", "smooth"),
15 scales = "free",
16 main="Multiple ScatterPlot: Depths Distribution by Tectonic Plates",
17 xlab="Profiles, Nr")
18
p

codes/R-18.r

A.6.19 R code for density curves: data distribution

Source data available on my GitHub.

1
# Part-1. Generate data frame
2
# step-1. prepare data
3 MDepths <- read.csv("DepthTect.csv", header=TRUE, sep = ",")
4 MDF1 <- na.omit(MDepths)
5 row.has.na <- apply(MDF1, 1, function(x){any(is.na(x))})
6 sum(row.has.na) #: [1] 0
7 head(MDF1)
8
# step-2. merge groups of categories by classes (here: tectonic plates, depths, slope angles)
9 DFDT = melt(setDT(MDF1), measure = patterns("^profile", "^tectonics", "^tg"), value.name = c("depth", "")
"tectonics", "trench_angle"))
10
DFDT
11
# step-3. generate multi-plot (here: by types of the tectonic plates as slope angles and depths change)
12 densityplot(~ depth, groups = variable, data = DFDT, plot points = FALSE, auto.key = FALSE)
13
densityplot(~ depth, groups = tectonics, data = DFDT, plot points = FALSE, auto.key = TRUE)

codes/R-19.r
A.6.20 R code to generate brackets for the specific regions along the Mariana Trench using library (pBrackets)

Source data available on my GitHub.

1 # Part-1. (Prepare data frame.
2 # step-1. read in a table with geomorphology data. create data frame
3 MDepths <- read.csv("DepthTect.csv", header=TRUE, sep = ",")
4 # step-2. clean up data frame from NA
5 MDF1 <- na.omit(MDepths)
6 row.has.na <- apply(MDF1, 1, function(x){any(is.na(x))})
7 sum(row.has.na) #: [1] 0
8 head(MDF1)
9 # step-3. create a list with 4 columns (numbers of observations 1:518, numbers of profiles 1:25, depths, tectonic plates).
10 DFDT = melt(setDT(MDF1), measure = patterns("^profile", "^tectonics", "^tg"), value.name = c("depth", " tectonics", "trench_angle"))
11 DFDT
12 # step-4. implicitly write number of profiles - factors values, depths - numeric values
13 DFDT$variable <- as.factor(DFDT$variable)
14 DFDT$depth<- as.numeric(DFDT$depth)
15 DFDT$trench_angle <- as.numeric(DFDT$trench_angle)
16
17 # Part 2: draw scatterplot of maximal depths by 25 bathymetric profiles
18 # step-5.
19 gi <- ggplot(MDF, aes.profile, igneous_volc)) +
20 geom_line() +
21 geom.smooth(method="lm", se=F) +
22 labs(subtitle="Correlation: Maximal Depths vs Profiles",
23 y="Depths (Max)", x="profiles",
24 title="Scatterplot with overlapping points", caption="Source: R, QGIS")
25 gi
26 # step-6.
27 angles <- ggplot(MDF, aes.profile, tg_angle, colour = tg_angle)) +
28 geom_point() +
29 # geom.smooth(method="lm", se=F) +
30 labs(subtitle="Correlation: Angles (tg(A/H)) vs Profiles",
31 y="Angles", x="profiles",
32 title="Scatterplot with overlapping points", caption="Source: R, QGIS")
33 angles
34 # step-7.
35 ab<- angles + brackets(0, 0.05, 5, 0.55, h = NULL, ticks = 0.5, curvature = 0.5, type = 1, col = 1, lwd = 1,
36 lty = 1, xpd = FALSE)
37 # Part 3: generate brackets for specific regions along the Mariana Trench using library {pBrackets}
38 # step-8.
39 grid.locator(unit="native")
40 angles
41 grid.brackets(155, 155, 78, 155, lwd=1, col="red")
42 grid.brackets(307, 155, 155, 155, lwd=1, col="red")
43 grid.brackets(420, 155, 307, 155, lwd=1, col="red")
44 grid.brackets(420, 155, 593, 155, lwd=1, col="red")
45 grid.brackets(686, 155, 593, 155, lwd=1, col="red")
46 # step-9.
47 ab<- angles + grid.brackets(155, 155, 80, 155, lwd=1, col="red")
48 grid.brackets(325, 155, 155, 155, lwd=1, col="red")
49 # step-10.
50 pn <- ggplot(data= MDF, aes(x=MDF$profile)) +
51 geom_point(mapping = aes(x = MDF$profile, y = MDF$igneous_volc, colour = "igneous_volc")) +
52 geom.smooth mapping = aes(x = MDF$profile, y = MDF$igneous_volc, colour = "si"), method = "loess", se = F, size
53 = .3, linetype = "solid", span = 0.95, show.legend=TRUE) +
54 geom_point mapping = aes(x = MDF$profile, y = MDF$plate_maria, colour = "plate_maria")) +
55 geom.smooth mapping = aes(x = MDF$profile, y = MDF$plate_maria, colour = "ma"), method = "loess", se = F, size
56 = .3, linetype = "solid", show.legend=TRUE, span = 0.9) +
57 scale_color_manual(values = c("igneous_volc" = "purple", "plate_maria"="orange", "si" = "green", "ma" = "blue")
58 ) +
59 scale_xcontinuous() +
60 scale_y continuous("igneous_volc", sec.axis = sec_axis(~./1, name = "plate_maria", breaks = c(seq(0.0, 300, by
61 = 50)))
62
58 pn
59 # Part 4:
60 # step-11. option with a white strip in the center of the blue-red colored plot
61 mid<-mean(DFDT$depth) #
62 g2<- ggplot(DFDT, aes(x = variable, y = depth, color = depth)) +
63 geom_point() +
64 scale_y continuous(breaks = c(seq(-10000, -0, by = 1000))) +
65 # scale_colorgradient(low="blue", high="red") + # -
66 scale_colorgradient2 (midpoint=mid, low="blue", mid="white", high="red", space ="Lab")) +
67 labs(subtitle="Scope of Depth Extension by Profiles",
68 y = "Depth", x ="profiles",
69 title="Scatterplot with overlapping points", caption="Source: R, QGIS")
70 g2
71 # step-12. option with blue colored plot, by default
72 alldepths<- ggplot(DFDT, aes(x = variable, y = depth, color = depth)) +
73 geom_point() +
74 scale_y continuous(breaks = c(seq(-10000, -0, by = 1000))) +
75 labs(subtitle="Scope of Depth Extension by Profiles",
76 y = "Depth", x ="profiles",
77 title="Scatterplot with overlapping points", caption="Source: R, QGIS")
78 alldepths
79
80 # Part 5 2 plots together, 2 y axes and one X axis on a common plot
81 # step-13.
82 p<- ggplot(DFDT, mapping = aes(x = DFDT$variable)) +
83 geom_point(DFDT, mapping = aes(x = DFDT$variable, y = DFDT$depth), color = "blue") +
84 geom_point(DFDT, mapping = aes(x = DFDT$variable, y = DFDT$trench angle), color = "red") +
85 scale_y continuous("Depth", sec axis = sec axis (~/.200000, name = "Angle"))
86 p<- p + scale_y continuous(sec axis = dup axis())
87 p<- p + scale_y continuous("Depth", sec axis = sec axis (~/.200000, name = "Angle"))
88 p
89 # step-14. 2nd plot
90 p <- p + scale_y continuous(sec axis = sec axis (~.*5, name = "Bathymetric depths"))
91 p <- p +scale_y continuous(breaks = c(seq(-10000, -0, by = 1000))) +
92 labs(subtitle="Scope of Depth Extension by Profiles",
93 y = "Depth", x ="profiles",
94 title="Scatterplot with overlapping points", caption="Source: R, QGIS")
95 g2
96 # step-15.
97 p <- p + scale_y continuous(sec axis = sec axis (~.*5, name = "Bathymetric depths"))
98 p<- ggplot(DFDT, aes(x = variable)) +
99 geom_point(aes(y = depth, color = 'blue')) +
100 geom_point(aes(y = trench angle, color = 'red')) +
101 scale_y continuous(breaks = c(seq(-10000, -0, by = 1000))) +
102 scale_y continuous(
103 name = "Depth", sec axis = sec axis (~/.5,
104 name = "Angle"))
105 labs(subtitle="Scope of Depth Extension by Profiles",
106 y = "Depth", x ="profiles",
107 title="Scatterplot with overlapping points", caption="Source: R, QGIS")

codes/R-20.r
## A.6.21 R code for radar chart using (fmsb) library

Source data available on my GitHub.

```R
# Radar Chart install/packages("fmsb")
# Part-1. Create dataframe
# step-1. Read table. Delete NA values
MDepths <- read.csv("Morphology.csv", header=TRUE, sep = ",")
MDF <- na.omit(MDepths)
row.has.na <- apply(MDF, 1, function(x) {any(is.na(x))})
sum(row.has.na)
head(MDF)

# step-2. Merge group categories by classes
MDFt <- melt(setDT(MDF), measure = patterns("^plate"), value.name = c("tectonics"))
head(MDFt)
levels(MDFt$variable) = c("Philippine", "Pacific", "Mariana", "Caroline")
Plates <- c("Philippine", "Pacific", "Mariana", "Caroline")

# Part-2. Radar Chart.
set.seed(25)

# step-3. Create mini-set of 2 values.
radardataM =as.data.frame(matrix(MDFt$tectonics, ncol=25, nrow=4))

# radardataM <- as.data.frame(MDFt$tectonics, MDFt$profile, MDFt$variable)
colnames(radardataM) <- c(paste("Profile", seq(1:25), sep=""))
rownames(radardataM) <- c("Philippine", "Pacific", "Mariana", "Caroline")

# step-4. add 2 lines to the dataframe: max-min of each topic
radardataM <- rbind(0, 25), rbind(518, 25), radardataM)

# step-5. The default radar chart proposed by the library:
radarchart(radardataM)

# step-6. Custom the radarChart. Plot 2: the same plot with custom features
colors边 border=c(rgb(0.2,0.5,0.5,0.9), rgb(0.8,0.2,0.5,0.9), rgb(0.7,0.5,0.1,0.9), rgb(0.2,0.7,0.4,0.9))
colors_in=c(rgb(0.2,0.5,0.5,0.1), rgb(0.8,0.2,0.5,0.1), rgb(0.7,0.5,0.1,0.1), rgb(0.2,0.7,0.4,0.1))
radarchart(radardataM, axistype=1, #custom polygon
           pcol=colors边 border, pfcol=colors_in, plwd=4, plty=1,
           cglcol="grey", cglty=1, axislabcol="grey", caxislabels=seq(0,20,5), cglwd=0.8, #custom grid
           vlcx=0.8, #custom labels
           title="Mariana Trench. \nRadar Chart: Tectonics by Profiles 1:25", cex=0.7)
legend(x=1.2, y=1.0, legend = rownames(radardataM[-c(1,2),]),
        bty = "n", pch=20, col=colors_in, text.col = "grey", cex = 0.8, pt.cex = 2)

# step-7
radarchart(radardataM, axistype=2, pcol= topo colors(4), plty=1, pdensity=c(10, 10, 10, 10), pangle=c(10, 45, 90, 120), pfcol= topo colors(4), title = "Mariana Trench. \nRadar Chart: Tectonics by Profiles 1:25", cex=0.7)
legend(x=1.2, y=1.0, legend = rownames(radardataM[-c(1,2),]),
        bty = "n", pch=20, col= topo colors(4), text.col = "grey", cex = 0.8, pt.cex = 2)
```

codes/R-21.r

## A.6.22 R code for highlighted group in lollipop chart by libraries (ggsignif) and (tidy-verse)

Source data available on my GitHub.

```R
# Part-1. Prepare data frame
# step-1. read in table. create initial data frame, clean up from the NA
MDepths <- read.csv("Morphology.csv", header=TRUE, sep = ",")
MDF <- na.omit(MDepths)
row.has.na <- apply(MDF, 1, function(x) {any(is.na(x))})
sum(row.has.na)
head(MDF)

# step-2. Create short data from 3 values
data<- data.frame(x = MDF$profile, y = MDF$tg_angle)
p_unsort<- ggplot(data, aes(x=x, y=y)) +
  geom_segment( aes(x=x, xend=x, y=0, yend=y), color="skyblue", size=0.5) +
  geom_point( color="slateblue1", size=4) +
  coord flip() +
  theme light() +
theme panel.grid.major.x = element blan k(),
```
```r
16 panel border = element-blank(),
17 axis.ticks.x = element-blank(),
18 axis.title.y = element-text(margin = margin(t = 20, r = 3), family = "Times New Roman", face = 1, size = 10),
19 axis.title.x = element-text(family = "Times New Roman", face = 1, size = 10, margin = margin(t = .2)),
20 ) +
21 xlab("Profile Nr.") +
22 ylab(expression(tg*degree*(A/H)))
23 p_unsort
24 # step-3. Reorder
25 p_sort <- data %>%
26 arrange(y) %>%
27 mutate(x=factor(x,x)) %>%
28 ggplot(aes(x=x, xend=x, y=0, yend=y), color="skyblue", size=0.5) +
29 geom_segment(aes(x=x, xend=x, y=0, yend=y), color=ifelse(data$x %in% c("4", "10", "16", "21"), "orange", "skyblue"), size=ifelse(data$x %in% c("4", "10", "16", "21"), 1.3, 0.5) ) +
30
31 geom_point( color=ifelse(data$x %in% c("4", "10", "16", "21"), "orange", "grey"), size=ifelse(data$x %in% c("4", "10", "16", "21"), 5, 2) ) +
32 geom_point( color="slateblue1", size=4, alpha=0.6) +
33 geom_signif(comparisons = list(c("21", "20")), annotation="strong \nslope", map_signif_level=TRUE, vjust = 1.3, textsize = 3.0, family = "Times New Roman") +
34 geom_signif(comparisons = list(c("20", "2")), annotation="very strong \nslope", map_signif_level=TRUE, vjust = 1.3, textsize = 3.0, family = "Times New Roman") +
35 geom_signif(comparisons = list(c("2", "13")), annotation="extreme \nslope", map_signif_level=TRUE, vjust = 1.3, textsize = 3.0, family = "Times New Roman") +
36 geom_signif(comparisons = list(c("13", "3")), annotation="steep \nslope", map_signif_level=TRUE, vjust = 1.3, textsize = 3.0, family = "Times New Roman") +
37 geom_signif(comparisons = list(c("3", "24")), annotation="very steep \nslope", map_signif_level=TRUE, vjust = 0.5, textsize = 3.0, family = "Times New Roman") +
38 theme Light() +
39 # coord-flip() +
40 theme(
41 panel.grid.major.y = element-blank(),
42 panel border = element-blank(),
43 axis.ticks.y = element-blank(),
44 axis.title.y = element-text(margin = margin(t = 20, r = 3), family = "Times New Roman", face = 1, size = 10),
45 axis.title.x = element-text(family = "Times New Roman", face = 1, size = 10, margin = margin(t = .2)),
46 ) +
47 xlab("Profile Nr.") +
48 ylab(expression(tg*degree*(A/H)))
49 p_sort
50 # step-4 add both plots on one layout
51 figure <-plot_grid(p_unsort, p_sort, labels = c("1", "2"), ncol = 2, nrow = 1)
52 figure
53 # step-5. add common title, subtitle, overall design and lower subscript
54 Ranking <- figure +
55 labs(title="Mariana Trench, Profiles Nr.1-25.",
56 subtitle = "Geomorphological Analysis: Ranking of Profiles by Angle Steepness (Left: Unsorted; Right: Sorted and Grouped)",
57 caption = "Statistics Processing and Graphs: R Programming. Data Source: QGIS") +
58 theme(
59 plot.margin = margin(5, 10, 20, 5),
60 plot.title = element-text(margin = margin(t = 0, r = 20, b = 5, l = 0), family = "Kai", face = "bold", size = 12),
61 plot.subtitle = element-text(margin = margin(t = 0, r = 20, b = 4, l = 0), family = "Hei", face = "bold", size = 10),
62 plot caption = element-text(face = 2, size = 8),
63 panelancor=ggplot2::elementRect(fill = "white"),
64 legend justification = "bottom", legend position = "bottom",
65 legend.box.just = "right", legend direction = "horizontal",
66 legend.box = "horizontal", legend.boxancor = elementRect(colour = "honeydew4", size=0.2),
67 legendancor=elementRect(fill = "white"),
68 legend.key.width = unit(1,"cm"), legend.key height = unit(.5,"cm"),
69 legendSpacing.x = unit(.2,"cm"), legendSpacing.y = unit(.1,"cm"),
70 legend.text = element-text(colour="black", size=6, face=1),
71 legend.title = element-text(colour="black", size=6, face=1))
72 Ranking
```

codes/R-22.r
A.6.23 R code for Euler-Venn Diagram (logical correlation of objects) using (venn) library

Source data available on my GitHub.

1 # Part-1. Prepare dataframe
2 # step-1. Read table. Clean up NA
3 MDepths <- read.csv("Morphology.csv", header=TRUE, sep = ",")
4 MDF <- na.omit(MDepths)
5 row.has.na <- apply(MDF, 1, function(x){any(is.na(x))})
6 sum(row.has.na)
7 head(MDF)
8
9 # Part-2. Create Euler-Venn Diagram: 3 cases.
10 library(venn)
11 # case-1. Morphology and tectonic plates
12 x <- list(Philippine = MDF$plate_phill, Pacific = MDF$plate_pacif,
13 Mariana = MDF$plate_maria, Caroline = MDF$plate_carol,
14 Aspect = MDF$aspect_class,
15 Morphology = MDF$morph_class, Slope = MDF$slope_class)
16 venn(x, ilabels = TRUE, col = "navyblue", zcolor = "style")
17 # case-2. tectonics plates
18 xp <- list(Philippine = MDF$plate_phill, Pacific = MDF$plate_pacif,
19 Mariana = MDF$plate_maria, Caroline = MDF$plate_carol)
20 venn(xp, ilabels = TRUE, ellipse = TRUE, col = "navyblue",
21 zcolor = "style")
22 # case-3. Geomorphic parameters
23 x3 <- list(Depth = MDF$Min, Volcanoles = MDF$igneous_volc,
24 Sediments = MDF$sedim_thick,
25 Angle = MDF$slope_angle, Hillshade = MDF$hillshade,
26 Aspect = MDF$aspect_degree)
27 venn(x3, ilabels = TRUE, col = "navyblue", zcolor = "style")

codes/R-23.r
A.6.24 R code for animation using (gganimate), (gapminder) and (ggplot2) libraries: regression analysis plots for 25 bathymetric profiles

Source data available on my GitHub.

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# Part-1. prepare data frame
# step-1. read-in table with data. create initial data frame. clean data frame from the NA values
MDepths <- read.csv("Depths.csv", header=TRUE, sep = ",")
M1 <- na.omit(MDepths)
row.has.na <- apply(M1, 1, function(x){any(is.na(x))})
sum(row.has.na)
head(M1)
# step-2. merge several columns into one using (melt) function
MDTt = melt(setDT(M1), measure = patterns("^profile"), value.name = c("depth"))
head(MDTt)
# Part-2. create animation: how the bathymetric depths change by profiles 1:25.
# step-3
theme_set themeBW()) # pre-set the bw theme.
Observations<- MDTt$observ
Depth<- MDTt$depth
Profiles<- MDTt$variable
# step-4: 1 variant - single-color, by one method
g <- ggplot(MDTt, aes(x = Observations, y = Depth, frame = Profiles)) +
  geom_point() +
  geom.smooth(aes(group = Profiles), method = "loess", show.legend = TRUE)
gganimate(g, "Animation-blue-1-method.gif", interval=1.0)
# step-5: 2 variant - four colors, with 4 methods
g <- ggplot(MDTt, aes(x = Observations, y = Depth, frame = Profiles, color = "Observation points")) +
  geom_point() +
  geom.smooth(aes(group = Profiles, x = Observations, y = Depth, colour = "Loess method"), method = loess, se =
    TRUE, span = .4, size=.2, linetype = "solid", show.legend = TRUE) +
  geom.smooth(aes(group = Profiles, x = Observations, y = Depth, colour = "Glm method"), method = glm, se = TRUE,
    span = .4, size=.2, linetype = "dotted", show.legend = TRUE) +
  geom.smooth(aes(group = Profiles, x = Observations, y = Depth, colour = "Lm method"), method = lm, se = TRUE,
    size=.2, linetype = "solid", show.legend = TRUE) +
  geomader(aes(group = Profiles, x = Observations, y = Depth, colour = "Quantiles"), size=.2, linetype = "
    solid", show.legend = TRUE) +
  scale_color_manual(name = "Regression Analysis \nLegend:", values = c("Observation points" = "seagreen", "Loess
    method" = "red", "Glm method" = "orange", "Lm method" = "blue", "Quantiles" = "purple"))
# step-5: now run animation
gganimate(g, "Animation-color-4-methods.gif", interval=1.0)

codes/R-24.r
A.6.25 R code for 'violin' plots using (violinplot) library: statistical analysis of bathymetric data distribution

Source data available on my GitHub.

1 # Part-1. Prepare data frame
2 # step-1. read in table with data. create initial data frame. clean data frame from the NA
3 MDepths <- read.csv("Depths.csv", header=TRUE, sep = ",")
4 M1 <- na.omit(MDepths)
5 row.has.na <- apply(M1, 1, function(x){any(is.na(x))})
6 sum(row.has.na)
7 head(M1)
8 # step-2. Merge columns with identical attributes
9 MDTt = melt(setDT(M1), measure = patterns("^profile"), value.name = c("depth"))
10 head(MDTt)
11
12 # Part-2. Plot 'violin plot' - statistical analysis of bathymetric data distribution (mean, standard deviation, median).
13 # step-3. variant-1. via library(violinmplot)
14 library(violinmplot)
15 violinmplot(deep ~ variable, MDTt, horizontal=FALSE, col.violin = "blue", main = "Violin plot of the Mariana Trench: a hybrid of a Box plot and a Kernel probability density estimation plot (rotated) + median")
16 # step-4. variant-2. via library(ggplot2)
17 g<- ggplot(MDTt, aes(variable, depth)) +
18 geom_violin(aes(fill = factor(variable)), colour = "blue", size = 0.2, drawader = c(0.25, 0.5, 0.75),
19 alpha = 0.5, scale = "count", trim = FALSE)
20 g
21 # step-5. add title, axes, etc.
22 Violin<- g +
23 xlab("Profiles") +
24 ylab("Depth, m") +
25 labs(
26 title = "Mariana Trench, Profiles 1-25.",
27 subtitle = "Violin plot of the Mariana Trench: \na hybrid of a Box plot and a Kernel probability density
28 estimation plot (rotated). \nMedian and Quantiles (0.25, 0.5, 0.75)",
29 caption = "Statistics Processing and Graphs: \nR Programming. Data Source: QGIS") +
30 theme(
31 plot-margin = margin(5, 10, 20, 5),
32 plot-title = element_text(family = "Kai", face = "bold", size = 12),
33 plot.subtitle = element_text(family = "Hei", face = "bold", size = 10),
34 plot标题 = element_text(face = 2, size = 8),
35 panel. background = ggplot2::elementRect(fill = "white"),
36 legend justification = "right",
37 legend. position = "right",
38 legend. box. just = "right",
39 legend. direction = "vertical",
40 legend. box = "vertical",
41 legend. box. background = elementRect(colour = "honeydew4", size=0.2),
42 legend. background = elementRect(fill = "white"),
43 legend. key. width = unit(.5, "cm"),
44 legend. key. height = unit(.5, "cm"),
45 legend. spacing. x = unit(.2, "cm"),
46 legend. spacing. y = unit(.1, "cm"),
47 legend. text = element_text(colour="black", size=6, face=1),
48 legend. title = element_text(colour="black", size=8, face=1),
49 strip. text. x = element_text(colour = "white", size=6, face=1),
50 panel. grid. major = element_line("gray24", size = 0.1, linetype = "solid"),
51 panel. grid. minor = element_line("gray24", size = 0.1, linetype = "dotted"),
52 axis. text. x = element_text(face = 3, color = "gray24", size = 6, angle = 15),
53 axis. text. y = element_text(face = 3, color = "gray24", size = 6, angle = 15),
54 axis. ticks. length = unit(.1, "cm"),
55 axis. line = element_line(size = .3, colour = "grey80"),
56 axis. title. y = element_text(margin = margin(t = 20, r = .3), face = 2, size = 8),
57 axis. title. x = element_text(face = 2, size = 8, margin = margin(t = .2))) +
58 guides(col = guide.legend(nrow = 13, ncol = 2, byrow = TRUE)) # improve design
59 Violin

codes/R-25.r
A.6.26 R code for Exploratory Factor Analysis (EFA) using (FactoMiner), (factoextra) and (psych) libraries

Source data available on my GitHub.

1 # libraries: 'psych' (MAIN), 'factoextra', 'FactoMiner' zip, openxlsx, carData, pbkrtest, rio, car, flashClust, leaps, scatterplot3d, FactoMineR, ca, igraph
2 # Part 1 create data frame
3 # step-1. read in table, create data frame.
4 MDepths <- read.csv("MorphologyFA.csv", header=TRUE, sep = ",")
5 MDF<- na.omit(MDepths)
6 row.has.na <- apply(MDF, 1, function(x) {any(is.na(x))})
7 sum(row.has.na)
8 # Part 2 Exploratory Factor Analysis (EFA)
9 # step-2. Create Correlation Matrix
10 corMat <- cor(MDF)
11 corMat
12 # step-3. Factor Analysis (method: Principal Axis)
13 faM <- fa corMat, nfactors = 2, rotate = "oblimin", scores = "regression", residuals = FALSE, SMC=TRUE, covar= FALSE, missing = FALSE, impute = "median", min.err = 0.001, max.iter = 50, symmetric = TRUE, warnings = TRUE, fm = "pa", alpha = .1, p = .05, oblique.scores = FALSE, npobs = NULL, use = "pairwise", cor = "cor", correct = .5, weight = NULL)
14 print(faM,sort=TRUE) # print the results from a factor analysis
15 plot(faM) # plot the loadings from a factor analysis
16 fa.plot(faM) # plot the loadings from a factor, principal components, or cluster analysis
17 # step-4. Parallel Factor Analysis compares the observed eigen values of a correlation matrix with those from random data
18 fa.parallel corMat)
19 # step-5. Weighted Least Squares Factor Analysis
20 faWls <- step corMat, nfactors = 2, rotate = "oblimin", scores = "regression", residuals = FALSE, SMC=TRUE, covar=FALSE, missing = FALSE, impute = "median", min.err = 0.001, max.iter = 50, symmetric = TRUE, warnings = TRUE, fm = "wls", alpha = .1, p = .05, oblique.scores = FALSE, npobs = NULL, use = "pairwise", cor = "cor", correct = .5, weight = NULL)
21 print(faWls,sort=TRUE) # print the results from a factor analysis
22 plot(faWls)
23 # step-6. Item Cluster Analysis: iclust
24 iclust corMat)
25 ic <- iclust corMat)
26 summary(ic)
27 # step-7. Hierarchical and bi-factor solutions using the omega function
28 om.h <- omega corMat, nobs=25, sl=FALSE)
29 om <- omega corMat, nobs=25)
30 # step-8. Alpha Factor Analysis
31 faMal <- fa corMat, nfactors = 2, rotate = "oblimin", scores = "regression", residuals = FALSE, SMC=TRUE, covar=FALSE, missing = FALSE, impute = "median", min.err = 0.001, max.iter = 50, symmetric = TRUE, warnings = TRUE, fm = "alpha", alpha = .1, p = .05, oblique.scores = FALSE, npobs = NULL, use = "pairwise", cor = "cor", correct = .5, weight = NULL)
32 print(faMal,sort=TRUE) # print the results from a factor analysis
33 plot(faMal)

codes/R-26.r
A.6.27 R code for hexagonal plot by library(hexbin): bathymetric data distribution, Mariana Trench

Source data available on my GitHub.

1
# Part-1. prepare data frame
2
# step-1. read in table with data. create initial data frame
3
MDepths <- read.csv("Depths.csv", header=TRUE, sep = ",")
4
M1 <- na.omit(MDepths)
5
row.has.na <- apply(M1, 1, function(x){any(is.na(x))})
6
sum(row.has.na)
7
head(M1)
8
# step-2. merge columns by categories
9
MDTt = melt(setDT(M1), measure = patterns("^profile"), value.name = c("depth"))
10
head(MDTt)
11
12
# Part-2. hexbin
13
# 1 variant, via library(hexbin)
14
x = as.factor(MDTt$variable)
15
y = MDTt$depth
16
hexbinplot(y ~ x, MDTt, aspect = 1,
17
trans = sqrt, inv = function(x) x^2)
18
x = MDF$profile
19
y = MDF$Min
20
hexbinplot(y ~ x, MDF, aspect = 1,
21
trans = sqrt, inv = function(x) x^2)
22
# 2 variant via function (plot bin)
23
bin <- hexbin(x, y)
24
plot.bin, style = "nested.lattice")
25
plot.bin, style = "nested.lattice")
26
# statistics on hex bin plot
27
hbi <- hexbin(y ~ x, xbins = 80, IDs= TRUE)
28
str(hbi)
29
tI <- table(hbi@cID)
30
stopifnot Exhibit names (tI) == hbi@cell,
31
tI == hbi@count)

codes/R-27.r
A.6.28 R code for ANOVA for Mariana Trench

Source data available on my GitHub.

1 # Part-1. prepare data frame
2 # step-1. read in table with data. create initial data frame
3 MDepths <- read.csv("Morphology.csv", header=TRUE, sep = ",")
4 MDF <- na.omit(MDepths)
5 row.has.na <- apply(MDF, 1, function(x){any(is.na(x))})
6 sum(row.has.na)
7 head(MDF)
8 # step-2. Merge identical columns into a category
9 MDTt = melt(setDT(MDF), measure = patterns("^plate"), value.name = c("tectonics"))
10 head(MDTt)
11 # Part-2. Compute ANOVA
12 # step-3. Compute one-way ANOVA test using function aov(): to know if there is any significant difference
13 # Interpret the result of one-way ANOVA tests: As the p-value is less than the significance level 0.05, we can
14 # conclude that there are significant differences between the groups highlighted with *"" in the model
15 # step-4. Compute the analysis of variance
16 res.aov <- aov(tectonics ~ variable, data = MDTt)
17 summary(res.aov) # Summary of the analysis
18 # step-5. Tukey multiple pairwise-comparisons. As the ANOVA test is significant, we can compute Tukey HSD (
19 # Tukey Honest Significant Differences, R function: TukeyHSD()) for performing multiple pairwise-comparison
20 # between the means of groups. The function TukeyHD() takes the fitted ANOVA as an argument.
21 TukeyHSD(res.aov)
22 # diff: difference between means of the two groups
23 # lwr, upr: the lower and the upper end point of the confidence interval at 95% (default)
24 # p adj: p-value after adjustment for the multiple comparisons.
25 # It can be seen from the output, that only the difference between plate Mariana and plate Pacific is
26 # significant (=24.12) with an adjusted p-value of 0.8601221.
27 # step-6. 2 variant via library(multcomp) Pairwise t-test. The function pairwise.t.test() can be also used to
28 # calculate pairwise comparisons between group levels with corrections for multiple testing:
29 pairwise.t.test(MDTt$tectonics, MDTt$variable, pzeadjust.method = "BH")
30 # step-7. Plot Homogeneity of variances
31 plot(res.aov, 1)
32 # step-8. Bartletts test or Levene's test to check the homogeneity of variances. Levene's test is less sensitive
33 # to departures from normal distribution. The function leveneTest() [in car package] :
34 library(car)
35 leveneTest(tectonics ~ variable, data = MDTt)
36 # step-9. Welch one-way test. Welch one-way test does not require that assumption have been implemented in the
37 # function oneway.test(). ANOVA test with no assumption of equal variances
38 oneway.test(tectonics ~ variable, data = MDTt)
39 # step-10. Shapiro test
40 aov_residuals <- residuals(object = res.aov )
41 shapiro.test(x = aov_residuals )
42 # step-11. non-parametric alternative to one-way ANOVA is Kruskal-Wallis rank sum test,
43 kruskal.test(tectonics ~ variable, data = MDTt)

codes/R-28.r
A.6.29 R code for Ternary Diagram by (ggtern) library

Source data available on my GitHub.

1 # ternaries
2 # Part 1: create data frame, delete NA values
3 # step-1. read in table with data.
4 MDF <- read.csv("Morphology.csv", header=TRUE, sep = ",")
5 MDF <- na.omit(MDF)
6 row.has.na <- apply(MDF, 1, function(x){any(is.na(x))})
7 sum(row.has.na)
8 head(MDF)
9 # step-2. merge columns
10 MDTt = melt(setDT(MDF), measure = patterns("^plate"), value.name = c("tectonics"))
11 head(MDTt)
12 levels(MDTt$variable) = c("Philippine", "Pacific", "Mariana", "Caroline")
13 Plates <- c("Philippine", "Pacific", "Mariana", "Caroline")
14
15 # Part 2: draw ternary diagram for Mariana Trench
16 library(ggtern)
17 # step-3. variant-1. 4 tectonic plates
18 MDTer <- data.frame(
19 x = MDTt$igneous_volc,
20 y = MDTt$tectonics,
21 z = MDTt$slope_angle,
22 Value = MDTt$slope_angle,
23 Group = as.factor(MDTt$variable))
24 MT1 <- ggtern(data = MDTer, aes(x,y,z, color = Group)) +
25 theme +
26 geom_point() +
27 # geom_path() +
28 scale_color_manual(values = c("green", "red", "orange", "blue")) +
29 labs(x = "Igneous \nVolcanos",
30 y = "Tectonics",
31 z = "Slope \nAngle",
32 title = "Mariana Trench",
33 subtitle = "Ternary Diagram: Tectonic Plates") +
34 geom_Tline(Tintercept = .5, arrow = arrow(), colour = 'red') +
35 geom_Lline(Lintercept = .2, colour = 'magenta') +
36 geom_Rline(Rintercept = .1, colour = 'blue') +
37 geom_confidence_tern()
38 MT1
39 # step-4. variant-2. by morphology
40 levels(MDTt$morph_class) = c("Strong Slope", "Very Strong Slope", "Steep Slope", "Extreme Slope")
41 MDTM <- data.frame(
42 x = MDTt$Min,
43 y = MDTt$aspect_degree,
44 z = MDTt$slope_angle,
45 Value = MDTt$slope_angle,
46 Group = as.factor(MDTt$morph_class))
47 MT2 <- ggtern(data = MDTM, aes(x,y,z, color = Group), show.legend = TRUE) +
48 theme +
49 geom_point() +
50 geom_path() +
51 labs(x = "Max \nDepth",
52 y = "Aspect Degree",
53 z = "Slope \nAngle",
54 title = "Mariana Trench",
55 subtitle = "Ternary Diagram: Slope Morphology Class") +
56 geom_Tline(Tintercept = .5, arrow = arrow(), colour = 'red') +
57 geom_Lline(Lintercept = .2, colour = 'magenta') +
58 geom_Rline(Rintercept = .1, colour = 'blue') +
59 geom_confidence_tern() +
60 geom_Tisoprop(value = 0.5) +
61 geom_Lisoprop(value = 0.5) +
62 geom_Risoprop(value = 0.5)
63 MT2
64 # step-5. variant-3 by slope aspect
65 MDTAs <- data.frame(
```r
66 x = MDTt$slope_angle,
67 y = MDTt$aspect_degree,
68 z = MDTt$Min,
69 Value = MDTt$aspect_degree,
70 Group = as.factor(MDTt$aspect_class))
71 MT3<- ggtern(data = MDTAs, aes(x,y,z, color = Group)) +
72 theme RGBW +
73 geom_point() +
74 scale_color_manual(values = c("green", "red", "orange", "blue", "yellow", "brown", "grey", "cyan")) +
75 labs(x="Slope \nAngle", size = 1,
76 y="Aspect \nDegree",
77 z="Max \nDepth",
78 title="Mariana Trench",
79 subtitle="Ternary Diagram: Aspect Class") +
80 geom_Tline(Tintercept=.5, arrow=arrow(), color='red') +
81 geom_Lline(Lintercept=.2, color='magenta') +
82 geom_Rline(Rintercept=.1, color='blue') +
83 geom_confidence_tern() +
84 geom_Tisoprop(value=0.5) +
85 geom_Lisoprop(value=0.5) +
86 geom_Risoprop(value=0.5)
87 MT3
88 # step-6. plotting ternaries
89 plot<- grid.arrange(MT1, MT2, MT3, newpage = TRUE, nrow = 1, ncol = 3, top="Mariana Trench")
90 # step-7. variant-4. by sediment thickness layer
91 MD4 <- data.frame(
92 x = MDTt$igneous_volc,
93 y = MDTt$sedim_thick,
94 z = MDTt$slope_angle,
95 Value = MDTt$slope_angle)
96 MT4<- ggtern(data = MD4, aes(x,y,z), show.legend=TRUE) +
97 theme RGBW +
98 geom_point() +
99 # geom_path(alpha = .5, lwd = 0.2) +
100 labs(x="Igneous \nVolcanos", size = 0.5, y="Sediment \nThickness", z="Slope \nAngle",
101 title="Mariana Trench",
102 subtitle="Ternary Diagram: Sediment Thickness") +
103 geom_Tline(Tintercept=.5, arrow=arrow(), color='deeppink') +
104 geom_Lline(Lintercept=.2, color='magenta') +
105 geom_Rline(Rintercept=.1, color='springgreen') +
106 geom_confidence_tern() +
107 geom.smooth_tern(method = 'loess', size = .4, color = "yellow1") +
108 geom_mean_ellipse(size = .5, color = "cyan")
109 MT4
110 figure <- plot_grid(MT1, MT2, MT3, MT4, labels = c("1", "2", "3", "4"), ncol = 2, nrow = 2)
```

codes/R-29.r
## A.6.30 R code for silhouette plot by (cluster) library

Source data available on my GitHub.

1
2 MarianaTrench DF <- read.csv("Morphology.csv", header=TRUE, sep = ",")
3 MarianaTrench DF <- na.omit(MarianaTrench DF)
4 row.has.na <- apply(MarianaTrench DF, 1, function(x) {any(is.na(x))})
5 sum(row.has.na)
6 head(MarianaTrench DF)
7 # step-2. generate silhouette plot by (cluster) library
8 library cluster)
9 pr5 <- pam(MarianaTrench DF, 6)
10 str(si <- silhouette(pr5))
11 #plot(si, col = c("lavender", "lightgoldenrodl", "darkseagreen1", "orchid1", "skyblue1", "lightslateblue"))
12 #plot(si, col = c("aquamarine", "aquamarine1", "aquamarine2", "aquamarine3", "aquamarine4", "aquamarine4"))
13 plot(si, col = c("thistle", "thistle1", "thistle2", "thistle3", "thistle4", "slategray4"))

codes/R-30.r

## A.6.31 R code for mosaic and association plots

Source data available on my GitHub.

1
2 MDF <- read.csv("Morphology.csv", header=TRUE, sep = ",")
3 MDF <- na.omit(MDF)
4 row.has.na <- apply(MDF, 1, function(x) {any(is.na(x))})
5 sum(row.has.na)
6 head(MDF)
7 # step-2. merge columns by categories (4 tectonic plates)
8 MDTt = melt(setDT(MDF), measure = patterns("^plate"), value.name = c("tectonics"))
9 head(MDTt)
10 levels(MDTt$variable) = c("Philippine Plate", "Pacific Plate", "Mariana Plate", "Caroline Plate")
11 head(MDTt)
12 # step-3. plot mosaic by 4 tectonic plates
13 mosaicplot(MDF, color = NULL, main = "Title")
14 count <- table(MDTt$variable, MDTt$tectonics)
15 count
16 # step-4. variant-1 normal plot
17 MosaicTect <- mosaicplot(count, main = "Mariana Trench: \nMosaic Plot",
18                      sub = "1:518 observation points in each profile",
19                      xlab = "Tectonic Plates", ylab = "Profiles", las = 1,
20                      color = c("steelblue1", "royalblue1", "dodgerblue", "steelblue3", "cornflowerblue", "royalblue4"),
21                      border = "tomato")
22 MosaicTect
23 # step-5. variant-2 - plot with residuals
24 MosaicTectRes <- mosaicplot(count, main = "Mariana Trench: \nMosaic Plot",
25                       sub = "1:518 observation points in each profile",
26                       xlab = "Tectonic Plates", ylab = "Profiles", las = 1,
27                       border = "chocolate",
28                       shade = TRUE)
29 MosaicTectRes
30 # step-6. via library(vcd) merge columns by categories (4 tectonic plates)
31 MDTt = melt(setDT(MDF), measure = patterns("^plate"), value.name = c("tectonics"))
32 head(MDTt)
33 # step-7.
34 count <- table(MDF$geom_sedim_thick, MDF$geom_slope_angle)
35 library(vcd)
36 mosaic(count, shade=TRUE, legend=TRUE)
37 # step-8.
38 # Association Plot Example
39 library(vcd)
40 assoc(count, shade=TRUE)

codes/R-31.r
A.6.32 R code plot correlation ellipses using library(ellipse)

Source data available on my GitHub.

1
2 MDF <- read.csv("Morphology.csv", header=TRUE, sep = ",")
3 MDF <- na.omit(MDF)
4 row.has.na <- apply(MDF, 1, function(x) (any(is.na(x))))
5 sum(row.has.na)
6 head(MDF)
7 # step-2. plot correlation ellipses using libraries (ellipse) and (RColorBrewer)
8 data=cor(MDF)
9 # Build a panel of 100 colors with Rcolor Brewer
10 my_colors <- brewer.pal(5, "Spectral")
11 my_colors=colorRamp Palette(my_colors)(100)
12 # step-3. Order correlation matrix
13 ord <- order(data[1,])
14 data_ord = data[ord, ord]
15 # step-4. variant-1 correlation ellipses
16 plotcorr(data_ord, col=my_colors(data_ord*50+50), mar=c(1,1,1,1),
17 outline = TRUE, numbers = FALSE,
18 main = "Mariana Trench: Correlation Ellipses",
19 xlab = "Geomorphological, bathymetric and geological factors",
20 ylab = "Geomorphological, bathymetric and geological factors",
21 cex.lab = 0.7)
22 # step-5. variant-2 correlation numbers
23 plotcorr(data_ord, col=my_colors(data_ord*50+50), mar=c(1,1,1,1),
24 outline = TRUE, numbers = TRUE,
25 main = "Mariana Trench: Numerical Correlation",
26 xlab = "Geomorphological, bathymetric and geological factors",
27 ylab = "Geomorphological, bathymetric and geological factors",
28 cex.lab = 0.7)

codes/R-32.r

A.6.33 R code for level plot using (ggplot2) library: sediment thickness

Source data available on my GitHub.

1
2 MDF <- read.csv("Morphology.csv", header=TRUE, sep = ",")
3 MDF <- na.omit(MDF)
4 row.has.na <- apply(MDF, 1, function(x) (any(is.na(x))))
5 sum(row.has.na)
6 head(MDF)
7 # step-2. set up values XYZ
8 x <- MDF$slope_angle
9 y <- MDF$depth_min
10 data <- expand.grid(X=x, Y=y)
11 data$Z <- MDF$sedim_thick
12 # step-3. generate levelplot by (ggplot2) library
13 ggplot(data, aes(X, Y, z = Z)) + geom tile(aes(fill = Z)) +
14 theme bw() +
15 scale_fillgradient(name = "Sediment 
" Thickness", low="white", high="blue") +
16 labs(
17 title = "Mariana Trench",
18 subtitle = "Levelplot of Sediment Thickness",
19 x = "Trench slope angle",
20 y = "Depth (m)")

codes/R-33.r
A.6.34 R code for hierarchical edge funding by (ggraph), (igraph), (tidyverse), (RColor-Brewer) libraries: Mariana Trench project

Source data available on my GitHub.

1 # Part-1
2 # step-1. Create hierarchical structure of the data frame (groups and leaves)
3 d1=data.frame(from="origin", to=paste("profile", seq(1:25), sep="_"))
4 d2=data.frame(from=rep(d1$to, each=4), to=paste(c("Slope angle", "Bathymetry", "Sediment thickness", "Tectonic plates"), seq(1,100)))
5 edges=rbind(d1, d2)
6 # step-2. Create connections between the individual leaves
7 all_leaves=paste(c("Slope angle", "Bathymetry", "Sediment thickness", "Tectonic plates"), seq(1,100))
8 connect=rbind( data.frame( from=sample(all_leaves, 100, replace=T) , to=sample(all_leaves, 100, replace=T)),
    data.frame( from=sample(head(all_leaves), 30, replace=T) , to=sample( tail(all_leaves), 30, replace=T)),
    data.frame( from=sample(all_leaves[25:30], 30, replace=T) , to=sample( all_leaves[55:60], 30, replace=T)),
    data.frame( from=sample(all_leaves[175:180], 30, replace=T) , to=sample( all_leaves[155:160], 30, replace =T) ) )
9 connect$mvalue=runif(nrow connect))
10 # step-3. Create vertices in a data.frame: one line per object of the hierarchy
11 vertices = data.frame(name = unique(c(as character( edges$from), as character( edges$to))) , value = runif(126))
12 # step-4. Add column with groups names, to color points by groups
13 vertices$group = edges$from[ match( vertices name, edges$to ) ]
14
15 # Part-2. Add information on labels: slope angles
16 # step-5.
17 vertices $id=NA
18 myleaves=which(is.na( match( vertices name, edges$from) ))
19 nleaves =length(myleaves)
20 vertices $id[ myleaves ] = seq(1:nleaves)
21 vertices$angle= 90 - 360 * vertices $id / nleaves
22 # step-6. calculate the alignment of labels: right or left
23 vertices$hjust<-ifelse( vertices$angle < -90, 1, 0)
24 # step-7. flip angle BY to make them readable
25 vertices$angle<-ifelse( vertices$angle < -90, vertices$angle+180, vertices$angle)
26
27 # Part-3 Create graphic object
28 # step-8. The connection object must refer to the ids of the leaves:
29 mygraph <- graph_from_data_frame( edges, vertices=vertices )
30 from = match( connect$from, vertices name)
31 to = match( connect$to, vertices name)
32
33 # Part-4 Plotting
34 # step-9.
35 M<- ggraph(mygraph, layout = 'dendrogram', circular = TRUE) +
36 geom_node_point( aes( filter = leaf, x = x*1.07, y=y*1.07, colour=group, size=value), alpha=0.2) +
37 geom_node_text( aes( x = x*1.15, y=y*1.15, filter = leaf, label=name, angle = angle, hjust=hjust, colour=group),
    size=2, alpha=1) +
38 geom_connBundle( data = get_con( from = from, to = to), alpha=0.2, width=0.5, aes( colour =.. index..)) +
39 scale_edgecoeur_distiller( palette = "BuPu") +
40 scale_colour_manual( values= rep( brewer.pal(9, "Paired"), 30)) +
41 scale_sizeQEontinuous( range = c(0.1,10) ) +
42 theme void() +
43 expand limits( x = c(-1.4, 1.4), y = c(-1.4, 1.4)) +
44 labs(
45 title = "Mariana Trench",
46 subtitle = "Structured Edge Bundling: 
\nConnections Between Leaves of a Hierarchical Network") +
47 theme(
48 legend. position="right",
49 legend. text = element_text( family = "Times New Roman", colour="black", size=6, face=1),
50 legend. title = element_text( family = "Times New Roman", colour="black", size=8, face=1),
51 plot. title = element_text( family = "Times New Roman", face = 2, size = 12),
52 plot. subtitle = element_text( family = "Times New Roman", face = 1, size = 10),
53 plot. margin = unit( c(0.5,0.5,0.5,0.5), "cm")
54 M

codes/R-34.r
### A.6.36 R code for word could by libraries (wordcloud), (tm), (RXKCD): Mariana Trench project

Source data available on my GitHub.

1 # step-1. text repository "Corpus". For that create a folder in a working directory, where the .txt file is stored. All texts from this folder are being uploaded into corpus. Then the tm package process it:
2 library(RXKCD)
3 library(tm)
4 library(wordcloud)
5 library(RColorBrewer)
6 library(SnowballC)
7 Mariana <- Corpus(DirSource("/Users/pauline/Documents/R/36_SCRIPT-Wordcloud/text/"))
8 # step-2. look up
9 inspect(Mariana)
10 # step-3. clean up and sort corpus of words:
11 Mariana <- tm_map(Mariana, stripWhitespace)
12 Mariana <- tm_map(Mariana, tolower)
13 Mariana <- tm_map(Mariana, removeWords, stopwords("english"))
14 Mariana <- tm_map(Mariana, stemDocument)
15 # step-4. plot word could:
16 wordcloud(Mariana, scale=c(5,0.3),
17 min freq=1,
18 max words=300,
19 random.order=F,
20 rot.per=0.35, use.rlayout=FALSE,
21 colors=brewer.pal(8, "Dark2"),
22 vfont=c("sans serif","plain"))

codes/R-36.r

### A.6.37 R code for 'waffle' plot by library (ggplot2): bathymetric data distribution

Source data available on my GitHub.

1 # step-1 load table with data. create data frame
2 MDF <- read.csv("Morphology.csv", header=TRUE, sep = ",")
3 MDF <- na.omit(MDF)
4 row.has.na <- apply(MDF, 1, function(x) (any(is.na(x))))
5 sum(row.has.na)
6 head(MDF)
7 # step-2. variant-1: aspect class.
8 var <- MDF$aspect_class # This is a categorical data. Here: 8 aspect classes
9 nrows <- 10
10 df <- expand.grid(y = 1:nrows, x = 1:nrows) # set up square 10*10
11 categ_table <- round(table(var) * ((nrows*nrows)/(length(var)))) # set up table with categorial values
12 categ_table
13 df$category <- factor.names(categ_table), categ_table) # names to be shown in a legend
14 # note: if sum(categ_table) is not 100 (i.e. nrows^2), it will need adjustment to make the sum to 100.
15 # step-3. Plot 'waffle' diagram
16 wa<- ggplot(df, aes(x = x, y = y, fill = category)) +
17 geom tile (color = "black", size = 0.5) +
18 scale_x continuous (expand = c(0, 0)) +
19 scale_y continuous (expand = c(0, 0), trans = 'reverse') +
20 scale_fill_brewer ( palette = "Set3") +
21 labs (title="Waffle Chart: Mariana Trench", subtitle="Aspect class of the basement angle",
22 caption="Graphics: R Programming") +
23 theme( plot. title = element_text (size = rel(1.2)),
24 axis. text = elementos blank(),
25 axis. title = elementos blank(),
26 axis. ticks = elementos blank(),
27 legend. title = elementos blank(),
28 legend. position = "right")
29 wa
30 # step-4. variant-2: slope steepness class. create table and load data.
31 var <- MDF$morph_class # the categorical data: slope steepness class
32 nrows <- 10
33 df <- expand.grid(y = 1:nrows, x = 1:nrows) # 10*10
34 categ_table <- round(table(var) * ((nrows*nrows)/(length(var)))) # set up table with categorial values
35 categ_table
36 df$category <- factor.names(categ_table, categ_table)) # names to be shown in a legend
37 # step-5. Plot waffle diagram by steepness
38 ws<- ggplot(df, aes(x = x, y = y, fill = category)) +
39 geom tile (color = "black", size = 0.5) +
40 scale_x continuous (expand = c(0, 0)) +
41 scale_y continuous (expand = c(0, 0), trans = 'reverse') +
42 scale_fill_brewer ( palette = "Set3") +
43 labs (title = "Waffle Chart: Mariana Trench", subtitle = "Steepness angle class of the profiles 1:25",
44 caption = "Graphics: R Programming") +
45 theme( plot. title = element_text (size = rel(1.2)),
46 axis. text = element Marginal,
47 axis. title = element Marginal,
48 axis. ticks = element Marginal,
49 legend. title = element Marginal,
50 legend. position = "right")
51 ws
52 #step-6. combine both waffles on one plot
53 figure <- plot_grid(wa, ws, labels = c("1", "2"), ncol = 2, nrow = 1)
54 figure

codes/R-37.r

A.6.38 R code for 'waffle' plot by library (waffle): bathymetric data distribution

Source data available on my GitHub.

1 # step-1. load table
2 MDF <- read.csv("Morphology.csv", header=TRUE, sep = ",")
3 MDF <- na.omit(MDF)
4 row.has.na <- apply(MDF, 1, function(x) (any(is.na(x))))
5 sum(row.has.na)
6 head(MDF)
7 # step-2. variant-1 for aspect class
8 library(waffle)
9 var <- MDF$aspect_class
10 nrows <- 10
11 df <- expand.grid(y = 1:nrows, x = 1:nrows)
12 categ_table <- round(table(var) * ((nrows*nrows)/(length(var))))
13 categ_table
14 aspect <- c("East"=8, "North"=24, "North-East" = 8, "North-West" = 16, "South" = 4, "South-East" = 12, "South-West" = 16, "West" = 12 )
15 aspect
16 wa<- waffle(aspect, rows=10, size=0.5,
17 colors=c("aquamarine", "cadetblue1", "darkseagreen1", "papayawhip", "thistle1", "lavender", "lightsteelblue1", "mistyrose"),
18 title=list("Aspect degree in basement. 
Mariana Trench, bathymetric profiles 1:25", size=0.3), xlab="total square: 100%")
19 wa
20 # step-3. variant-2 for morphology (angle steepness)
21 var <- MDF$morph_class
22 nrows <- 10
23 df <- expand.grid(y = 1:nrows, x = 1:nrows)
24 categ_table <- round(table(var) * ((nrows*nrows)/(length(var))))
25 categ_table
26 morph <- c("Extreme Slope"=24, "Steep Slope" =20, "Strong Slope" = 16, "Very Steep Slope" = 40)
27 morph
28 wa<- waffle(morph, rows=10, size=0.5,
29 title=list("Steepness angle morphology class. 
Mariana Trench, bathymetric profiles 1:25", size=0.3),
30 xlab="total square: 100%")
31 wa
32 # step-4. combine both plots together on one layout
33 iron(wa, wa)

codes/R-38.r
## A.6.39 R code for treemap by library (treemap)

Source data available on my GitHub.

```R
library(treemap)

# step-1. Create a dataset
group=c(rep("tectonics",4), rep("bathymetry",10), rep("geology",2))
subgroup=paste(c("Mariana Plate", "Philippine Plate", "Pacific Plate", "Caroline Plate"),
                "Depth \n(absolute maximum)", "Slope angle", "Aspect degree", "Depth (Mean)", "Depth (Median)", "Aspect class",
                "Steepness class", "Depth \n(1 quart.)", "Depth \n(3 quart.)", "tg angle",
                "sedimental \nthickness", "igneous \nvolcanic \nspots"))
value=c(22,14,18,7,12,10,6,4,5,9,10,3,2,7,14,17)
data=data.frame(group, subgroup, value)

# step-2. Plot treemap
treemap(data,
        title = "Mariana Trench Impact Factors",
        index=c("group", "subgroup"),
        # palette="Set2"
        # palette="Set1",
        palette="Accent",
        vSize="value",
        type="index",
        fontsize.labels=c(18,12), # size of labels per level of aggregation: group, subgroup, subsubgroups
        fontcolor.labels=c("white", "gray40"),
        fontface.labels=c(2,1), # Fonts: 1,2,3,4 for normal, bold, italic, bold-italic
        bg.labels=c("transparent"), # Background color of labels
        align.labels=list(c("center", "center"), c("center", "center")),
        overlap.labels=0.9,
        inflate.labels=F, # If true, labels are bigger when rectangle is bigger.
        border.col=c("gray48", "gray30"), # Color of borders of groups, of subgroups
        border.lwds=c(4,1)
)
```

codes/R-39.r

## A.6.40 R code for network by (igraph) library: Mariana Trench project

Source data available on my GitHub.

```R
# Important notes on methodology. Here: I build a flowchart-diagram of hierarchical structure of the 'Mariana Trench' project work, i.e. methods used in the research (12, hierarchy of 1 level), methods used (total 100, hierarchy of 2 level). For instance, class 'GIS analysis' has sub-types 'GIS projections', ' visualization', 'cartography', 'spatial analysis' etc. Correlation class has sub-types 'k-means clustering', 'correlograms', 'dendrograms' etc. Here: create connections between the items (connections go by ID, here: 12 main joints of 1 hierarchical level - centroids (1:12): connections between the hierarchies of level 1, from 13 up to 111: nodes of the hierarchy of 2 level. Connections from levels 1 to 2. All nodes by both levels are stored in a table together (nodes.csv). A table has 2 columns: ID and names. Table connections.csv stores connections, e.g. level 1 towards sublevels 13,14,15,16 etc. Level 2: towards its sub-levels etc. There are 2 tables: 'nodes.csv' and 'connections.csv'. Column 'weight' of connections.csv keeps importance of connections.

# step-1. Preparing data and network
library(igraph)

nodes <- read.table("nodes.csv", header = T, sep = ",", stringsAsFactors = FALSE)
connections <- read.table("connections.csv", header = T, sep = ",", stringsAsFactors = FALSE)
net <- graph_from_data_frame connections, directed = F, vertices = nodes

#V(net)$color <- "orange"
#V(net)$label <- "name"

V(net)$size <- 10
E(net)$arrow.mode <- 0
vertex.label= nodes$name

# step-2. Plotting
library(RColorBrewer)
coul = brewer.pal(8, "Set1")
plot(net, vertex.label= nodes$name, vertex.color = coul, vertex.size=4, vertex.label font = 1, vertex.label.cex = .6, vertex.label.dist = 3.0, vertex.frame.color = 'blue')
```

codes/R-40.r
A.6.41 R code for scatterplot matrices by (GGally) library to assess data distribution, Mariana Trench

Source data available on my GitHub.

1 #Part-1
2 # Scatterplot matrix by GGally
3 require(GGally)
4 # step-1. read in table. create data frame
5 MDFGeo <- read.csv("GeoMorphology.csv", header=TRUE, sep = ",")
6 # step-2. clean up data frame from NA values
7 MDFGeo <- na.omit(MDFGeo)
8 row.has.na <- apply(MDFGeo, 1, function(x){any(is.na(x))})
9 sum(row.has.na)
10 head(MDFGeo)
11 # step-3. Check correlation between variables
12 cor(MDF)
13 # step-4. variant-1. Check correlations (as scatterplots), distribution and print correlation coefficient
14 pi<- ggpairs(MDFGeo, axisLabels = "show",
15 title = "Mariana Trench: Scatterplot Correlation Matrix",
16 labeller = "label_parsed",
17 upper = list(continuous = wrap("density", alpha = 0.5), combo = "box_no_facet"),
18 lower = list(combo = "denstrip")
19 pi
20 pair<- pi + theme(axis.text.x = element_text(face = 3, color = "gray24", size = 6, angle = 15),
21 axis.text.y = element_text(face = 3, color = "gray24", size = 6, angle = 15))
22 pair
23 # step-5. variant-2, with numerical correlations
24 pin<- ggpairs(MDFGeo, axisLabels = "show",
25 title = "Mariana Trench: Scatterplot Correlation Matrix", )
26 pin
27 pair<- pi + theme(axis.text.x = element_text(face = 3, color = "gray24", size = 6, angle = 15),
28 axis.text.y = element_text(face = 3, color = "gray24", size = 6, angle = 15))
29 pair
30
31 #Part-2. Scatterplot matrix by GGally, detailed
32 #step-6. read in data table
33 MDF4 <- read.csv("Morph-9-factors.csv", header=TRUE, sep = ",")
34 MDF4 <- na.omit(MDF4)
35 row.has.na <- apply(MDF4, 1, function(x){any(is.na(x))})
36 sum(row.has.na)
37 head(MDF4)
38 library(data.table)
39 library(GGally)
40 library(ggplot2)
41 # step-7. set up factor value
42 MDF4$slope_class <- factor(MDF4$class)
43 # step-8. plot scatterplot matrix by steepness angle
44 sl<- ggpairs(MDF4,
45 title = "Mariana Trench \nScatterplot Correlation Matrix by Slope Angle Class",
46 upper = list(continuous = wrap("density", alpha = 0.5, lwd = 0.3)),
47 lower = list(continuous = wrap("points", color = "red", alpha = 0.5),
48 combo = wrap("box", color = "orange", alpha = 0.6, lwd = 0.3)),
49 diag = list(continuous = wrap("densityDiag", color = "blue", alpha = 0.5, lwd = 0.3)))
50 sl
51 # step-9. add axis ticks
52 pair<- sl + theme(
53 axis.text.x = element_text(face = 3, color = "gray24", size = 6, angle = 15),
54 axis.text.y = element_text(face = 3, color = "gray24", size = 6, angle = 15))
55 pair
56 # step-10. Visualization of correlations
57 ggcorr(data=MDF, method = c("everything", "pearson"))
58
59 #Part-4. Correlation matrices by 3 approaches
60 # step-11. Read in data table
61 MDF <- read.csv("Morphology.csv", header=TRUE, sep = ",")
62 MDF <- na.omit(MDF)
63 row.has.na <- apply(MDF, 1, function(x){any(is.na(x))})
64 sum(row.has.na)
```
65 head(MDF)
66 # step-12. Check correlation between variables
67 cor(MDF)
68 # step-13. Variant-1: Pearson correlation coefficients, using pairwise observations (default method)
69 gp<- ggcorr(data=MDF, method = c("everything", "pearson"),
70 name = "\nPearson \ncorrelation \nmethod \n(parametric)",
71 label = TRUE, label_size = 2, label_round = 2, label alpha = TRUE,
72 hjust = 0.75, size = 3, color = "grey50", legend. position = "left")
73 gpt<- gp + labs(title="Mariana Trench",
74 subtitle = "Correlation of Geomorphological Impact Factors \nPearson correlation method (parametric)",
75 caption = "Statistics Processing and Graphs: \nR Programming. Data Source: QGIS") +
76 theme plot. title = element_text (family = "Times New Roman", face = 2, size = 12),
77 plot. subtitle = element_text (family = "Times New Roman", face = 1, size = 10),
78 plot. caption = element_text (family = "Times New Roman", face = 2, size = 8))
79 gpt
80 # step-14. Variant-2: Spearman correlation coefficients, using strictly complete observations
81 gs<- ggcorr(data=MDF, method = c("everything", "spearman"), geom = "circle", nbreaks = 5,
82 min_size = 3, max_size = 9, palette = "PiYG",
83 name = "\nSpearman \ncorrelation \nmethod \n(non-parametric)",
84 label = TRUE, label_size = 2, label_round = 2, label alpha = TRUE,
85 hjust = 0.75, size = 3, color = "grey50", legend. position = "left")
86 gs
87 gst<- gs + labs (title="Mariana Trench",
88 subtitle = "Correlation of Geomorphological Impact Factors \nSpearman correlation method \n(nonparametric measure of rank using monotonic function)",
89 caption = "Statistics Processing and Graphs: \nR Programming. Data Source: QGIS") +
90 theme (plot. title = element_text (family = "Times New Roman", face = 2, size = 12),
91 plot. subtitle = element_text (family = "Times New Roman", face = 1, size = 10),
92 plot. caption = element_text (family = "Times New Roman", face = 2, size = 8))
93 gst
94 # step-15. Variant-3: Kendall correlation coefficients, using complete observations
95 gk<- ggcorr(data=MDF, method = c("complete", "kendall"),
96 geom = "text", nbreaks = 5, palette = "RdYlBu", hjust = 1, label = TRUE, label alpha = 0.4)
97 gk
98 gkt<- gk + labs (title="Mariana Trench",
99 subtitle = "Correlation of Geomorphological Impact Factors \nKendall correlation coefficients, \nusing complete observations",
100 caption = "Statistics Processing and Graphs: \nR Programming. Data Source: QGIS")
101 gkt
102 figure <- plot_grid (gpt, gst, labels = c("1", "2"), ncol = 2, nrow = 1)
103 figure
```

codes/R-41.r
A.6.42 R code for diagonal scatterplot matrices by (car) library

Source data available on my GitHub.

1 library(car)
2 library(RColorBrewer)
3 # Part 1: create data.frame
4 # step-1. read in table. build data frame
5 MDF <- read.csv("Morphology.csv", header=TRUE, sep = ",")
6 MDF <- na.omit(MDF)
7 row.has.na <- apply(MDF, 1, function(x) (any(is.na(x))))
8 sum(row.has.na)
9 head(MDF)
10 # step-2. merge values of 4 tectonic plates into one category
11 MDF = melt(setDT(MDF), measure = patterns("^plate"), value.name = c("tectonics"))
12 head(MDF)
13 levels(MDF$variable) = c("Philippine Plate", "Pacific Plate", "Mariana Plate", "Caroline Plate") # rename values
14 head(MDF)
15 # step-3. read data frame into object 'data'
16 data= MDF
17 # step-4. set up colors Set2 by category 'tectonics'
18 my_colors <- brewer.pal(nlevels(as.factor(data$variable)), "Set2")
19
20 # Part-2. Plotting
21 # step-5. Variant-1: tectonics + slope angle + igneous volcanic zones
22 scatterplotMatrix(~ tectonics + slope_angle + igneous_volc | variable, data=data,
23 smoother="", col=my_colors , smoother.args=list(col="grey")),
24 regLine = list(method=lm, lty=1, lwd=1),
25 lwd=0.5, pch=c(15,16,17),
26 main="Mariana trench scatter plot (tectonics, geomorphology, magmatism) \nwith four tectonic plates options: Mariana, Caroline, Philippine, Pacific",
27 cex=1.0, cex.axis = 1.0, # cex.axis, font of legend
28 legend = TRUE, cex.labels =1.3, cex.main = 1.0, ellipse=F,
29 var.labels = c("tectonics", "geomorphology \n(slope angle)", "magmatism \n(igneous volcanic zones)")
30 )
31 # step-6. Variant-2: depths + sediments + igneous volcanic zones
32 scatterplotMatrix(~ tectonics + Min + sedim_thick | variable, data=data,
33 smoother="", col=my_colors , smoother.args=list(col="grey")),
34 regLine = list(method=lm, lty=1, lwd=1),
35 lwd=0.5, pch=c(15,16,17),
36 main="Mariana trench scatter plot (tectonics, bathymetry, geology) \nwith four tectonic plates options: Mariana, Caroline, Philippine, Pacific",
37 cex=1.0, cex.axis = 1.0, # cex.axis, font of legend
38 legend = TRUE, cex.labels =1.3, cex.main = 1.0, ellipse=F,
39 var.labels = c("tectonics", "bathymetry \n(minimal depth)", "geology \n(sedimental thickness)")
40 )
41 # step-7. plotting
42 plot(data, pch=1, cex=0.3, col=rgb(0.5, 0.8, 0.9, 0.7))

codes/R-42.r
# Appendix G

## Indices
# G.1 Index of Places

This index lists the geographic places and locations mentioned in this dissertation

## A

<table><tr><td>Academy of Sciences Rise</td><td>188</td></tr><tr><td>Acapulco Trench</td><td>22</td></tr><tr><td>Agrigan, Island</td><td>63</td></tr><tr><td>Aleutian Arc</td><td>15, 18</td></tr><tr><td>Aleutian Islands</td><td>18</td></tr><tr><td>Aleutian Trench</td><td>18, 20</td></tr><tr><td>Aleutian-Komandorsky chain</td><td>18</td></tr><tr><td>Alpine Fault</td><td>72–74, 76</td></tr><tr><td>Amami Sankaku Basin</td><td>66</td></tr><tr><td>Andean Cordillera</td><td>28</td></tr><tr><td>Andes</td><td>28</td></tr><tr><td>Andes, Central</td><td>28</td></tr><tr><td>Antarctic</td><td>20, 241</td></tr><tr><td>Antarctic Plate</td><td>26–28, 183, 240</td></tr><tr><td>Antarctica</td><td>31</td></tr><tr><td>Arica bend</td><td>28</td></tr><tr><td>Atacama Desert</td><td>29</td></tr><tr><td>Atacama Trench</td><td>26</td></tr><tr><td>Atlantic Ocean</td><td>57</td></tr><tr><td>Australia</td><td>30, 31, 35</td></tr><tr><td>Australian Plate</td><td>35, 72</td></tr><tr><td>Ayu Trough</td><td>53</td></tr></table>

## B

<table><tr><td>Bering Sea</td><td>188</td></tr><tr><td>Biobío Canyon</td><td>27</td></tr><tr><td>Bismarck Sea fault</td><td>35</td></tr><tr><td>Bismarck Sea</td><td>35</td></tr><tr><td>Bismarck Sea back-arc basin</td><td>35</td></tr><tr><td>Bonin Islands</td><td>64, 65</td></tr><tr><td>Boso triple junction</td><td>65</td></tr><tr><td>Bougainville Island</td><td>37</td></tr><tr><td>Bougainville Trench</td><td>35</td></tr><tr><td>Bounty Trough</td><td>77</td></tr><tr><td>Bussol Strait</td><td>17, 186–189</td></tr></table>

## C

<table><tr><td>Calle-Calle Canyon</td><td>27</td></tr><tr><td>Caribbean Plate</td><td>22, 24</td></tr></table>

<table><tr><td>Carnegie Ridge</td><td>28</td></tr><tr><td>Caroline Plate</td><td>53, 55, 57</td></tr><tr><td>Caroline Ridge</td><td>55</td></tr><tr><td>Central Depression</td><td>28</td></tr><tr><td>Central valley</td><td>28</td></tr><tr><td>Challenger Deep</td><td>57, 63</td></tr><tr><td>Chatham Rise</td><td>30, 77</td></tr><tr><td>Cherskii Mountains</td><td>14</td></tr><tr><td>Chile</td><td>15, 26, 28</td></tr><tr><td>Chile Ridge</td><td>28</td></tr><tr><td>Chile Tripe Junction</td><td>28</td></tr><tr><td>Chiloé segment</td><td>27</td></tr><tr><td>China</td><td>57</td></tr><tr><td>Choiseul Island</td><td>37</td></tr><tr><td>Chortis Block</td><td>24</td></tr><tr><td>Coastal Cordillera</td><td>28</td></tr><tr><td>Cocos Plate</td><td>21</td></tr><tr><td>Cocos Ridge</td><td>21, 23</td></tr><tr><td>Cook Strait</td><td>30</td></tr><tr><td>Copiapó Ridge</td><td>28</td></tr><tr><td>Cordillera, Andean</td><td>29</td></tr><tr><td>Cordillera, Coastal</td><td>29</td></tr><tr><td>Costa Rica</td><td>23, 195</td></tr><tr><td>Cotabato Trench</td><td>211</td></tr></table>

## E

<table><tr><td>East Luzon Trough</td><td>44</td></tr><tr><td>East Mariana Basin</td><td>63</td></tr><tr><td>East Pacific Rise</td><td>22, 28</td></tr><tr><td>Easter microplate</td><td>28</td></tr><tr><td>Easter Seamount Chain</td><td>28</td></tr><tr><td>Ecuador</td><td>26</td></tr><tr><td>Eurasian Plate</td><td>15, 45, 48</td></tr></table>

## F

<table><tr><td>Fiji</td><td>63</td></tr><tr><td>Fiji, North</td><td>34</td></tr><tr><td>Fiordland Massif</td><td>72</td></tr><tr><td>Franciscan subduction zone</td><td>22</td></tr><tr><td>Futuna Island</td><td>34</td></tr></table>

## G

<table><tr><td>Greater Kuril Chain</td><td>14, 17, 187–189</td></tr></table>

<table><tr><td>Guam</td><td>64</td></tr><tr><td>Guatemala Trench</td><td>21</td></tr></table>

## H

<table><tr><td>Hikurangi Margin</td><td>73, 75, 77</td></tr><tr><td>Hikurangi Plateau</td><td>77</td></tr><tr><td>Hikurangi Trench</td><td>72, 75–77, 183, 239, 241</td></tr><tr><td>Hikurangi Trough</td><td>30</td></tr><tr><td>Hjort Plateau</td><td>73</td></tr><tr><td>Hjort Ridge</td><td>73</td></tr><tr><td>Hjort Trench</td><td>72, 74–76, 239, 241</td></tr><tr><td>Hokkaido Island</td><td>13, 17, 189</td></tr><tr><td>Honduras Platform</td><td>22</td></tr><tr><td>Honshu Island</td><td>68</td></tr><tr><td>Honshu island arc</td><td>65</td></tr><tr><td>Huon Gulf</td><td>35</td></tr></table>

## I

<table><tr><td>Imperial Canyon</td><td>27</td></tr><tr><td>Indian Ocean</td><td>57</td></tr><tr><td>Indo-Australian Plate</td><td>31, 35, 72, 74, 75, 183, 240</td></tr><tr><td>Iquique Ridge</td><td>28</td></tr><tr><td>Iturup Island</td><td>14, 17, 187, 189</td></tr><tr><td>Izu Islands</td><td>64</td></tr><tr><td>Izu-Bonin Collision Zone</td><td>65</td></tr><tr><td>Izu-Bonin Trench</td><td>64–66</td></tr><tr><td>Izu-Bonin-Mariana Arc</td><td>63, 66</td></tr><tr><td>Izu-Ogasawara Trench</td><td>64</td></tr></table>

## J

<table><tr><td>Jalisco</td><td>22</td></tr><tr><td>Japan</td><td>48, 67</td></tr><tr><td>Japan Plate</td><td>67</td></tr><tr><td>Japan Trench</td><td>25, 64, 67</td></tr><tr><td>Japan trench-arc system</td><td>67</td></tr><tr><td>Juan Fernández microplate</td><td>28</td></tr><tr><td>Juan Fernández Ridge</td><td>28</td></tr></table>
K

Kamchatka Peninsula 15, 16,
18, 188, 189

Kamchatka-Aleutian triple
junction 18

Kerama Fault 49

Kermadec Ridge 31

Kermadec Trench 72, 77

Kronotskiy arc 18

Krusenstern strait 14

Kunashir Island 14, 187

Kuril Island Chain 17

Kuril-Kamchatka Arc 15

Kuril-Kamchatka subduction
zone 15

Kuril-Kamchatka Trench 13,
186

Kuroshio 14

Kyushu Island 48

Kyushu-Palau Ridge 53

L

Louisville Seamount Chain
31

Lua 34

Luzon Arc 47

Luzon Island 45, 213, 214

M

Macquarie Arc 237

Macquarie Fault Zone 72,
241

Macquarie Island 72

Macquarie Ridge Complex
72

Macquarie Triple Junction
72, 241

Magellan Seamounts 230

Malaita Island 37

Manila Trench 45, 47

Marcus-Wake Seamounts
230

Mariana Islands 34, 57

Mariana Plate 57

Mariana Trench 57, 63

Maule 27

Meiji Guyot seamount 15

Mexico 22

Middle America 21

Middle America Transverse
Zone 22

Middle America Trench 21

Mindoro Island 45

Miyako Fault 184, 217

Miyako-jima (Island) 52

Mocha 27

Mocha Fracture Zone 27, 28

Motagua Fault 24

Motagua-Polochic system 24

N

Nankai subduction zone 70

Nazca Plate 22, 26-28

Nazca Ridge 28

Negros Trench 211

New Britain arc 35

New Britain Island 35

New Britain Trench 35, 37

New Hebrides Trench 32

New Zealand 31, 63, 72-74,
76, 77

Nicaragua 24, 195

Nicoya Peninsula 24, 195

North America 22

North American Plate 14, 22,
67

North Bismarck Plate 35

North Island 31, 75, 77, 183,
240

Northern Chile 29

O

O'Higgins Guyot 28

Ogasawara Plateau 64, 65

Okhotsk Plate 188

Okinawa Trough 48

Okinawa-jima (Island) 52

Ontong Java Plateau 35, 77

Oyashio 14

P

Pacific Ocean 17, 30, 31, 34,
35, 57, 77, 188

Pacific Ocean, south 26

Pacific Ocean, southwest 241

Pacific Plate 15, 17, 22, 35,
37, 57, 69, 72, 74,
75, 183, 188, 240

Pacific Volcanic Chain 24

Palau Islands 53

Palau Trench 53

Panama, Isthmus of 22

Papua New Guinea 35, 53,
55

Paramushir Island 188

Parece Vela Basin 56

Peru 28

Peru-Chile Trench 20, 26, 29
Chilean 29
Chiloé 29
Maule 29
Peruvian 29

Philippine Archipelago 43,
57

Philippine Fault 44

Philippine Sea 53, 65, 210

Philippine Sea Plate 45, 53,
55, 57, 65, 210

Philippine Trench 39

Philippines 213, 214

Polochic Fault 24

Puysegur Bank 72

Puysegur Margin 73

Puysegur Trench 72, 74-76,
239, 241

R

Ramu-Markham Fault 35

Ring of Fire 15, 67

Ryukyu island arc 48, 184,
216

Ryukyu Trench 48, 216

S

Sakhalin Island 187

Samoan Passage 60

San Antonio Canyon 27

San Cristobal Island 37

San Cristobal Trench 37

Sanriku-Haruka-Oki 69

Santa Isabel Island 37

Sea of Japan 67

Sea of Okhotsk 14, 15, 17,
187, 188

Shiashkotan Island 188

Shikoku Basin 64, 65

Shikotan Island 187, 189
<table><tr><td>Solomon island arc</td><td>35, 37</td></tr><tr><td>Solomon Sea</td><td>35, 37</td></tr><tr><td>Solomon Sea Plate</td><td>35, 37</td></tr><tr><td>Sorol Trough</td><td>55</td></tr><tr><td>South America</td><td>28</td></tr><tr><td>South American Plate</td><td>26</td></tr><tr><td>South Bismarck Plate</td><td>35</td></tr><tr><td>South China Sea</td><td>45</td></tr><tr><td>South Island</td><td>72-74, 241</td></tr><tr><td>South Kuril</td><td>16</td></tr><tr><td>Southern Ocean</td><td>20</td></tr><tr><td>Sulu Trench</td><td>211</td></tr><tr><td>Sunda Plate</td><td>45, 210</td></tr></table>

T

<table><tr><td>Taitao Triple Junction</td><td>28, 29</td></tr><tr><td>Taiwan Island</td><td>45, 48, 218</td></tr><tr><td>Taltal Ridge</td><td>28</td></tr><tr><td>Tanega-shima</td><td>49</td></tr></table>

<table><tr><td>Tasman Sea</td><td>74</td></tr><tr><td>Tehuantepec Ridge</td><td>21</td></tr><tr><td>Tohoku</td><td>68, 70</td></tr><tr><td>Tohoku-Oki</td><td>68</td></tr><tr><td>Tokachi-Oki</td><td>69</td></tr><tr><td>Tokara Channel</td><td>49</td></tr><tr><td>Tokyo</td><td>64</td></tr><tr><td>Tolten Canyon</td><td>27</td></tr><tr><td>Tonga Platform</td><td>31</td></tr><tr><td>Tonga Ridge</td><td>180, 198</td></tr><tr><td>Tonga Trench</td><td>31, 182, 199</td></tr><tr><td>Tonga-Kermadec Arc</td><td>30</td></tr><tr><td>Tonga-Kermadec Ridge</td><td>31</td></tr><tr><td>Tonga-Kermadec-Hikurangi subduction system</td><td>77</td></tr></table>

U

<table><tr><td>Urup Island</td><td>14, 187, 189</td></tr></table>

V

<table><tr><td>Valdivia</td><td>27</td></tr><tr><td>Valdivia Fracture Zone</td><td>27</td></tr><tr><td>Vanuatu Trench</td><td>32</td></tr><tr><td>Vityaz Trench</td><td>33</td></tr></table>

W

<table><tr><td>West Bismarck arc</td><td>35</td></tr><tr><td>Woodlark Basin</td><td>35, 37</td></tr><tr><td>World Ocean</td><td>57</td></tr></table>

Y

<table><tr><td>Yaku-shima</td><td>49</td></tr><tr><td>Yap Islands</td><td>53</td></tr><tr><td>Yap Trench</td><td>55</td></tr><tr><td>Yap-Palau subduction zone</td><td>55</td></tr></table>

<figure><img src="image_214.png" /></figure>
## G.2 Index of Concepts

This index lists the most essential concepts and definitions used in this dissertation, including both geological and technical terminology (programming, statistics, mathematics)

### A

abyssal

plain, 58

zone, 61

accretionary wedge, 77

aeolian processes, 18

algorithm

ANOVA, 167

Levene's test, 145

Parewise t-test, 145

Shapiro-Wilk test of normality, 146

Tukeys honest significance test, 145

approach, 12

autocorrelation, 172

EFA, 142

Euclidean distance metric, 132

Gauss-Newton, 174

KDE, 119, 122, 139, 154, 155, 160, 162, 164, 230

least squares, 174

nonlinear, 174

ordinary, 174

weighted, 174

LOESS

method, 125

Savitzky-Golay smoothing filter, 125

Principal Component Analysis, 138, 165

QQ, 172, 173

regression

isotonic, 175

quantile, 153, 172

regression analysis, 125, 158

non-parametric, 125

SARIMA, 176

ternary diagrams, 129

Artificial Intelligence, 85

Machine Learning, x, 7, 9, 12, 78, 84, 85, 103, 119, 120, 139, 140, 152, 243, 248–250, 252, 253

axial rift, 6

### B

back arcs, 71

bathymetry, 61

biological communities, 61

buoyancy, 34, 206

### C

canyons

underwater, 61

steps, 61

cartography, 243

data

CMT, 80, 88–90, 92

CryoSat-2, 78

DEM, 80

EGM96, 80, 90, 98, 99, 102

ENVISAT, 81

ETOPO1, 3, 78, 80, 85, 86, 90, 94, 95, 100, 101, 110, 111, 191–193, 224, 249

ETOPO5, 80

GEBCO, x, 3, 12, 78–81, 85, 86, 90, 92, 94, 95, 224, 234, 240, 247

Google Earth, 80

GSHHGD, 78, 80, 90

GVP database, 80

ISC database, 80

Jason-1, 78

KMZ, 90

raster, 84

SPOT3, 81

SPOT4, 81

SRTM, x, 12, 37, 78, 80, 213, 247, 249

vector, 84

grid, 83

map element, 83

annotation, 83

coastline, 83

color scale, 83, 86

directional rose, 83, 86, 101, 108

embellishment, 86

grid, 83

grid ticks, 86, 108

labels, 83

legend, 83, 86, 101, 108, 111

logo, 86

north direction, 83

scale bar, 83, 86, 108

vector lines, 83, 86

map projection, 83, 86, 101, 111

azimuthal, 86

cylindrical, 86

Cylindrical

Equal-Area

Gall-Peters, 86

Eckert IV equal-area pseudo-cylindrical, 86

Gall-Peters, 102

Lambert Azimuthal Equal-Area, 86

Mercator, 86, 95

Mollweide

pseudo-cylindrical

homolographic

equal-area, 86

Oblique Mercator, 86

Orthographic

azimuthal, 86
Polyconic, 86

Transverse Mercator,
86

cliffs, 62

cluster analysis, 138

cluster, 133

clustering
k-means, 133, 134

dendrogram, 164

segmentation, 164

continental

collision, 34, 74

crust, 74

margins, 60

shelf, 61

slab age-buoyancy, 61

slope, 20

convergent, 73

correlation

coefficient, 135

correlogram, 160

correlogram ellipse, 134

covariance matrices, 160

Kendall, 137, 160

Pearson, 136, 160

Spearman, 136, 160

D

décollement, 70

data

analysis, 243

ASCII, 78, 83, 88, 95,
108, 109, 191, 250,
251

distribution, 125, 227

frame, 82

grouping, 138

modeling, 8

multidimensional, 126

ranking, 131

set, 243

skewness, 125

sorting, 138

variables, 123

visualizing, 243

deep-sea

basins, 34, 58

depressions, 58

ridges, 61

deformation

coseismic, 77

rheology, 34

dehydration, 206

dislocations

lateral, 63

vertical, 63

E

Earth

crust, 49, 58

lower, 69

oceanic, 60, 77

evolution, 34

lithosphere, 16, 34, 42,
51, 55, 58, 60, 63,
66

magma

magma chamber, 16

magmatism, 23, 116

mantle, 60

deep, 77

island-arc, 69

mantle plume, 17

pillow lava, 60

Mohorovičić (Moho)

boundary, 58

earthquake, ix, x, 4–8, 12,

14–16, 18, 24, 26,

28, 29, 34, 63, 88,

184, 230, 234, 245,

246, 248, 253

centroid moment tensor,
88

deep, 34

focal

mechanism, 17, 33,
41, 42, 74, 88

zone, 16

hypocenter, 45, 88, 90

magnitude, 186

submarine, 246, 249

erosion, 18

F

fault, 6, 59

graben, 14, 25, 54, 61,
190

horst, 25, 54, 61

stretching, sub-parallel,
77

transform

dextral, 76

near-vertical, 73

fluid thermal fluxes, 16

forearc, 77

fracture, 15, 63

G

gas hydrates, 77

GDAL, 83

geochemical recycling, 34

geochemistry, 74

geodesy, 11, 92, 117, 153,
171, 189, 244, 249

GPS, 77, 93

geoid, 37, 74, 78, 84–86, 98,
101, 191, 195, 203,
205, 218, 236

undulation, 219

Geologic time period

Cenozoic, 211

Cretaceous, 49, 211

Early, 60

Late, 22, 60

Eocene, 44, 74

Late, 18

Jurassic, 49, 58, 211

Mesozoic, 18, 23, 24,
44, 49, 60, 63, 211

Miocene, 25, 39, 74

Middle, 25

Neogene, 67

Oligocene, 27, 39, 74

Paleocene, 25

Paleogene, 22, 24

Pliocene, 22

Early, 25

Precambrian, 210

Quaternary, 25, 44, 60,
67, 74, 211

Tertiary, 18

geomorphology, 61, 66, 67,
69, 73, 74, 76–78,
80, 81, 83–86, 91,
100, 101, 103, 104,
111, 113–118, 120,
124–126, 130–135,
137–144, 147, 149,
151, 153, 155, 163,
165, 167, 168, 173,
176, 179–181, 185,
186, 188–191, 193

GIS, 1, 3, 7–12, 78, 81–83,
91, 97, 114, 117,
147, 149, 150, 168,
177, 242, 244,
248–250, 252, 253

ArcGIS, 114

GRASS GIS, 253

MapInfo, 114

QGIS, 97, 150

GMT, 83

module

blockmean, 95

blockmedian, 95

blockmode, 95

gmt.conf, 84

gmtdefaults, 84

gmtinfo, 84

gmtlogo, 84, 104

gmtset, 84, 98, 105

gmtwhich, 84

grd2cpt, 98, 101

grdcontour, 83, 84, 98,
101

grdcut, 83, 84, 101

grdgradient, 108

grdhisteq, 84, 95

grdimage, 83, 84, 98,
186

grdtrack, 83, 105, 186

grdview, 101

img2grd, 101

logo, 98, 101, 105

makecpt, 84, 95

nearneighbor, 84

psbasemap, 83, 84, 98

pscoast, 84, 98, 101

psconvert, 84, 98, 101,
104, 105

pshistogram, 84, 105

psimage, 98

pslegend, 105

psmeca, 88, 89

psrose, 105

psscale, 84, 98, 101

pstext, 84, 98, 101

psvelo, 92

psvelomeca, 89

psxy, 83, 104, 186

trend1d, 83

Google Earth, 90

gravitation, 62

flow system, 58

instability, 62

gravity

anomaly

marine free-air, 10,
85, 98, 101, 203,
209, 214, 217, 219,
226, 236

pull, 62

H

hills, 6

hydrosphere, 66

I

interseismic coupling, 77

L

landslide, 61

submarine, 62

lateral

displacement, 62

geologic shifts, 61

ledges, 61

M

magnetic anomaly lineations,
63

mainland bank, 62

marginal seas, 62

melting, 206

mid-ocean ridge, 58, 60

basalts, 63

mineral physics, 74

N

normalized steepness, 137

O

ocean bedrock, 61

ores, multi-metal, 16

P

passive margins, 61

petrology, 74

plate

boundary, 74

deformation, 62

transpression, 73, 74

lithospheric, 58

motion, right-lateral, 72

ophiolites, 58

upper, 77

plateau, 61

marginal, 61

oceanic, 77

plot

2D, 3, 101, 105, 151,
209, 213, 248

3D, 3, 50, 83, 99–101,
119, 166, 184, 208,
209, 213, 231, 234,
243, 248, 249

scatterplot matrix,
166

area chart, 158

boxplot, 119, 120, 122,
142, 154, 155

categorywise, 127

clusterman, 156

Euler-Venn diagram,
147, 157, 158

facet grid, 158

facetted, 125

flowchart network, 168

hexagonal, 135
mapping, 160

letter-value, 119, 154,
155

pie chart, 171

projection
affine, 166
scalar, 165

radar charts, 129, 170

ridgeline, 127

stacked
area, 168
bar, 169

strip plot, 157

subplot, 172
violinplot, 155
programming language
AWK, 115, 250
C++, 247
MATLAB, ix, 114, 252
Octave, ix, 115, 247, 250
Python, ix, 153, 247, 250
Jupyter Notebook, 153
Math, 170
Matplotlib, 8, 148, 149, 152–155, 162, 170
Matpotlib, 150
NumPy, 8, 148–150, 152, 153, 168, 170
OS, 162, 170
Pandas, 149, 150, 162, 170
Scikit Learn, 165
SciPy, 8, 148–150, 152, 153, 164, 168
Seaborn, 150, 153, 155, 162, 170
StatsModels, 8, 149, 153, 172, 176
R, ix, 247, 248, 250
bkde, 123
car, 11
correlplot, 137
data.table, 136
dendextend, 138
extrafont, 229
extrafontdb, 229
fmsb, 129
ggalt, 129
ggimage, 137
ggplot2, 120, 121, 125, 127, 128, 131, 138
ggrides, 127
ggsignif, 155, 229
graphics, 155
gridExtra, 138
gridGraphics, 138
hclust, 138

kde.R, 123
KernSmooth, 123
LatticeExtra, 130
LatticeExtra2, 126
magrittr, 138
Matrix, 137
mclust, 137
methods, 138
tidyverse, 127, 155, 229
vcd, 120, 132
violinplot, 122
violinplot, 121
waffle, 131

**R**
ridge, 6, 61
longitudinal, 61
rift, 60
rock
igneous
basalt, 23, 25, 34, 53, 58, 60
basalt, tholeiitic, 23
basalts, phyric, 23
basalts, plagioclase
phyric
high-alumina, 23
clinopyroxene, 23
diabase, 60
gabbro, 58, 60
microlites, 23
olivine, 23, 63
olivine-plagioclase, 23
quartz, 60
quartz-trachyte, 60
tholeiites magma
series, 23
tholeiites, island-arc, 23
tholeiites, oceanic, 23
trachyte, 60
ultrabasic, 210
ultrabasic complex, 60
ultrabasites, 60
mechanics, 74
metamorphic, 211

sedimentary
clastic, 60
turbidite, 20

**S**

seafloor, ix, x, 3–8, 12–14, 25, 34, 35, 40, 43, 44, 46, 47, 54, 56, 58, 60–62, 84, 85
bathymetry, 61
spreading, 6, 60
spreading fabric, 63
seamounts, 5, 14, 27, 28, 47, 49, 58, 69, 76, 77, 151, 183, 184, 209, 230, 231, 238, 240, 241
sedimentation, 58
abyssal, 62
accumulation, 60
active, 60
areal, 60
avalanche, 19
terrigenous, 18, 60
sediments, 20
aleurite-clay, 190
ash, sand-size, 20
clay, 20
pelagic, 190
precipitation, 59
ringwoodite, 63
terrigenous material, 18
thickness, 60, 172
trap, 60
wadsleyite, 63
seismicity, 14, 17, 18, 28, 32–34, 39, 41, 44, 45, 47, 50, 63, 67, 68, 70, 74, 116, 186
adiabatic shear instabilities, 39
seismology, 74
shelf, 62
continental, 77
slab, 63
cold, 70
dehydration, 71
mineralogy, 70
morphology, 39
subduction, 39, 70

advance, 39

buoyancy, 39

dynamics, 39

rate, 39

retreat, 39

stagnation, 41

stress field, 39

warm, 70

slope, 57, 61

inclination, 63

instability

tension cracks, 33, 62, 63, 68

stability, 63

statistics

argument, 161

descriptive, 125

median, 172

noise, 158

quantiles, 172

significance, 167

SPSS, 105

variable, 161

dependent, 125

independent, 125

variance, 167

structural geology, 74

subduction

deep, 74

dip, 15

dipping, 77

dynamics, 74

frontal erosion, 77

margins, 75

oblique, 76

process, 74

scissors, 73, 76

sediment, 60

slab, 59

speed, 77

zone, 71, 73, 74

submarine

canyons, 20, 58

margins of continents, 62

valleys, 58

volcanoes, 66

suspension flows, 62

T

tectonic

block rotations, 77

plate, 60, 61

boundary, 6

collision, 77

movements, 63

subduction, 34, 74

velocity, 61

tension fractures, 62

terrace, 61

marine, 15

submarine, 20

submerged, 77

thermochronology, 77

thresholds, 59

transform

fault, right-lateral, 72

transformation

kinetics, 34

phase, 34

trench, 76

curvature, 76

dynamics, 60

kinematics, 77

profile

arcuate barrel-shaped, 183

asymmetric, 57, 182, 184

cascade-shaped, 180

crescent-shaped

circular-like, 184

elongated, 180, 181, 183

irregular, 179

irregular right-sided, 184

irregular V-shaped, 179, 183

irregular V-shaped left-sided, 180

irregular V-shaped right-sided, 183

sinuous-shaped, 180

symmetric, 182

transverse, 57

trough-like W-shaped, 184

U-shaped, 182

V-shaped, 181, 183

triple junction, 72

trough, 59, 76

tsunami, 77

U

UNIX

bash, 104

shell script, 83

utility

cat, 104

cut, 83

echo, 83, 95, 101, 104

rm, 83, 101, 104

V

velocity, horizontal fields, 77

volcanic

activities, 15

island arc, 15, 58, 62, 74

volcanism, x, 4, 11, 16, 25,

27, 34, 40, 42, 44, 50, 60, 66, 67, 116, 142, 147, 168, 176, 184, 186, 189, 205, 206, 214, 229, 232, 234

W

Wadati-Benioff zone, 63, 77

wave

body

magnitude, 89

ocean, 60, 61

oceanic (T-waves), 63

period, 89

primary (P-waves), 90

seismic, 39, 58, 74, 89

surface (L-waves)

magnitude, 89

tsunami, 246

velocity, 12, 71
G.3 Index of Authors

This index lists authors of the cited literature sources used in this dissertation

A

<table><tr><td>Abbott (L. D.)</td><td>35</td></tr><tr><td>Abdi (H.)</td><td>165</td></tr><tr><td>Adamic (J.)</td><td>18</td></tr><tr><td>Adler (P. M.)</td><td>33, 81</td></tr><tr><td>Adrianov (A. V.)</td><td>14</td></tr><tr><td>Agapova (G. V.)</td><td>6</td></tr><tr><td>Agarwal (B. N. P.)</td><td>116</td></tr><tr><td>Aho (A. V.)</td><td>110</td></tr><tr><td>Airey (D.)</td><td>5</td></tr><tr><td>Aiuppa (A.)</td><td>32</td></tr><tr><td>Aivazis (M.)</td><td>171</td></tr><tr><td>Akiyoshi (H.)</td><td>65</td></tr><tr><td>Aksentov (K. I.)</td><td>29</td></tr><tr><td>Alalykina (I.)</td><td>14</td></tr><tr><td>Albarau (J. F.)</td><td>233</td></tr><tr><td>Alden (A.)</td><td>39</td></tr><tr><td>Alexeiev (D. V.)</td><td>18</td></tr><tr><td>Alexeiev (D.)</td><td>18</td></tr><tr><td>Algeo (T. J.)</td><td>6</td></tr><tr><td>Ali (J. R.)</td><td>4, 7, 42, 44</td></tr><tr><td>Ali (M.)</td><td>154</td></tr><tr><td>Ali (S. M.)</td><td>233</td></tr><tr><td>Allard (P.)</td><td>32</td></tr><tr><td>Allen (G.)</td><td>79, 80</td></tr><tr><td>Alsdorf (D.)</td><td>80</td></tr><tr><td>Alvarez-Gómez (J. A.)</td><td>7, 25</td></tr><tr><td>Amante (C.)</td><td>78, 80</td></tr><tr><td>Ammon (C. J.)</td><td>68</td></tr><tr><td>Anderson (C. A.)</td><td>58</td></tr><tr><td>Anderson (C. D.)</td><td>4, 7, 42, 44</td></tr><tr><td>Anderson (M. E.)</td><td>30</td></tr><tr><td>Anderson (M. O.)</td><td>44, 57</td></tr><tr><td>Anderson (R. N.)</td><td>93</td></tr><tr><td>Ando (K.)</td><td>29</td></tr><tr><td>Ando (M.)</td><td>44, 48</td></tr><tr><td>Andrade (J. F.)</td><td>81</td></tr><tr><td>Angel (M. V.)</td><td>26</td></tr><tr><td>Angulo Ibanez (J. M.)</td><td>105, 154</td></tr><tr><td>Aniel-Quiroga (Í.)</td><td>25</td></tr><tr><td>Ansley (C. F.)</td><td>176</td></tr><tr><td>Appelgate (B.)</td><td>58</td></tr><tr><td>Arai (K.)</td><td>52, 67</td></tr></table>

<table><tr><td>Arai (S.)</td><td>66</td></tr><tr><td>Arakawa (Y.)</td><td>66</td></tr><tr><td>Araki (E.)</td><td>35, 37</td></tr><tr><td>Arcay (D.)</td><td>218</td></tr><tr><td>Arcos (M. E.)</td><td>15</td></tr><tr><td>Arculus (R. J.)</td><td>20, 66</td></tr><tr><td>Argus (D. F.)</td><td>15, 63, 92, 93</td></tr><tr><td>Arisa (D.)</td><td>66</td></tr><tr><td>Armstrong (A. A.)</td><td>57–59</td></tr><tr><td>Arndt (J. E.)</td><td>79</td></tr><tr><td>Arpa (C. B.)</td><td>4</td></tr><tr><td>Arriagada (C.)</td><td>8, 28</td></tr><tr><td>Arróspide (F.)</td><td>81</td></tr><tr><td>Arzu (I.)</td><td>24</td></tr><tr><td>Asada (M.)</td><td>34</td></tr><tr><td>Asano (S.)</td><td>67</td></tr><tr><td>Asanuma (T.)</td><td>67</td></tr><tr><td>Asao (K.)</td><td>44, 66</td></tr><tr><td>Ashi (J.)</td><td>67, 70</td></tr><tr><td>Ashraf (M.)</td><td>233</td></tr><tr><td>Ashworth (P. J.)</td><td>246</td></tr><tr><td>Atwater (B. F.)</td><td>15</td></tr><tr><td>Aubouin (J.)</td><td>22–24</td></tr><tr><td>Augustin (J.-M.)</td><td>233</td></tr><tr><td>Auzende (J. M.)</td><td>32</td></tr><tr><td>Avendonk (H. J. A.) van</td><td>47</td></tr><tr><td>Awcock (G.)</td><td>246</td></tr><tr><td>Azéma (J.)</td><td>22–25</td></tr></table>

B

<table><tr><td>Babonneau (N.)</td><td>29</td></tr><tr><td>Bacolcol (T.)</td><td>44, 83</td></tr><tr><td>Bagas (L.)</td><td>116</td></tr><tr><td>Bai (Y.)</td><td>18, 55, 198</td></tr><tr><td>Bailey (R. A.)</td><td>167</td></tr><tr><td>Baker (E. K.)</td><td>14, 43, 218</td></tr><tr><td>Baker (E. T.)</td><td>44, 57</td></tr><tr><td>Baker (S. J.)</td><td>4, 42, 44</td></tr><tr><td>Baldwin (S. L.)</td><td>35</td></tr><tr><td>Ballance (P. F.)</td><td>77</td></tr><tr><td>Banasik (P.)</td><td>249</td></tr><tr><td>Bani (P.)</td><td>32</td></tr><tr><td>Bannister (S.)</td><td>76, 77</td></tr></table>

<table><tr><td>Baranov (B. V.)</td><td>14, 15, 18, 190</td></tr><tr><td>Barazangi (M.)</td><td>63</td></tr><tr><td>Barker (D. H. N.)</td><td>77</td></tr><tr><td>Barnes (J. D.)</td><td>64</td></tr><tr><td>Barnes (P. M.)</td><td>72, 76, 77</td></tr><tr><td>Barnett (H. F. J.)</td><td>18</td></tr><tr><td>Barr (I. D.)</td><td>14</td></tr><tr><td>Barrier (E.)</td><td>24</td></tr><tr><td>Bartlett (D. H.)</td><td>57, 61, 233</td></tr><tr><td>Bas (T. P. L.)</td><td>81</td></tr><tr><td>Bateman (D.)</td><td>114</td></tr><tr><td>Bates (D. M.)</td><td>162</td></tr><tr><td>Bathe (F.)</td><td>81</td></tr><tr><td>Baumann (K.)</td><td>81</td></tr><tr><td>Baumberger (T.)</td><td>44, 57</td></tr><tr><td>Bautista (C. B.)</td><td>44, 45</td></tr><tr><td>Bautista (M. L. P.)</td><td>44, 45</td></tr><tr><td>Bazanova (L. I.)</td><td>15</td></tr><tr><td>Beaudoin (J.)</td><td>57–59</td></tr><tr><td>Beaumais (A.)</td><td>32</td></tr><tr><td>Beavan (J.)</td><td>72, 77</td></tr><tr><td>Beazley (D. M.)</td><td>168</td></tr><tr><td>Beccaluva (L.)</td><td>56, 58</td></tr><tr><td>Bechter (T.)</td><td>81</td></tr><tr><td>Beck (S. L.)</td><td>16</td></tr><tr><td>Becker (J. J.)</td><td>6</td></tr><tr><td>Becker (N.)</td><td>58</td></tr><tr><td>Becker (T. W.)</td><td>6, 7, 44, 49, 57, 58, 77</td></tr></table>

<table><tr><td>Beek (P.) van der</td><td>37</td></tr><tr><td>Behn (M. D.)</td><td>34, 58, 59</td></tr><tr><td>Behrmann (J. H.)</td><td>29</td></tr><tr><td>Behrmann (J. S.)</td><td>28, 29</td></tr><tr><td>Beier (C.)</td><td>33, 205</td></tr><tr><td>Belkin (I. M.)</td><td>14</td></tr><tr><td>Bell (R. E.)</td><td>77</td></tr><tr><td>Bello-González (J. P.)</td><td>8, 28</td></tr><tr><td>Belyaev (G. M.)</td><td>31</td></tr><tr><td>Berger (J.-F.)</td><td>81</td></tr><tr><td>Berghe (E.) van den</td><td>8, 233</td></tr><tr><td>Bermas (P. P. J.)</td><td>44</td></tr><tr><td>Bernardel (G.)</td><td>73</td></tr><tr><td>Berndt (C.)</td><td>61, 233</td></tr></table>
<table>
  <tbody>
    <tr>
      <td>Berryman (K. R.)</td>
      <td>76, 77</td>
    </tr>
    <tr>
      <td>Berthet (T.)</td>
      <td>42, 50, 51</td>
    </tr>
    <tr>
      <td>Bertoldi (W.)</td>
      <td>81</td>
    </tr>
    <tr>
      <td>Bertrand (H.)</td>
      <td>32</td>
    </tr>
    <tr>
      <td>Besana (G. M.)</td>
      <td>44</td>
    </tr>
    <tr>
      <td>Best (B. D.)</td>
      <td>119</td>
    </tr>
    <tr>
      <td>Best (M. J.)</td>
      <td>175</td>
    </tr>
    <tr>
      <td>Bevis (M.)</td>
      <td>30, 33</td>
    </tr>
    <tr>
      <td>Bialas (J.)</td>
      <td>76, 77</td>
    </tr>
    <tr>
      <td>Bilina (R.)</td>
      <td>105</td>
    </tr>
    <tr>
      <td>Billington (S.)</td>
      <td>18</td>
    </tr>
    <tr>
      <td>Bina (C. R.)</td>
      <td>39, 41, 43, 63, 65, 74, 218</td>
    </tr>
    <tr>
      <td>Bindeman (I. N.)</td>
      <td>15</td>
    </tr>
    <tr>
      <td>Binder (B.)</td>
      <td>6</td>
    </tr>
    <tr>
      <td>Bird (P.)</td>
      <td>7, 13, 190</td>
    </tr>
    <tr>
      <td>Birk (S.)</td>
      <td>81</td>
    </tr>
    <tr>
      <td>Björck (A.)</td>
      <td>175</td>
    </tr>
    <tr>
      <td>Blankenship (L. E.)</td>
      <td>31</td>
    </tr>
    <tr>
      <td>Blondel (M.)</td>
      <td>152</td>
    </tr>
    <tr>
      <td>Bloomer (S. H.)</td>
      <td>31</td>
    </tr>
    <tr>
      <td>Blusztajn (J.)</td>
      <td>50</td>
    </tr>
    <tr>
      <td>Blöschl (G.)</td>
      <td>9</td>
    </tr>
    <tr>
      <td>Bobbitt (A. M.)</td>
      <td>44, 57</td>
    </tr>
    <tr>
      <td>Bodine (J. H.)</td>
      <td>7, 58</td>
    </tr>
    <tr>
      <td>Bogdanic (T.)</td>
      <td>28</td>
    </tr>
    <tr>
      <td>Bogdanov (I.)</td>
      <td>33, 81</td>
    </tr>
    <tr>
      <td>Bogdanov (J. A.)</td>
      <td>61</td>
    </tr>
    <tr>
      <td>Bogdanov (J. L.)</td>
      <td>57</td>
    </tr>
    <tr>
      <td>Bogolepov (K. V.)</td>
      <td>34</td>
    </tr>
    <tr>
      <td>Bohlmann (H.)</td>
      <td>253</td>
    </tr>
    <tr>
      <td>Bohrmann (G.)</td>
      <td>6</td>
    </tr>
    <tr>
      <td>Bolik (F.)</td>
      <td>81</td>
    </tr>
    <tr>
      <td>Bonali (F. L.)</td>
      <td>246</td>
    </tr>
    <tr>
      <td>Boriskina (N. G.)</td>
      <td>14, 16</td>
    </tr>
    <tr>
      <td>Borradaile (G. J.)</td>
      <td>120, 154</td>
    </tr>
    <tr>
      <td>Bose (S.)</td>
      <td>70</td>
    </tr>
    <tr>
      <td>Boston (B.)</td>
      <td>8, 67</td>
    </tr>
    <tr>
      <td>Bougault (H.)</td>
      <td>23</td>
    </tr>
    <tr>
      <td>Bourgeois (J.)</td>
      <td>15, 17</td>
    </tr>
    <tr>
      <td>Bourrouilh-Le Jan (F. G.)</td>
      <td>33</td>
    </tr>
    <tr>
      <td>Boutelier (D.)</td>
      <td>61, 218</td>
    </tr>
    <tr>
      <td>Bovolo (C. I.)</td>
      <td>116</td>
    </tr>
    <tr>
      <td>Bowden (D. A.)</td>
      <td>76, 77, 246</td>
    </tr>
    <tr>
      <td>Bowin (C.)</td>
      <td>7</td>
    </tr>
    <tr>
      <td>Box (G. E. P.)</td>
      <td>154, 171, 172</td>
    </tr>
    <tr>
      <td>Brandl (P. A.)</td>
      <td>20, 33, 66</td>
    </tr>
    <tr>
      <td>Brandt (A.)</td>
      <td>14</td>
    </tr>
  </tbody>
</table>

<table>
  <tbody>
    <tr>
      <td>Braud (J.)</td>
      <td>6</td>
    </tr>
    <tr>
      <td>Braz (C.)</td>
      <td>28</td>
    </tr>
    <tr>
      <td>Bretz (F.)</td>
      <td>126</td>
    </tr>
    <tr>
      <td>Brown (D.)</td>
      <td>47</td>
    </tr>
    <tr>
      <td>Brown (S. T.)</td>
      <td>246</td>
    </tr>
    <tr>
      <td>Brucher (M.)</td>
      <td>152</td>
    </tr>
    <tr>
      <td>Brune (S.)</td>
      <td>34</td>
    </tr>
    <tr>
      <td>Bruns (T. R.)</td>
      <td>36</td>
    </tr>
    <tr>
      <td>Budanova (L. Y.)</td>
      <td>6</td>
    </tr>
    <tr>
      <td>Bui (Q. C.)</td>
      <td>45</td>
    </tr>
    <tr>
      <td>Bujakowski (K.)</td>
      <td>249</td>
    </tr>
    <tr>
      <td>Buland (R.)</td>
      <td>80</td>
    </tr>
    <tr>
      <td>Burbank (D.)</td>
      <td>80</td>
    </tr>
    <tr>
      <td>Burlini (L.)</td>
      <td>7</td>
    </tr>
    <tr>
      <td>Burnett (B.)</td>
      <td>44, 211, 233</td>
    </tr>
    <tr>
      <td>Burnley (P. C.)</td>
      <td>63</td>
    </tr>
    <tr>
      <td>Bursa (M.)</td>
      <td>98</td>
    </tr>
    <tr>
      <td>Butterfield (D. A.)</td>
      <td>44, 57</td>
    </tr>
    <tr>
      <td>Butuzova (G. O.)</td>
      <td>60</td>
    </tr>
    <tr>
      <td>Byrne (T. B.)</td>
      <td>67, 70</td>
    </tr>
  </tbody>
</table>

C

<table>
  <tbody>
    <tr>
      <td>Cadien (D. B.)</td>
      <td>31</td>
    </tr>
    <tr>
      <td>Calabrese (S.)</td>
      <td>32</td>
    </tr>
    <tr>
      <td>Calder (B. R.)</td>
      <td>57–59, 243</td>
    </tr>
    <tr>
      <td>Calmant (S.)</td>
      <td>30, 33, 34</td>
    </tr>
    <tr>
      <td>Cameron (A. C.)</td>
      <td>125</td>
    </tr>
    <tr>
      <td>Cameron (J.)</td>
      <td>61</td>
    </tr>
    <tr>
      <td>Campbell (K. A.)</td>
      <td>77</td>
    </tr>
    <tr>
      <td>Campos (J.)</td>
      <td>5</td>
    </tr>
    <tr>
      <td>Cande (S. C.)</td>
      <td>28, 29</td>
    </tr>
    <tr>
      <td>Cannata (A.)</td>
      <td>33</td>
    </tr>
    <tr>
      <td>Cao (L.)</td>
      <td>41, 43</td>
    </tr>
    <tr>
      <td>Cao (W.)</td>
      <td>56</td>
    </tr>
    <tr>
      <td>Capote (R.)</td>
      <td>7, 25</td>
    </tr>
    <tr>
      <td>Carfantan (J. C.)</td>
      <td>22–24</td>
    </tr>
    <tr>
      <td>Carlson (P. R.)</td>
      <td>7, 58</td>
    </tr>
    <tr>
      <td>Carney (J. N.)</td>
      <td>33</td>
    </tr>
    <tr>
      <td>Caro (E.)</td>
      <td>80</td>
    </tr>
    <tr>
      <td>Carrizo (D.)</td>
      <td>28, 34</td>
    </tr>
    <tr>
      <td>Carron (M. J.)</td>
      <td>7</td>
    </tr>
    <tr>
      <td>Cartapanis (O.)</td>
      <td>29</td>
    </tr>
    <tr>
      <td>Castillo (P. R.)</td>
      <td>31</td>
    </tr>
    <tr>
      <td>Cazenave (A.)</td>
      <td>74</td>
    </tr>
    <tr>
      <td>Cecioni (A.)</td>
      <td>28, 29</td>
    </tr>
    <tr>
      <td>Chadwick (W. W. J.)</td>
      <td>44, 57</td>
    </tr>
    <tr>
      <td>Chakravarti (N.)</td>
      <td>175</td>
    </tr>
    <tr>
      <td>Chandler (M.)</td>
      <td>56</td>
    </tr>
    <tr>
      <td>Chang (C.)</td>
      <td>67, 70</td>
    </tr>
  </tbody>
</table>

<table>
  <tbody>
    <tr>
      <td>Chang (C.-P.)</td>
      <td>83</td>
    </tr>
    <tr>
      <td>Chang (H. K.)</td>
      <td>51</td>
    </tr>
    <tr>
      <td>Chang (W. Y.)</td>
      <td>4, 42, 44</td>
    </tr>
    <tr>
      <td>Chang (W.-Y.)</td>
      <td>43, 53</td>
    </tr>
    <tr>
      <td>Charvis (P.)</td>
      <td>33</td>
    </tr>
    <tr>
      <td>Chase (C. G.)</td>
      <td>61, 116</td>
    </tr>
    <tr>
      <td>Chayes (D.)</td>
      <td>79</td>
    </tr>
    <tr>
      <td>Chazot (G.)</td>
      <td>32</td>
    </tr>
    <tr>
      <td>Chen (C. H.)</td>
      <td>15</td>
    </tr>
    <tr>
      <td>Chen (C.-H.)</td>
      <td>43, 53</td>
    </tr>
    <tr>
      <td>Chen (D.)</td>
      <td>6, 61, 218</td>
    </tr>
    <tr>
      <td>Chen (H.)</td>
      <td>57, 233</td>
    </tr>
    <tr>
      <td>Chen (J.)</td>
      <td>6</td>
    </tr>
    <tr>
      <td>Chen (L.)</td>
      <td>6, 61, 218</td>
    </tr>
    <tr>
      <td>Chen (M.)</td>
      <td>209</td>
    </tr>
    <tr>
      <td>Chen (P.-C.)</td>
      <td>51</td>
    </tr>
    <tr>
      <td>Chen (S.)</td>
      <td>50</td>
    </tr>
    <tr>
      <td>Chen (W.)</td>
      <td>17</td>
    </tr>
    <tr>
      <td>Chen (Y.)</td>
      <td>29, 56</td>
    </tr>
    <tr>
      <td>Chen (Y.-F.)</td>
      <td>43</td>
    </tr>
    <tr>
      <td>Cheng (Z.)</td>
      <td>47</td>
    </tr>
    <tr>
      <td>Chester (F. M.)</td>
      <td>70</td>
    </tr>
    <tr>
      <td>Cheung (K. F.)</td>
      <td>18, 68</td>
    </tr>
    <tr>
      <td>Chiang (G.-T.)</td>
      <td>116</td>
    </tr>
    <tr>
      <td>Chiba (H.)</td>
      <td>57</td>
    </tr>
    <tr>
      <td>Chikov (B. M.)</td>
      <td>34</td>
    </tr>
    <tr>
      <td>Chilés (J.-P.)</td>
      <td>249</td>
    </tr>
    <tr>
      <td>Chin (S.-J.)</td>
      <td>45</td>
    </tr>
    <tr>
      <td>Chiquin (L.)</td>
      <td>24</td>
    </tr>
    <tr>
      <td>Chiquin (M.)</td>
      <td>24</td>
    </tr>
    <tr>
      <td>Chiu (J.-K.)</td>
      <td>4, 42</td>
    </tr>
    <tr>
      <td>Chiu (S.-D.)</td>
      <td>101</td>
    </tr>
    <tr>
      <td>Chiyonobu (S.)</td>
      <td>43, 65, 209</td>
    </tr>
    <tr>
      <td>Chou (T.-A.)</td>
      <td>80</td>
    </tr>
    <tr>
      <td>Christensen (U. R.)</td>
      <td>40, 74</td>
    </tr>
    <tr>
      <td>Christenson (B. W.)</td>
      <td>76, 249</td>
    </tr>
    <tr>
      <td>Churikova (T.)</td>
      <td>246</td>
    </tr>
    <tr>
      <td>Ciaccio (A. D.)</td>
      <td>105, 154</td>
    </tr>
    <tr>
      <td>Cielen (D.)</td>
      <td>154</td>
    </tr>
    <tr>
      <td>Cifuentes (I. L.)</td>
      <td>28</td>
    </tr>
    <tr>
      <td>Cirano (M.)</td>
      <td>148</td>
    </tr>
    <tr>
      <td>Clark (K.)</td>
      <td>76, 77</td>
    </tr>
    <tr>
      <td>Clark (M. R.)</td>
      <td>8, 29–32, 61, 215, 218, 232, 233, 246</td>
    </tr>
    <tr>
      <td>Clarke (S.)</td>
      <td>5</td>
    </tr>
    <tr>
      <td>Cloetingh (S.)</td>
      <td>28</td>
    </tr>
    <tr>
      <td>Cobas (M.)</td>
      <td>148</td>
    </tr>
  </tbody>
</table>
Cochran (U.) 76, 77
Cochrane (G. R.) 36
Coffin (M. F.) 73
Coffin (R. B.) 77
Coli (M.) 105, 154
Collerson (K. D.) 30, 31
Collot (J.-Y.) 74
Comiti (F.) 81
Conin (M.) 67, 70
Conrad (C. P.) 6, 198
Consalvey (M.) 8
Contreras-Reyes (E.) 8,
27-29, 34, 202
Cooper (A. K.) 36
Corbi (F.) 218
Coren (F.) 74
Cornillon (P. C.) 14
Costello (M. J.) 8
Cotten (J.) 33
Coulbourn (W. T.) 25
Cournapeau (D.) 152
Covault (J. A.) 43
Cowan (D. S.) 25
Crameri (F.) 149
Crawford (B. R.) 242, 246
Cressie (N. A. C.) 105
Crippen (R.) 80
Crosato (A.) 81
Cruden (A. R.) 61, 218
Crutchley (G. J.) 76, 77
Cui (W.) 4
Cui (X.) 7
Cui (Z.) 29, 56
Cull (J. P.) 37
Culotta (R.) 67
Cuomo (N. J.) 12
Curtis (A. C.) 20, 57
Cízková (H.) 41, 43, 63, 65,
218

D

D'Arrigo (R.) 16
Dada (O. A.) 29, 56
Dahlin (T.) 249
Dai (L.) 5
Dale (D.) 155
Daly (R. A.) 6
Danovaro (R.) 26, 29, 232
Dantas (E.) 81

Davey (F. J.) 74
David (H. A.) 154
Davies (H. L.) 37
Davis (G. P.) 101, 108
Davis (J. C.) 105, 125
Davis (J. R.) 105, 116, 150
De Batist (M.) 76, 77
De Leo (F. C.) 246
DeBari (S. M.) 65, 209
Deen (T.) 253
Delfiner (P.) 249
Della Croce (N.) 232
Delteil (J.) 74
Demant (A.) 22-24
DeMets (C.) 15, 63, 92, 93
Den (N.) 67
Deng (H.) 6
Depner (J.) 6
Deschamps (A.) 44, 59
Dezileau (L.) 29
Di Giacomo (D.) 80
Di Giuseppe (E.) 6
Dic (R.) 61
Dierssen (H. M.) 4, 12
Dimalanta (C. B.) 44
Ding (H.) 47
Ding (W.) 47
Divins (D.) 42, 219
Dixon (T. H.) 93
Dmitriev (Y. I.) 23
Doglioni (C.) 5
Dokht (R. M. H.) 5
Dolotov (Y. S.) 43, 198
Dong (D.) 55, 198
Doo (W.-B.) 45, 47, 48, 51,
101
Dorschel (B.) 79, 80
Dosso (L.) 32
Dostal (J.) 34, 56, 58
Dougherty (D.) 112
Dove (M. T.) 116
Dowling (L.) 76, 77
Downes (G.) 77
Draut (A. E.) 18
Drazen (J. C.) 30-32, 61, 215,
218, 232, 233
Dresen (G.) 68
Drewes (H.) 18

Droettboom (M.) 155
Dubinin (E. P.) 60
Dubourg (V.) 152
Duchesnay (E.) 152
Duncan (R. A.) 30, 77
Dunn (D. C.) 119
Dupuy (C.) 34
Duquesnoy (T.) 24
Duren (R.) 80
Durham (W. B.) 63
Dutkiewicz (A.) 198
Dziewonski (A. M.) 80, 88

E

Eagles (G.) 253
Eakins (B. W.) 78, 80
Eaton (J. W.) 114
Eberhart-Phillips (D.) 76, 77
Ebina (N.) 57
Edwards (M.) 58
Egloff (F.) 48
Eissen (J.-P.) 32, 33
Ekman (M.) 98
Ekström (G.) 18, 80, 88
Elek (I.) 133
Elliott (T.) 60, 63, 218
Ellis (S. M.) 73, 77
Endo (D.) 66
Engdahl (E. R.) 18, 80
Ernst (W. G.) 190
Escauriaza (C.) 81
Eustace (R. M.) 60
Everitt (B.) 171
Ewart (A.) 30, 31
Ewen (J.) 116
Ewing (J. I.) 67
Ewing (M.) 67

F

Faas (R. W.) 25
Fabre (D.) 6
Faccenda (M.) 7, 47
Faccenna (C.) 6, 7, 42, 44, 49,
57, 58, 61, 63, 66,
77, 116
Factor (J.) 6
Fagereng (A.) 77
Fahl (A.) 253
Falahat (S.) 29
<table><tbody><tr><td>Falconer (R.)</td><td>79, 80</td></tr><tr><td>Falvey (D. A.)</td><td>30</td></tr><tr><td>Fan (J.)</td><td>44, 45, 55, 198</td></tr><tr><td>Fan (L.)</td><td>57, 233</td></tr><tr><td>Fang (J.)</td><td>31, 56, 215, 233</td></tr><tr><td>Fang (P.)</td><td>47</td></tr><tr><td>Farr (T. G.)</td><td>80</td></tr><tr><td>Faure (K.)</td><td>76, 77, 249</td></tr><tr><td>Faustino-Eslava (D. V.)</td><td>44</td></tr><tr><td>Feigl (T.)</td><td>249, 253</td></tr><tr><td>Fernandez (M. O.)</td><td>5</td></tr><tr><td>Ferrari (L.)</td><td>28</td></tr><tr><td>Ferrini (V.)</td><td>79, 80</td></tr><tr><td>Filizola (N.)</td><td>81</td></tr><tr><td>Fink (H.)</td><td>68</td></tr><tr><td>Finlayson (D. M.)</td><td>37</td></tr><tr><td>Firing (E.)</td><td>155</td></tr><tr><td>Fischer (R. L.)</td><td>26</td></tr><tr><td>Fisher (E. H.)</td><td>81</td></tr><tr><td>Fitzgerald (P. G.)</td><td>35</td></tr><tr><td>Flament (N.)</td><td>28</td></tr><tr><td>Fletcher (M.)</td><td>5</td></tr><tr><td>Fliedner (M.)</td><td>18</td></tr><tr><td>Flores (O.)</td><td>24</td></tr><tr><td>Flueh (E. R.)</td><td>27, 29</td></tr><tr><td>Fodrie (F. J.)</td><td>18</td></tr><tr><td>Fohrmann (M.)</td><td>77</td></tr><tr><td>Forsyth (D. W.)</td><td>34, 40</td></tr><tr><td>Forwick (M.)</td><td>249</td></tr><tr><td>Fourré (E.)</td><td>32</td></tr><tr><td>Francis (R.)</td><td>98, 101, 209</td></tr><tr><td>Franco (A.)</td><td>24</td></tr><tr><td>Fraser (G. D.)</td><td>18</td></tr><tr><td>Freitag (R.)</td><td>15, 18</td></tr><tr><td>Freymuth (H.)</td><td>60, 63, 218</td></tr><tr><td>Friedman (J. R.)</td><td>30</td></tr><tr><td>Friendly (M.)</td><td>160</td></tr><tr><td>Frigge (M.)</td><td>162</td></tr><tr><td>Fryer (G.)</td><td>18, 58</td></tr><tr><td>Fryer (P.)</td><td>8, 58, 61</td></tr><tr><td>Fu (Y.)</td><td>202</td></tr><tr><td>Fujie (G.)</td><td>12, 65</td></tr><tr><td>Fujii (T.)</td><td>30–32, 64, 215, 232</td></tr><tr><td>Fujikura (K.)</td><td>60</td></tr><tr><td>Fujio (S.)</td><td>29</td></tr><tr><td>Fujioka (K.)</td><td>4, 6, 42, 53, 55–57, 63, 65, 68, 183, 209, 222, 246</td></tr></tbody></table>

<table><tbody><tr><td>Fujiwara (T.)</td><td>44, 53, 55, 70, 183, 222</td></tr><tr><td>Fukao (Y.)</td><td>5, 15, 17</td></tr><tr><td>Fukumori (H.)</td><td>14</td></tr><tr><td>Fuller (I.)</td><td>81</td></tr><tr><td>Fuller (M.)</td><td>4, 7, 44</td></tr><tr><td>Funiciello (F.)</td><td>6, 7, 42, 61, 63, 66, 116, 198, 218</td></tr><tr><td>Furukawa (R.)</td><td>15</td></tr><tr><td>Furuya (H.)</td><td>44, 66</td></tr></tbody></table>

G

<table><tbody><tr><td>Gaedicke (C.)</td><td>15, 18</td></tr><tr><td>Gaillot (A.)</td><td>233</td></tr><tr><td>Gaina (C.)</td><td>56</td></tr><tr><td>Galbraith (E.)</td><td>29</td></tr><tr><td>Galewsky (J.)</td><td>35</td></tr><tr><td>Galicia (O.)</td><td>24</td></tr><tr><td>Gallagher (S.)</td><td>5</td></tr><tr><td>Galli (C.)</td><td>24</td></tr><tr><td>Gallo (N. D.)</td><td>61</td></tr><tr><td>Gambi (C.)</td><td>26, 29, 232</td></tr><tr><td>Gamble (J. A.)</td><td>30</td></tr><tr><td>Gamo (T.)</td><td>57</td></tr><tr><td>Ganzey (L. A.)</td><td>17</td></tr><tr><td>Gao (R.)</td><td>34</td></tr><tr><td>Gao (X.)</td><td>41, 43</td></tr><tr><td>Garaebiti (E.)</td><td>32, 33</td></tr><tr><td>Garcia (E.)</td><td>80, 98, 101, 209</td></tr><tr><td>Gardner (J. V.)</td><td>57–59</td></tr><tr><td>Garfunkel (Z.)</td><td>58</td></tr><tr><td>Garzanti (E.)</td><td>150</td></tr><tr><td>Gauger (S.)</td><td>249, 253</td></tr><tr><td>Gauthier (P. J.)</td><td>32</td></tr><tr><td>Ge (H.)</td><td>31, 56, 215</td></tr><tr><td>GEBCO Committee</td><td>79</td></tr><tr><td>Geersen (J.)</td><td>27, 29</td></tr><tr><td>Gelman (A.)</td><td>167</td></tr><tr><td>Gerringer (M. E.)</td><td>31, 32, 232, 233</td></tr><tr><td>Gerya (T. V.)</td><td>7</td></tr><tr><td>Giacobbe (S.)</td><td>233</td></tr><tr><td>Gieskes (J.)</td><td>6, 18, 61, 218</td></tr><tr><td>Giletycz (S. J.)</td><td>83</td></tr><tr><td>Gill (J.)</td><td>105</td></tr><tr><td>Gille (S. T.)</td><td>43</td></tr><tr><td>Giuseppe (E. D.)</td><td>42, 63, 66</td></tr><tr><td>Glade (T.)</td><td>249</td></tr></tbody></table>

<table><tbody><tr><td>Glud (R. N.)</td><td>29, 30, 233</td></tr><tr><td>Go (C. N. A.)</td><td>45</td></tr><tr><td>Gohl (K.)</td><td>249, 253</td></tr><tr><td>Goldfinger (C.)</td><td>7</td></tr><tr><td>Golovand (O.)</td><td>14</td></tr><tr><td>Golub (G. H.)</td><td>155</td></tr><tr><td>Gong (L.)</td><td>233</td></tr><tr><td>Gong (W.)</td><td>4, 66</td></tr><tr><td>Gong (Z.)</td><td>57, 233</td></tr><tr><td>González (M.)</td><td>25</td></tr><tr><td>Goodlife (A. M.)</td><td>31</td></tr><tr><td>Gorbatov (A.)</td><td>5, 15, 17, 34, 190</td></tr><tr><td>Gordeev (E.)</td><td>5, 15, 17</td></tr><tr><td>Gordon (R. G.)</td><td>15, 63, 92, 93</td></tr><tr><td>Gorini (M. A. V.)</td><td>6, 9</td></tr><tr><td>Gorman (A. R.)</td><td>73, 77</td></tr><tr><td>Gorospe (M. L.)</td><td>242, 246</td></tr><tr><td>Gorshkov (G. S.)</td><td>15</td></tr><tr><td>Gouretski (V.)</td><td>80</td></tr><tr><td>Graf (W.)</td><td>81</td></tr><tr><td>Gramfort (A.)</td><td>152</td></tr><tr><td>Grand (S. P.)</td><td>8</td></tr><tr><td>Granger (B. E.)</td><td>152</td></tr><tr><td>Grebennikova (T. A.)</td><td>17</td></tr><tr><td>Green II (H. W.)</td><td>63</td></tr><tr><td>Gregorova (B.)</td><td>249</td></tr><tr><td>Gregorová (B.)</td><td>242, 249</td></tr><tr><td>Greinert (J.)</td><td>76, 77</td></tr><tr><td>Gretskaya (E. V.)</td><td>17</td></tr><tr><td>Grevemeyer (I.)</td><td>27, 29</td></tr><tr><td>Griffiths (J. S.)</td><td>28, 29</td></tr><tr><td>Griffiths (R. W.)</td><td>63</td></tr><tr><td>Grisel (O.)</td><td>152</td></tr><tr><td>Grobys (J.)</td><td>253</td></tr><tr><td>Gross (M.)</td><td>135</td></tr><tr><td>Gu (Y. J.)</td><td>5</td></tr><tr><td>Gualtieri (C.)</td><td>81</td></tr><tr><td>Gubbins (D.)</td><td>63</td></tr><tr><td>Guo (L.)</td><td>5</td></tr><tr><td>Guo (W.)</td><td>4</td></tr><tr><td>Guo (Y.)</td><td>4, 66</td></tr><tr><td>Guotana (J. M. R.)</td><td>44</td></tr><tr><td>Gupta (A.)</td><td>70</td></tr><tr><td>Gurnell (A. M.)</td><td>81</td></tr><tr><td>Gurnis (M.)</td><td>56</td></tr><tr><td>Gurvich (E. G.)</td><td>57, 58, 61</td></tr><tr><td>Gusiakov (V. K.)</td><td>17</td></tr></tbody></table>
Gutiérrez Gutiérrez (O. Q.)
25
Gutscher (M.-A.) 42, 50
Guzmán-Speziale (M.) 22
Géli (L.) 5
Gómez-González (J. M.) 22

H

Haase (K. M.) 33, 205
Hackney (R.) 63
Haddock (S. H. D.) 8
Haines (J.) 72
Hall (R.) 4, 7, 42, 44
Halpin (P. N.) 119
Halterman (R. L.) 150
Hamada (C.) 253
Hang (T. T.) 56
Hao (K.-C.) 45, 47
Haraguchi (S.) 4, 42
Hardy (K.) 61
Haren (H.) van 61, 233
Harrington (A. N.) 150
Harris (J.) 80
Harris (P. T.) 14, 43, 58, 218
Hartley (A. J.) 28, 29
Hartwell (A. M.) 233
Hashima (A.) 44, 66
Hashimoto (J.) 58
Hashimoto (K.) 37
Hass (C.) 249
Hauberg (S.) 114
Hauton (C.) 81
Hawkins (J. W.) 31
Haxby (F.) 219
Hayakawa (M.) 67
Hayakawa (T.) 69
Hayes (D. E.) 29
He (C.) 246
He (E.) 47
He (R.-W.) 34
Heesemann (M.) 29
Heezen (B. C.) 6, 32
Heiskanen (W.) 98
Heki (K.) 66
Helffrich (G. R.) 60, 66
Helu (S.) 30
Henrys (S. A.) 77
Henrys (S.) 76, 77
Hensley (S.) 80

Hergt (J. M.) 31
Herman (F.) 76
Hessler (R. R.) 44, 211, 233
Heubeck (C.) 27, 29
Heuret (A.) 6-8, 42, 54, 61, 62, 116, 198, 218
Heyman (W. D.) 81
Hickey-Vargas (R.) 20, 66
Hilde (T. W. C.) 44, 253
Hillenbrand (C. D.) 253
Hillenbrand (C.-D.) 249
Hilst (R. D.) van der 8, 48, 58, 63, 80
Hino (R.) 35, 37, 65, 69
Hirano (N.) 60, 246
Hirano (S.) 43, 44
Hoaglin (D. C.) 125, 155, 162
Hoernle (K.) 246
Hofmann H. Kafadar (K.) 154, 155
Hokanishi (N.) 66
Holbroock (W. S.) 18
Hollister (C. D.) 32
Holm (R. J.) 35
Holt (A. F.) 44, 49, 57, 58, 77
Honda (M. C.) 50
Honza (E.) 37
Horiuchi (D.) 49, 209
Horleston (A. C.) 60, 66
Hornik (K.) 176
Horton (T.) 30
Hothorn (T.) 126
Hotta (H.) 4, 63, 67, 68
Howard (F.) 5
Howarth (J.) 76, 77
Howell (A.) 76, 77
Hsieh (H.-S.) 43
Hsu (S.-K.) 29, 42, 43, 45, 47, 48, 51
Hsu (Y.-J.) 44, 83
Hsua (S.-K.) 101
Hu (D.) 47
Hu (Y.) 4
Huaman (D.) 33, 81
Huang (H. L.) 7
Huang (H.-C.) 45
Huang (Y.-S.) 47, 48, 101
Hubble (T.) 5

Hudson (P. F.) 81
Huene (R.) 18, 25, 34, 67, 104
Huijbregts (C.) 9
Hull (A.) 77
Hunen (J.) van 42, 63, 66
Hunter (J. D.) 152
Hunter (J.) 155
Husker (A.) 28
Husson (L.) 58
Hussong (D. M.) 66, 209
Huvenne (V. A. I.) 81
Hwang (R.-D.) 4, 42, 44
Hühnerbach (V.) 81

I

Ianniruberto (M.) 81
Ichikawa (K.) 67
Ichino (M. C.) 61, 215, 218
Ichiyama (Y.) 66
Iglewicz (B.) 162
IHO 3, 7, 80
Idaka (T.) 43
Ikari (M. J.) 5
Ikehara (K.) 67, 68
Ikuta (R.) 48
Iliev (A. Y.) 17
Imai (T.) 48
Imaoka (T.) 50
Ingalls (S.) 6
Ingram (C. L.) 44, 211, 233
Inoue (T.) 52, 67
International Seismological Centre 80
Irino (T.) 68
Iryu (Y.) 43, 52, 65, 209
Isacks (B. L.) 30, 63, 70
Ishibashi (J.-i.) 57
Ishibashi (K.) 77
Ishida (K.) 44
Ishii (T.) 4, 8, 42, 56, 58, 246
Ishizuka (H.) 50
Ishizuka (O.) 4, 20, 42, 56, 63, 64, 66, 209, 246
Iso (N.) 77
Isse (T.) 43
Ito (A.) 12, 43
Ito (H.) 66
Ito (T.) 44, 66, 68
<table><tr><td>Itoh (M.)</td><td>51, 233</td></tr><tr><td>Iton (T.)</td><td>67, 70</td></tr><tr><td>Iwabuchi (Y.)</td><td>53, 55, 183, 222</td></tr><tr><td>Iwasaki (L. T.)</td><td>48</td></tr><tr><td>Izbekov (P. E.)</td><td>15</td></tr></table>

J

<table><tr><td>Jaccard (S. L.)</td><td>29</td></tr><tr><td>Jackson (R. J.)</td><td>37</td></tr><tr><td>Jakobsson (M.)</td><td>79, 80</td></tr><tr><td>Jamieson (A. J.)</td><td>8, 12, 29-32, 60, 61, 215, 218, 232, 233</td></tr><tr><td>Janeka (K.)</td><td>249</td></tr><tr><td>Jang (J.-P.)</td><td>51</td></tr><tr><td>Janssen (C.)</td><td>68</td></tr><tr><td>Jara (J.)</td><td>27, 29, 202</td></tr><tr><td>Jarrard (R.)</td><td>61</td></tr><tr><td>Jean-Baptiste (P.)</td><td>32</td></tr><tr><td>Jelsma (H. A.)</td><td>35</td></tr><tr><td>Jenkins (R. G.)</td><td>60</td></tr><tr><td>Jensen (A.)</td><td>28</td></tr><tr><td>Jiang (X.)</td><td>4, 66</td></tr><tr><td>Jiao (R.)</td><td>76, 77</td></tr><tr><td>Jicha (B. R.)</td><td>15, 246</td></tr><tr><td>Jimenez-Diaz (A.)</td><td>7</td></tr><tr><td>Johnson (R. W.)</td><td>37</td></tr><tr><td>Jones (D. O. B.)</td><td>61, 215, 218</td></tr><tr><td>Jones (E.)</td><td>105</td></tr><tr><td>Jordan (T. H.)</td><td>43, 209</td></tr><tr><td>Joron (J.-L.)</td><td>23</td></tr><tr><td>Joshima (M.)</td><td>37, 63, 209</td></tr><tr><td>Journel (A. G.)</td><td>9, 123, 125, 165</td></tr><tr><td>Joydas (T. V.)</td><td>233</td></tr><tr><td>Judge (A. V.)</td><td>7</td></tr><tr><td>Jung (W.-Y.)</td><td>7</td></tr><tr><td>Just (J.)</td><td>253</td></tr><tr><td>Jónasson (H.)</td><td>198</td></tr></table>

K

<table><tr><td>Kaiho (Y.)</td><td>70</td></tr><tr><td>Kaistrenko (V. M.)</td><td>17</td></tr><tr><td>Kalnins (L. M.)</td><td>7</td></tr><tr><td>Kameda (J.)</td><td>5, 70</td></tr><tr><td>Kameo (K.)</td><td>44, 66</td></tr><tr><td>Kamimura (A.)</td><td>65, 69</td></tr><tr><td>Kanagawa (K.)</td><td>67, 70</td></tr></table>

Kanamatsu (T.) 4, 6, 42, 57, 67, 68, 70

Kanamori (H.) 16, 58, 61, 68

Kanazawa (T.) 43, 48, 65, 69

Kaneda (H.) 44, 66

Kaneda (K.) 64

Kaneda (Y.) 12, 69, 70

Kao (H.) 17, 43, 53

Karato (S.) 58, 70, 74

Karig (D. E.) 43, 44

Karner (G. D.) 219

Kasahara (A.) 68

Kasahara (J.) 55, 65, 69

Kasatkin (S. A.) 14, 16

Katagiri (H.) 51, 233

Katao (H.) 55

Kato (M.) 43, 209

Kawabata (H.) 50

Kawabe (M.) 58

Kawamura (K.) 51, 233

Kay (R. W.) 18

Kazakova (V.) 25

Keene (J. B.) 37

Kelemen (P. B.) 246

Kendrick (E.) 30

Kennett (B. L. N.) 34, 63, 190

Kennett (J.) 6

Kernigan (B. W.) 110

Kharlamov (A. A.) 17

Khomarudin (M. R.) 103

Khomich (V. G.) 14, 16

Kido (Y.) 69

Kilgallen (N. M.) 29, 30, 60, 61

Kilner (J.) 77

Kim (S. H.) 6

Kim (Y. M.) 222

Kiminami (K.) 50

Kimura (A.) 61, 233

Kimura (G.) 67, 70

Kinoshita (M.) 67, 70

Kirby (J. F.) 7

Kirby (S. H.) 18, 63, 205

Kirkpatrick (J. D.) 70

Kitagawa (S.) 8, 59

Kitahashi (T.) 51, 60, 233

Kitamura (A.) 48

Kitazato (H.) 29, 30, 53, 56,

183, 222, 233

Kitazawa (K.) 30

Kiyokawa (S.) 64

Kizaki (K.) 49

Klaucke (I.) 61, 77, 233

Klauco (M.) 242, 249

Klauer (B.) 81

Klauo (M.) 249

Kleiber (C.) 127

Klemperer (S. L.) 18

Klingelhoefer (F.) 42, 50, 51

Kluesner (J. W.) 18

Kniesz (K.) 14

Knuth (D. E.) 177

Kobayashi (K.) 4, 53, 55, 63, 68, 183, 222

Kober (M.) 249

Kobrick (M.) 80

Kodaira (S.) 8, 12, 48, 64, 67, 69, 70

Kohn (B. P.) 76, 77

Kohn (R. J.) 176

Koizumi (K.) 44

Kojima (S.) 51, 60, 233

Koleda (P.) 249

Kong (X.) 5

Kono (T.) 69

Kopf (A. J.) 5

Koresawa (S.) 55

Kostoglodov (V.) 17, 28

Kotowicz (D.) 253

Kou (Q.) 233

Kozyreva (N. P.) 17

Krabbenhoeft (A.) 77

Kravchunovskaya (E. A.) 15, 17

Krbetschek (M.) 15

Krishnakumar (P. K.) 233

Kroenke (L. W.) 77

Kruger (S.) 249

Kruskal (J. B.) 175

Ku (C.-Y.) 47

Kuhlman (D.) 150

Kuhn (G.) 249, 253

Kukowski (N.) 77

Kulm (L. D.) 27, 29, 42

Kumar (M.) 80

Kuo (B.-Y.) 51
Kurnosov (V. B.) 23, 25
Kusano (Y.) 20, 66

L

Laaha (G.) 9
LaBrecque (J. L.) 219
Lacey (N. C.) 29-31, 61
Lacharité (M.) 198
Ladd (J.) 25
Ladner (R.) 6
Laing (A.) 37
Lallemand (S.) 6-8, 29, 42,
44, 49, 51, 54,
57-59, 61-63, 66,
67, 77, 116, 218
Lamarche (G.) 74, 76, 77, 79,
80
Lamba (T. S.) 116
Lambeck (K.) 37
Lander (A. V.) 15
Lang (W.) 61, 233
Lanier (A.) 7
Larina (N. I.) 6
Larsen (B.) 211, 246
Larsen (W. A.) 155, 162
Larter (R. D.) 253
Lasserre (C.) 24
Lavier (L.) 47
Lavrenteva (A.) 14
Lawford (S.) 105
Lay (T.) 68
Le (T. B.) 81
Lebellegard (P.) 33
Lebrun (J.-F.) 74
Leduc (D.) 30, 31, 233
Lee (C.-S.) 42-44, 50, 51,
253
Lee (S.-M.) 44, 55, 222
Lees (J. M.) 17
Leeuw (J. d.) 176
Lehu (R.) 29
Leinweber (V.) 253
Lemenkov (V.) 249
Lemenkova (P.) ix, xvi-xviii,
xx-xxii, 4, 6, 9-16,
18-28, 32-35, 37,
44-46, 48-51,
53-56, 61, 63-65,
67, 70, 72-78, 81,

84, 85, 87, 94, 97,
98, 100, 101,
103-106, 109, 111,
114-116, 120-122,
125-130, 133-135,
137-144, 147-149,
155, 157, 158, 162,
168, 172, 173, 175,
176, 180, 182-184,
187, 188, 191-203,
205, 209-214, 216,
217, 219-223,
226-232, 234-236,
239-242, 244, 247,
249, 250, 252, 253
Lemoine (A.) 5
Lensch (N.) 253
Leonov (V. L.) 15
Leslie (R. B.) 28
Leslie (S. D.) 28, 29
Lester (R.) 47
Levin (L. A.) 18, 31, 61
Levy (L. S.) 116
Lewin (J.) 246
Lewis (J. C.) 67, 70
Lewis (K. B.) 76, 77
Li (C.) 4, 66
Li (F.) 47
Li (J.) 47
Li (Q.-S.) 34
Li (S.) 5
Li (T.-D.) 34
Li (X.) 233
Li (Y.) 57, 233
Liang (C.-W.) 42, 43, 45, 101
Lieberman (J. E.) 110
Lima (S. M.) 33
Lin (A. T. S.) 83
Lin (A. T.) 29
Lin (C.-R.) 51
Lin (J. W. B.) 148
Lin (J.) 34, 58-61, 63, 168,
246
Lin (J.-Y.) 4, 42, 43, 45, 48
Lin (S.-C.) 51
Lin (W.) 67, 70
Lin (X.) 47
Lin (Y.-C.) 43

Lin (Z.) 29, 56
Lindh (P.) 249
Lindley (I. D.) 35, 37
Linke (P.) 76, 77
Linley (T. D.) 29-32, 61, 232
Lisicyn (A. P.) 61, 198
Listanco (E. L.) 4
Lister (G. S.) 28, 34, 63
Litchfield (N.) 76, 77
Little (R. P.) 37
Little (T. A.) 76, 77
Litvin (V. M.) 4, 6
Liu (C.-S.) 47, 51
Liu (F.) 7
Liu (H.) 45, 57, 233
Liu (J.) 57, 233
Liu (M.) 7
Liu (R.) 233
Liu (X.) 56
Liu (Y.) 45, 56, 246
Lizarraide (D.) 18
Ljung (G. M.) 172
Lo (C.-L.) 4, 42, 45, 47, 48,
101
Loan (C. F.) van 155
Lodolo (E.) 74
Loher (M.) 6
Long (M. D.) 48
Long (P. J. G.) 115
Lonsdale (P. F.) 31, 104
Lopes (R. M. C.) 33
Lorenz (R. D.) 33
Louat (R.) 33, 246
Lu (B.) 56
Lu (J.) 56
Luchinina (A. A.) 17
Ludwig (T.) 190
Ludwig (W. J.) 67
Luis (J. F.) 48, 80, 83, 244,
248
Luna (J.) 24
Luo (M.) 6, 61, 218
Lv (X.) 56
Lyon-Caen (H.) 24
Lörz (A.-N.) 14, 30, 31

M

Ma (L.) 47
Ma (Y.-F.) 101
<table>
  <tbody>
    <tr>
      <td>Macdonald (K. C.)</td>
      <td>42</td>
    </tr>
    <tr>
      <td>MacFarlane (A.)</td>
      <td>33</td>
    </tr>
    <tr>
      <td>Machiyama (H.)</td>
      <td>52</td>
    </tr>
    <tr>
      <td>MacInnes (B. T.)</td>
      <td>15, 17</td>
    </tr>
    <tr>
      <td>MacLeod (C. J.)</td>
      <td>31</td>
    </tr>
    <tr>
      <td>Macmillan-Lawler (M.)</td>
      <td>14, 43, 218</td>
    </tr>
    <tr>
      <td>Madariaga (R.)</td>
      <td>5</td>
    </tr>
    <tr>
      <td>Madin (L. P.)</td>
      <td>8</td>
    </tr>
    <tr>
      <td>Mahoney (J. J.)</td>
      <td>77</td>
    </tr>
    <tr>
      <td>Maillet (P.)</td>
      <td>33</td>
    </tr>
    <tr>
      <td>Maiorova (A. S.)</td>
      <td>14</td>
    </tr>
    <tr>
      <td>Mair (P.)</td>
      <td>176</td>
    </tr>
    <tr>
      <td>Makris (J.)</td>
      <td>48</td>
    </tr>
    <tr>
      <td>Maksymowicz (A.)</td>
      <td>27, 29, 202</td>
    </tr>
    <tr>
      <td>Malys (S.)</td>
      <td>98</td>
    </tr>
    <tr>
      <td>Malyutina (M.)</td>
      <td>14</td>
    </tr>
    <tr>
      <td>Mammerickx (J.)</td>
      <td>35</td>
    </tr>
    <tr>
      <td>Manea (M.)</td>
      <td>28</td>
    </tr>
    <tr>
      <td>Manea (V. C.)</td>
      <td>15, 28</td>
    </tr>
    <tr>
      <td>Manikandan (K. P.)</td>
      <td>233</td>
    </tr>
    <tr>
      <td>Mann (P.)</td>
      <td>73</td>
    </tr>
    <tr>
      <td>Mao (A.)</td>
      <td>93</td>
    </tr>
    <tr>
      <td>Mao (L.)</td>
      <td>81</td>
    </tr>
    <tr>
      <td>Mao (X.)</td>
      <td>6</td>
    </tr>
    <tr>
      <td>Marcelot (G.)</td>
      <td>34</td>
    </tr>
    <tr>
      <td>Marchese (E.)</td>
      <td>81</td>
    </tr>
    <tr>
      <td>Marcon (Y.)</td>
      <td>6, 233</td>
    </tr>
    <tr>
      <td>Mark (D. F.)</td>
      <td>35</td>
    </tr>
    <tr>
      <td>Markovic (V.)</td>
      <td>249</td>
    </tr>
    <tr>
      <td>Markovic (V.)</td>
      <td>242, 249</td>
    </tr>
    <tr>
      <td>Markovi (V.)</td>
      <td>249</td>
    </tr>
    <tr>
      <td>Marks (K. M.)</td>
      <td>6, 79</td>
    </tr>
    <tr>
      <td>Marova (N. A.)</td>
      <td>6</td>
    </tr>
    <tr>
      <td>Marques (A. C.)</td>
      <td>5</td>
    </tr>
    <tr>
      <td>Marta-Almeida (M.)</td>
      <td>148</td>
    </tr>
    <tr>
      <td>Martin (A. P.)</td>
      <td>61, 215, 218</td>
    </tr>
    <tr>
      <td>Martin (J. B.)</td>
      <td>18</td>
    </tr>
    <tr>
      <td>Martin (R. A.)</td>
      <td>77</td>
    </tr>
    <tr>
      <td>Martin (T.)</td>
      <td>5</td>
    </tr>
    <tr>
      <td>Martinez (F.)</td>
      <td>58, 209</td>
    </tr>
    <tr>
      <td>Martins (L. O.)</td>
      <td>61, 233</td>
    </tr>
    <tr>
      <td>Marton (F. C.)</td>
      <td>39, 74</td>
    </tr>
    <tr>
      <td>Martínez-Díaz (J. J.)</td>
      <td>25</td>
    </tr>
    <tr>
      <td>Maruyama (S.)</td>
      <td>9, 44</td>
    </tr>
    <tr>
      <td>Masson (D. G.)</td>
      <td>6, 81</td>
    </tr>
    <tr>
      <td>Masuda (H.)</td>
      <td>57</td>
    </tr>
  </tbody>
</table>

<table>
  <tbody>
    <tr>
      <td>Mather (A. E.)</td>
      <td>28, 29</td>
    </tr>
    <tr>
      <td>Matsuda (H.)</td>
      <td>52</td>
    </tr>
    <tr>
      <td>Matsuzaki (H.)</td>
      <td>56</td>
    </tr>
    <tr>
      <td>Maulana (T.)</td>
      <td>103</td>
    </tr>
    <tr>
      <td>Maury (R. C.)</td>
      <td>23</td>
    </tr>
    <tr>
      <td>Maus (S.)</td>
      <td>56</td>
    </tr>
    <tr>
      <td>Mayer (L. A.)</td>
      <td>79, 80, 243</td>
    </tr>
    <tr>
      <td>Mayor (D. J.)</td>
      <td>29, 30, 61, 215</td>
    </tr>
    <tr>
      <td>Mayr (C.)</td>
      <td>253</td>
    </tr>
    <tr>
      <td>McCaffrey (R.)</td>
      <td>77</td>
    </tr>
    <tr>
      <td>McCarthy (A.)</td>
      <td>20, 66</td>
    </tr>
    <tr>
      <td>McClusky (S. C.)</td>
      <td>37</td>
    </tr>
    <tr>
      <td>McColl (S.)</td>
      <td>81</td>
    </tr>
    <tr>
      <td>McGeary (S.)</td>
      <td>18</td>
    </tr>
    <tr>
      <td>McGill (R.)</td>
      <td>155, 162</td>
    </tr>
    <tr>
      <td>McIntosh (K.)</td>
      <td>47, 51</td>
    </tr>
    <tr>
      <td>Mckenzie (D.)</td>
      <td>7</td>
    </tr>
    <tr>
      <td>McMillan (P. A.)</td>
      <td>18</td>
    </tr>
    <tr>
      <td>McMillan (P. J.)</td>
      <td>31, 32, 232</td>
    </tr>
    <tr>
      <td>McNeill (L. C.)</td>
      <td>67, 70</td>
    </tr>
    <tr>
      <td>McNutt (M. K.)</td>
      <td>7</td>
    </tr>
    <tr>
      <td>McQueen (H.)</td>
      <td>37</td>
    </tr>
    <tr>
      <td>Meckel (T. A.)</td>
      <td>73</td>
    </tr>
    <tr>
      <td>Meijer (P. T.)</td>
      <td>25</td>
    </tr>
    <tr>
      <td>Melanholina (E. N.)</td>
      <td>61</td>
    </tr>
    <tr>
      <td>Melhuish (A.)</td>
      <td>74</td>
    </tr>
    <tr>
      <td>Menard (H. W.)</td>
      <td>4, 42</td>
    </tr>
    <tr>
      <td>Mendoza (G.)</td>
      <td>18</td>
    </tr>
    <tr>
      <td>Mercier de Lepinay (B.)</td>
      <td>77</td>
    </tr>
    <tr>
      <td>Merle (S. G.)</td>
      <td>44, 57</td>
    </tr>
    <tr>
      <td>Metaxas (A.)</td>
      <td>198</td>
    </tr>
    <tr>
      <td>Metrich (N.)</td>
      <td>33</td>
    </tr>
    <tr>
      <td>Metzger (E. J.)</td>
      <td>43</td>
    </tr>
    <tr>
      <td>Meysman (A. D. B.)</td>
      <td>154</td>
    </tr>
    <tr>
      <td>Micallef (A.)</td>
      <td>6</td>
    </tr>
    <tr>
      <td>Michel (V.)</td>
      <td>152</td>
    </tr>
    <tr>
      <td>Michibayashi (K.)</td>
      <td>8, 58</td>
    </tr>
    <tr>
      <td>Middelboe (M.)</td>
      <td>29</td>
    </tr>
    <tr>
      <td>Middelkoop (H.)</td>
      <td>81</td>
    </tr>
    <tr>
      <td>Mikhina (V.)</td>
      <td>25</td>
    </tr>
    <tr>
      <td>Miller (M. S.)</td>
      <td>34, 63, 190</td>
    </tr>
    <tr>
      <td>Milliman (J. D.)</td>
      <td>9</td>
    </tr>
    <tr>
      <td>Millman (K. J.)</td>
      <td>171</td>
    </tr>
    <tr>
      <td>Minami (H.)</td>
      <td>50</td>
    </tr>
    <tr>
      <td>Mitchell (D.)</td>
      <td>5</td>
    </tr>
    <tr>
      <td>Mitchell (N. C.)</td>
      <td>6</td>
    </tr>
    <tr>
      <td>Miura (S.)</td>
      <td>67, 69, 70</td>
    </tr>
    <tr>
      <td>Miyairi (Y.)</td>
      <td>48</td>
    </tr>
  </tbody>
</table>

<table>
  <tbody>
    <tr>
      <td>Miyauchi (T.)</td>
      <td>44, 66, 77</td>
    </tr>
    <tr>
      <td>Mochizuki (K.)</td>
      <td>35, 37, 55</td>
    </tr>
    <tr>
      <td>Molina (E.)</td>
      <td>24</td>
    </tr>
    <tr>
      <td>Molnar (P.)</td>
      <td>63, 70</td>
    </tr>
    <tr>
      <td>Monahan (D.)</td>
      <td>79, 80</td>
    </tr>
    <tr>
      <td>Montagner (J.-P.)</td>
      <td>43</td>
    </tr>
    <tr>
      <td>Montgomery (D. C.)</td>
      <td>167</td>
    </tr>
    <tr>
      <td>Monzier (M.)</td>
      <td>33</td>
    </tr>
    <tr>
      <td>Moore (G. F.)</td>
      <td>8, 67, 104</td>
    </tr>
    <tr>
      <td>Moore (J. C.)</td>
      <td>67, 70</td>
    </tr>
    <tr>
      <td>Morales (L.)</td>
      <td>68</td>
    </tr>
    <tr>
      <td>Moran (C. L.)</td>
      <td>31</td>
    </tr>
    <tr>
      <td>Morgan (P. J.)</td>
      <td>37, 244</td>
    </tr>
    <tr>
      <td>Morgan (W. J.)</td>
      <td>23, 58, 61</td>
    </tr>
    <tr>
      <td>Mori (H.)</td>
      <td>61, 233</td>
    </tr>
    <tr>
      <td>Mori (J. J.)</td>
      <td>70</td>
    </tr>
    <tr>
      <td>Mori (Y.)</td>
      <td>50, 66</td>
    </tr>
    <tr>
      <td>Moritz (H.)</td>
      <td>98</td>
    </tr>
    <tr>
      <td>Mosher (S.)</td>
      <td>73</td>
    </tr>
    <tr>
      <td>Mosteller (F.)</td>
      <td>125</td>
    </tr>
    <tr>
      <td>Mountjoy (J. J.)</td>
      <td>76, 77</td>
    </tr>
    <tr>
      <td>Moyer (C. L.)</td>
      <td>20, 57</td>
    </tr>
    <tr>
      <td>Murauchi (S.)</td>
      <td>67</td>
    </tr>
    <tr>
      <td>Murdmaa (I.)</td>
      <td>25</td>
    </tr>
    <tr>
      <td>Murphy (B.)</td>
      <td>37</td>
    </tr>
    <tr>
      <td>Murray (H. W.)</td>
      <td>18, 220</td>
    </tr>
    <tr>
      <td>Myers (J. L.)</td>
      <td>105, 136</td>
    </tr>
    <tr>
      <td>Müller (D.)</td>
      <td>89, 90</td>
    </tr>
    <tr>
      <td>Müller (R. D.)</td>
      <td>28, 39, 56, 98, 101, 198, 209, 253</td>
    </tr>
    <tr>
      <td>Münker (C.)</td>
      <td>246</td>
    </tr>
  </tbody>
</table>

N

<table>
  <tbody>
    <tr>
      <td>Nagahashi (Y.)</td>
      <td>68</td>
    </tr>
    <tr>
      <td>Nagaraja (H. N.)</td>
      <td>154</td>
    </tr>
    <tr>
      <td>Naka (J.)</td>
      <td>63, 209</td>
    </tr>
    <tr>
      <td>Nakakuki (T.)</td>
      <td>253</td>
    </tr>
    <tr>
      <td>Nakamura (M.)</td>
      <td>48</td>
    </tr>
    <tr>
      <td>Nakamura (T.)</td>
      <td>56</td>
    </tr>
    <tr>
      <td>Nakamura (Y.)</td>
      <td>8, 44, 67, 70</td>
    </tr>
    <tr>
      <td>Nakanishi (A.)</td>
      <td>64, 69</td>
    </tr>
    <tr>
      <td>Nakanishi (M.)</td>
      <td>58, 63</td>
    </tr>
    <tr>
      <td>Nakao (A.)</td>
      <td>68</td>
    </tr>
    <tr>
      <td>Nakata (T.)</td>
      <td>43, 44</td>
    </tr>
    <tr>
      <td>Nakazawa (T.)</td>
      <td>43, 65, 209</td>
    </tr>
    <tr>
      <td>Nanayama (F.)</td>
      <td>15</td>
    </tr>
    <tr>
      <td>Naumann (R.)</td>
      <td>68</td>
    </tr>
    <tr>
      <td>Neal (C. R.)</td>
      <td>77</td>
    </tr>
    <tr>
      <td>Negishi (H.)</td>
      <td>44</td>
    </tr>
  </tbody>
</table>
<table><tbody><tr><td>Neira (C.)</td><td>18</td></tr><tr><td>Nelson (C. H.)</td><td>42</td></tr><tr><td>Nelson (S.)</td><td>6</td></tr><tr><td>Neprochnov (J. P.)</td><td>57</td></tr><tr><td>Nesbitt (E. A.)</td><td>77</td></tr><tr><td>Nettles (M.)</td><td>80, 88</td></tr><tr><td>Netzeband (G. L.)</td><td>76, 77, 253</td></tr><tr><td>Netzeband (G.)</td><td>253</td></tr><tr><td>Ngiwsara (L.)</td><td>61, 233</td></tr><tr><td>Nguyen (P. H.)</td><td>45</td></tr><tr><td>Nicol (A.)</td><td>75, 77</td></tr><tr><td>Nielsen (S. G.)</td><td>50</td></tr><tr><td>Ning (J.)</td><td>246</td></tr><tr><td>Nishimura (A.)</td><td>43, 65, 209</td></tr><tr><td>Nishino (M.)</td><td>69</td></tr><tr><td>Nishizawa (A.)</td><td>49, 55, 64, 209</td></tr><tr><td>Niu (Y.)</td><td>31</td></tr><tr><td>No (T.)</td><td>70</td></tr><tr><td>Noguchi (I.)</td><td>67</td></tr><tr><td>Nolasco (R.)</td><td>148</td></tr><tr><td>Nomaki (H.)</td><td>60</td></tr><tr><td>Normark (W. R.)</td><td>7, 58</td></tr><tr><td>Norton (I.)</td><td>25</td></tr><tr><td>NumPy Community</td><td>150, 153</td></tr><tr><td>Nunnally (C. C.)</td><td>30</td></tr></tbody></table>

O

<table><tbody><tr><td>O'Callaghan (S.)</td><td>198</td></tr><tr><td>O'Dor (R.)</td><td>233</td></tr><tr><td>Oakley (A. J.)</td><td>8</td></tr><tr><td>Oda (H.)</td><td>56, 246</td></tr><tr><td>Oda (M.)</td><td>43, 50, 65, 209</td></tr><tr><td>Ogawa (Y.)</td><td>4, 60, 63, 65, 68, 246</td></tr><tr><td>Ogura (K.)</td><td>50</td></tr><tr><td>Oguri (K.)</td><td>29, 56</td></tr><tr><td>Ohara (Y.)</td><td>4, 6, 8, 42, 44, 50, 56-58, 80, 209, 246</td></tr><tr><td>Ohashi (Y.)</td><td>48</td></tr><tr><td>Oikawa (M.)</td><td>49, 209</td></tr><tr><td>Oike (K.)</td><td>44, 45</td></tr><tr><td>Okal (E. A.)</td><td>63, 205</td></tr><tr><td>Okamoto (A.)</td><td>8, 58</td></tr><tr><td>Okamura (Y.)</td><td>49, 209</td></tr><tr><td>Okino (K.)</td><td>4, 6, 34, 42, 44, 57, 222</td></tr><tr><td>Okuyama (M.)</td><td>61, 233</td></tr></tbody></table>

<table><tbody><tr><td>Oliphant (T. E.)</td><td>105, 119, 152, 154</td></tr><tr><td>Oliver (J.)</td><td>63</td></tr><tr><td>Olu (K.)</td><td>233</td></tr><tr><td>Oncken (O.)</td><td>61, 218</td></tr><tr><td>Ondréas (H.)</td><td>233</td></tr><tr><td>Ooka (T.)</td><td>43, 65, 209</td></tr><tr><td>Orozco-Esquivel (T.)</td><td>28</td></tr><tr><td>Osborn (K. J.)</td><td>8</td></tr><tr><td>Oshika (J.)</td><td>66</td></tr><tr><td>Oskin (M.)</td><td>80</td></tr><tr><td>Osses (A.)</td><td>29</td></tr><tr><td>Ota (Y.)</td><td>77</td></tr><tr><td>Otero (P.)</td><td>148</td></tr><tr><td>Otsuji (N.)</td><td>56</td></tr><tr><td>Otsuki (K.)</td><td>209, 253</td></tr><tr><td>Owens (W. B.)</td><td>20</td></tr><tr><td>Ozawa (A.)</td><td>4</td></tr></tbody></table>

P

<table><tbody><tr><td>Pabst (S.)</td><td>190</td></tr><tr><td>Paller (M.)</td><td>80</td></tr><tr><td>Pan (B.)</td><td>4</td></tr><tr><td>Papadopoulos (V. P.)</td><td>233</td></tr><tr><td>Pape (T.)</td><td>6</td></tr><tr><td>Papenberg (C.)</td><td>77</td></tr><tr><td>Parajka (J.)</td><td>9</td></tr><tr><td>Parello (F.)</td><td>32</td></tr><tr><td>Park (J.-O.)</td><td>69</td></tr><tr><td>Parsiegla (N.)</td><td>253</td></tr><tr><td>Passos (A.)</td><td>152</td></tr><tr><td>Pathom-aree (W.)</td><td>61, 233</td></tr><tr><td>Payot (B. D.)</td><td>44</td></tr><tr><td>Pebesma (E.)</td><td>9</td></tr><tr><td>Pecher (I. A.)</td><td>76, 77</td></tr><tr><td>Pedley (K.)</td><td>76, 77</td></tr><tr><td>Pedoja (K.)</td><td>15</td></tr><tr><td>Pedregosa (F.)</td><td>152</td></tr><tr><td>Pelc-Mieczkowska (R.)</td><td>249</td></tr><tr><td>Peliz (A.)</td><td>148</td></tr><tr><td>Pelletier (B.)</td><td>32-34, 246</td></tr><tr><td>Pereira (J.)</td><td>148</td></tr><tr><td>Perepelov (A. B.)</td><td>15</td></tr><tr><td>Peresypkin (V. I.)</td><td>233</td></tr><tr><td>Perez (F.)</td><td>152</td></tr><tr><td>Perez (J. S.)</td><td>43, 44</td></tr><tr><td>Perktold (J.)</td><td>175, 176</td></tr><tr><td>Perlin (N.)</td><td>119</td></tr><tr><td>Perrot (M.)</td><td>152</td></tr></tbody></table>

<table><tbody><tr><td>Petersen (C. J.)</td><td>77</td></tr><tr><td>Peterson (P.)</td><td>105</td></tr><tr><td>Petitat (M.)</td><td>249</td></tr><tr><td>Petterson (M. G.)</td><td>77</td></tr><tr><td>Peyret (M.)</td><td>218</td></tr><tr><td>Peña (R. E.)</td><td>44</td></tr><tr><td>Pflanz (D.)</td><td>15</td></tr><tr><td>Pham (T. T.)</td><td>45</td></tr><tr><td>Pharaoh (A.)</td><td>6</td></tr><tr><td>Philipps-Bussau (K.)</td><td>14</td></tr><tr><td>Phillips (D. A.)</td><td>33</td></tr><tr><td>Pierre (G.)</td><td>33, 81</td></tr><tr><td>Piertney (S. B.)</td><td>30, 31, 60</td></tr><tr><td>Pillet (R.)</td><td>33, 34, 56</td></tr><tr><td>Pineda (V.)</td><td>28, 29</td></tr><tr><td>Pinegina (T. K.)</td><td>15, 17</td></tr><tr><td>Pinheiro (A.)</td><td>81</td></tr><tr><td>Plank (T.)</td><td>70</td></tr><tr><td>Pleijel (F.)</td><td>8</td></tr><tr><td>Pletterbauer (F.)</td><td>81</td></tr><tr><td>Polissar (P. J.)</td><td>70</td></tr><tr><td>Pollnac (R. B.)</td><td>242, 246</td></tr><tr><td>Ponomareva (V. V.)</td><td>15</td></tr><tr><td>Porras (O.)</td><td>24</td></tr><tr><td>Portnyagin (M. V.)</td><td>15, 246</td></tr><tr><td>Power (W.)</td><td>77</td></tr><tr><td>Praeg (D.)</td><td>6</td></tr><tr><td>Prasad (K. V. K. K.)</td><td>116</td></tr><tr><td>Prettenhofer (P.)</td><td>152</td></tr><tr><td>Price (R. C.)</td><td>30</td></tr><tr><td>Priede (I. G.)</td><td>30, 215</td></tr><tr><td>Prince (R. A.)</td><td>29</td></tr><tr><td>Promper (C.)</td><td>249</td></tr><tr><td>Pronin (A. A.)</td><td>28</td></tr><tr><td>Puga-Bernabeu (A.)</td><td>5</td></tr><tr><td>Punongbayan (R. S.)</td><td>44, 45</td></tr><tr><td>Pushharovskij (J. M.)</td><td>57, 61</td></tr><tr><td>Pysklywec (R. N.)</td><td>73</td></tr><tr><td>Python Development Team</td><td>148, 247</td></tr><tr><td>Pérez (M. E.)</td><td>18</td></tr><tr><td>Pérez-Gussinyé (M.)</td><td>7</td></tr></tbody></table>

Q

<table><tbody><tr><td>Qin (Q.)</td><td>57, 233</td></tr><tr><td>Qiu (X.)</td><td>47</td></tr><tr><td>Queaño (K. L.)</td><td>44</td></tr><tr><td>Qurban (M. A.)</td><td>233</td></tr></tbody></table>
R

<table><tr><td>R Development Core Team</td><td>119, 127, 131, 142</td></tr><tr><td>Rabaoui (L.)</td><td>233</td></tr><tr><td>Rabinowitz (H. S.)</td><td>70</td></tr><tr><td>Rackebrandt (N.)</td><td>253</td></tr><tr><td>Raitt (R. W.)</td><td>26</td></tr><tr><td>Ramos (N. T.)</td><td>44</td></tr><tr><td>Ranero (C. R.)</td><td>244</td></tr><tr><td>Rangin (C.)</td><td>22–24</td></tr><tr><td>Rapinski (J.)</td><td>249</td></tr><tr><td>Rapp (R. H.)</td><td>99</td></tr><tr><td>Rathburn (A. E.)</td><td>18</td></tr><tr><td>Ratzov (G.)</td><td>29</td></tr><tr><td>Razjigaeva (N. G.)</td><td>17</td></tr><tr><td>Recy (J.)</td><td>30</td></tr><tr><td>Regalla (C.)</td><td>67, 70</td></tr><tr><td>Regelous (M.)</td><td>30, 31, 205</td></tr><tr><td>Reichel (T.)</td><td>27, 29</td></tr><tr><td>Reichert (C.)</td><td>27</td></tr><tr><td>Reid (W. D. K.)</td><td>12</td></tr><tr><td>Remitti (F.)</td><td>70</td></tr><tr><td>Renkin (M. L.)</td><td>15</td></tr><tr><td>Requena (J.)</td><td>24</td></tr><tr><td>Resentini (A.)</td><td>150</td></tr><tr><td>Resing (J. A.)</td><td>44, 57</td></tr><tr><td>Reutter (K.-J.)</td><td>28</td></tr><tr><td>Rew (R. K.)</td><td>101, 108</td></tr><tr><td>Reyes (A. G.)</td><td>76, 249</td></tr><tr><td>Reyners (M.)</td><td>76, 77</td></tr><tr><td>Rhede (D.)</td><td>68</td></tr><tr><td>Ribeiro (J. M.)</td><td>209</td></tr><tr><td>Richard (C.)</td><td>30</td></tr><tr><td>Richmond (L.)</td><td>253</td></tr><tr><td>Riedel (M. R.)</td><td>58, 70, 205</td></tr><tr><td>Rijsingen (E.) van</td><td>218</td></tr><tr><td>Ripper (I. D.)</td><td>37</td></tr><tr><td>Ritchie (H.)</td><td>30, 60</td></tr><tr><td>Robbins (A. D.)</td><td>110, 112</td></tr><tr><td>Roberts (J. J.)</td><td>119</td></tr><tr><td>Roberts (N. M.)</td><td>105, 116, 150</td></tr><tr><td>Robin (C.)</td><td>32, 33</td></tr><tr><td>Robinson (R.)</td><td>77</td></tr><tr><td>Robison (B. H.)</td><td>8</td></tr><tr><td>Robles (V.)</td><td>24</td></tr><tr><td>Rodriguez (E.)</td><td>80</td></tr><tr><td>Roest (W. R.)</td><td>28, 39, 253</td></tr></table>

<table><tr><td>Romankevich (E. A.)</td><td>233</td></tr><tr><td>Romer (R. H. W.)</td><td>205</td></tr><tr><td>Romero (J.)</td><td>24</td></tr><tr><td>Romsos (C.)</td><td>7</td></tr><tr><td>Rosen (P. A.)</td><td>80</td></tr><tr><td>Rosenbaum (G.)</td><td>35</td></tr><tr><td>Rosgen (D. L.)</td><td>246</td></tr><tr><td>Ross (D. A.)</td><td>25, 104</td></tr><tr><td>Rossum (G.) van</td><td>148, 247</td></tr><tr><td>Rost (D.)</td><td>190</td></tr><tr><td>Roth (L.)</td><td>80</td></tr><tr><td>Rothery (D. A.)</td><td>242</td></tr><tr><td>Rouland (D.)</td><td>56</td></tr><tr><td>Roult (G.)</td><td>56</td></tr><tr><td>Rouse (G. W.)</td><td>8</td></tr><tr><td>Rovere (M.)</td><td>79</td></tr><tr><td>Rowden (A. A.)</td><td>8, 29–31, 61, 76, 77, 215, 218, 233, 246</td></tr><tr><td>Rowe (C. D.)</td><td>70</td></tr><tr><td>Rowe (C.)</td><td>70</td></tr><tr><td>Royden (L. H.)</td><td>44, 49, 57, 58, 77</td></tr><tr><td>Ruangrassamee (A.)</td><td>45</td></tr><tr><td>Rubie (D. C.)</td><td>63, 74, 205</td></tr><tr><td>Rudenko (M. V.)</td><td>6</td></tr><tr><td>Ruff (L. J.)</td><td>16, 61, 74</td></tr><tr><td>Ruhl (H. A.)</td><td>61, 215, 218</td></tr><tr><td>Ruiz (J.)</td><td>7</td></tr><tr><td>Ruiz-Villarreal (M.)</td><td>148</td></tr><tr><td>Rupp (J.)</td><td>14, 43, 218</td></tr><tr><td>Ruppert (N. A.)</td><td>17</td></tr><tr><td>Ryan (H. F.)</td><td>18</td></tr><tr><td>Römer (M.)</td><td>6</td></tr></table>

S

<table><tr><td>Sacchi (M. D.)</td><td>5</td></tr><tr><td>Saelem (N.)</td><td>45</td></tr><tr><td>Saffer (D. M.)</td><td>5, 67, 70</td></tr><tr><td>Saha (P.)</td><td>70</td></tr><tr><td>Sahling (H.)</td><td>6</td></tr><tr><td>Saito (K.)</td><td>60, 246</td></tr><tr><td>Saito (S.)</td><td>67, 70</td></tr><tr><td>Sakairi (N.)</td><td>61, 233</td></tr><tr><td>Salah (M. K.)</td><td>43</td></tr><tr><td>Samowitz (I. R.)</td><td>34, 40</td></tr><tr><td>Sandilands (M.)</td><td>9, 120</td></tr><tr><td>Sandwell (D. T.)</td><td>6–8, 78, 80, 83, 86, 98, 101,</td></tr></table>

102, 209

<table><tr><td>Sangawa (A.)</td><td>43, 44</td></tr><tr><td>Sano (T.)</td><td>64</td></tr><tr><td>Sano (Y.)</td><td>57</td></tr><tr><td>Santos (A.)</td><td>45, 81</td></tr><tr><td>Santos Ferreira (C.) dos</td><td>6</td></tr><tr><td>Sarmiento-Rojas (L. F.)</td><td>28</td></tr><tr><td>Sasaki (K.)</td><td>52</td></tr><tr><td>Sasaki (T.)</td><td>67</td></tr><tr><td>Satake (K.)</td><td>15</td></tr><tr><td>Sato (H.)</td><td>44, 66</td></tr><tr><td>Sato (T.)</td><td>43, 44, 52, 55, 65, 66, 69, 209</td></tr><tr><td>Sattarova (V. V.)</td><td>29</td></tr><tr><td>Savage (H. M.)</td><td>70</td></tr><tr><td>Savostin (L. A.)</td><td>14, 190</td></tr><tr><td>Savov (I. P.)</td><td>20, 66, 190</td></tr><tr><td>Scharroo (R.)</td><td>48, 80, 83, 244, 248</td></tr><tr><td>Schellart (W. P.)</td><td>12, 28, 63</td></tr><tr><td>Schenke (H. W.)</td><td>80, 85, 103, 249, 253</td></tr><tr><td>Scherwath (M.)</td><td>29</td></tr><tr><td>Scheuber (E.)</td><td>28</td></tr><tr><td>Schiano (P.)</td><td>33</td></tr><tr><td>Schiller (J.)</td><td>81</td></tr><tr><td>Schlacher (R.)</td><td>8</td></tr><tr><td>Schloter (P.)</td><td>253</td></tr><tr><td>Schlueter (P.)</td><td>253</td></tr><tr><td>Schluter (P.)</td><td>253</td></tr><tr><td>Schmidt-Effing (R.)</td><td>24</td></tr><tr><td>Schmitt (A. K.)</td><td>15</td></tr><tr><td>Schmitt (T.)</td><td>79</td></tr><tr><td>Schnurle (P.)</td><td>51</td></tr><tr><td>Scholl (D. W.)</td><td>18, 34</td></tr><tr><td>Schubert (G.)</td><td>22, 58</td></tr><tr><td>Schutz (B. E.)</td><td>30</td></tr><tr><td>Sclater (J. G.)</td><td>15</td></tr><tr><td>Scorpio (V.)</td><td>81</td></tr><tr><td>Sdroliasa (M.)</td><td>28, 39, 253</td></tr><tr><td>Seabold (S.)</td><td>175, 176</td></tr><tr><td>Seal (D.)</td><td>80</td></tr><tr><td>Seekings (L. C.)</td><td>4, 9</td></tr><tr><td>Seliverstov (N. I.)</td><td>17, 18</td></tr><tr><td>Sella (G. F.)</td><td>93</td></tr><tr><td>Sen (A.)</td><td>233</td></tr><tr><td>Senda (R.)</td><td>66</td></tr><tr><td>Senger (K.)</td><td>77</td></tr></table>
<table>
  <tbody>
    <tr>
      <td>Senna (H.)</td>
      <td>56, 246</td>
    </tr>
    <tr>
      <td>Seno (T.)</td>
      <td>9, 43, 44, 58, 63, 65</td>
    </tr>
    <tr>
      <td>Serri (G.)</td>
      <td>56, 58</td>
    </tr>
    <tr>
      <td>Seton (M.)</td>
      <td>28, 56</td>
    </tr>
    <tr>
      <td>Seward (D.)</td>
      <td>76, 77</td>
    </tr>
    <tr>
      <td>Shaffer (S.)</td>
      <td>80</td>
    </tr>
    <tr>
      <td>Shank (T. M.)</td>
      <td>61, 215, 218</td>
    </tr>
    <tr>
      <td>Shen (C.)</td>
      <td>37</td>
    </tr>
    <tr>
      <td>Shepard (F. P.)</td>
      <td>6, 246</td>
    </tr>
    <tr>
      <td>Shephard (G.)</td>
      <td>56</td>
    </tr>
    <tr>
      <td>Sherman (K.)</td>
      <td>14</td>
    </tr>
    <tr>
      <td>Shevchenko (A. Y.)</td>
      <td>23, 25</td>
    </tr>
    <tr>
      <td>Shi (X.)</td>
      <td>6, 61, 218</td>
    </tr>
    <tr>
      <td>Shi (Y.)</td>
      <td>34, 45</td>
    </tr>
    <tr>
      <td>Shigeno (K.)</td>
      <td>15</td>
    </tr>
    <tr>
      <td>Shimada (J.)</td>
      <td>80</td>
    </tr>
    <tr>
      <td>Shimamura (H.)</td>
      <td>48</td>
    </tr>
    <tr>
      <td>Shimanaga (M.)</td>
      <td>51, 60, 233</td>
    </tr>
    <tr>
      <td>Shimizu (K.)</td>
      <td>66</td>
    </tr>
    <tr>
      <td>Shimokawa (K.)</td>
      <td>15</td>
    </tr>
    <tr>
      <td>Shinjo (R.)</td>
      <td>50</td>
    </tr>
    <tr>
      <td>Shinmura (T.)</td>
      <td>66</td>
    </tr>
    <tr>
      <td>Shinohara (M.)</td>
      <td>35, 37, 65, 69</td>
    </tr>
    <tr>
      <td>Shiobara (H.)</td>
      <td>43, 65</td>
    </tr>
    <tr>
      <td>Shipboard Scientific Party</td>
      <td>24, 25, 195</td>
    </tr>
    <tr>
      <td>Shipley (N.)</td>
      <td>15</td>
    </tr>
    <tr>
      <td>Shiraiwa (T.)</td>
      <td>16</td>
    </tr>
    <tr>
      <td>Shirao (M.)</td>
      <td>64</td>
    </tr>
    <tr>
      <td>Shishikura (M.)</td>
      <td>48</td>
    </tr>
    <tr>
      <td>Shito (A.)</td>
      <td>43</td>
    </tr>
    <tr>
      <td>Shor (G. G.)</td>
      <td>104</td>
    </tr>
    <tr>
      <td>Shu (Y.)</td>
      <td>50</td>
    </tr>
    <tr>
      <td>Shukuno (H.)</td>
      <td>63, 209</td>
    </tr>
    <tr>
      <td>Shyu (J. B. H.)</td>
      <td>83</td>
    </tr>
    <tr>
      <td>Sibuet (J.-C.)</td>
      <td>42, 43, 47, 51</td>
    </tr>
    <tr>
      <td>Silver (E. A.)</td>
      <td>35</td>
    </tr>
    <tr>
      <td>Sims (K. W. W.)</td>
      <td>246</td>
    </tr>
    <tr>
      <td>Singer (B. S.)</td>
      <td>15</td>
    </tr>
    <tr>
      <td>Singh (R.)</td>
      <td>30</td>
    </tr>
    <tr>
      <td>Sirisansaneeyakul (S.)</td>
      <td>61, 233</td>
    </tr>
    <tr>
      <td>Skyllingstad (E. D.)</td>
      <td>119</td>
    </tr>
    <tr>
      <td>Skøien (J. O.)</td>
      <td>9</td>
    </tr>
    <tr>
      <td>Smirnov (Y. B.)</td>
      <td>17</td>
    </tr>
    <tr>
      <td>Smith (C. R.)</td>
      <td>246</td>
    </tr>
    <tr>
      <td>Smith (I. E. M.)</td>
      <td>30</td>
    </tr>
    <tr>
      <td>Smith (W. H. F.)</td>
      <td>6–8, 48, 78,</td>
    </tr>
  </tbody>
</table>

<table>
  <tbody>
    <tr>
      <td>Snaith (H.)</td>
      <td>79, 80</td>
    </tr>
    <tr>
      <td>Snieder (R. K.)</td>
      <td>63</td>
    </tr>
    <tr>
      <td>Snyder (G. L.)</td>
      <td>18</td>
    </tr>
    <tr>
      <td>Solan (M.)</td>
      <td>215</td>
    </tr>
    <tr>
      <td>Solidum (R.)</td>
      <td>44</td>
    </tr>
    <tr>
      <td>Solomina (O.)</td>
      <td>16</td>
    </tr>
    <tr>
      <td>Soloviev (S. L.)</td>
      <td>45</td>
    </tr>
    <tr>
      <td>Song (A. T. R.)</td>
      <td>83</td>
    </tr>
    <tr>
      <td>Soofi (K.)</td>
      <td>80, 101</td>
    </tr>
    <tr>
      <td>Sorbadere (F.)</td>
      <td>33</td>
    </tr>
    <tr>
      <td>Sosson (M.)</td>
      <td>74</td>
    </tr>
    <tr>
      <td>Spagnolo (M.)</td>
      <td>14</td>
    </tr>
    <tr>
      <td>Spakman (W.)</td>
      <td>42, 50</td>
    </tr>
    <tr>
      <td>Spencer (K.)</td>
      <td>65, 209</td>
    </tr>
    <tr>
      <td>Spina (L.)</td>
      <td>33</td>
    </tr>
    <tr>
      <td>Srivastava (S.)</td>
      <td>116</td>
    </tr>
    <tr>
      <td>Stagpoole (V.)</td>
      <td>80</td>
    </tr>
    <tr>
      <td>Stankov (U.)</td>
      <td>242, 249</td>
    </tr>
    <tr>
      <td>Stehlikova (J.)</td>
      <td>29</td>
    </tr>
    <tr>
      <td>Stein (S.)</td>
      <td>15, 39, 63, 74, 93, 205</td>
    </tr>
    <tr>
      <td>Stephan (J.-F.)</td>
      <td>23</td>
    </tr>
    <tr>
      <td>Stern (L. A.)</td>
      <td>63</td>
    </tr>
    <tr>
      <td>Stern (R. J.)</td>
      <td>63, 209</td>
    </tr>
    <tr>
      <td>Stern (T.)</td>
      <td>77</td>
    </tr>
    <tr>
      <td>Stetson-Lee (T.)</td>
      <td>105, 116, 150</td>
    </tr>
    <tr>
      <td>Stewart (A. L.)</td>
      <td>31, 32, 232</td>
    </tr>
    <tr>
      <td>Stewart (H. A.)</td>
      <td>233</td>
    </tr>
    <tr>
      <td>Stewart (R. B.)</td>
      <td>30</td>
    </tr>
    <tr>
      <td>Stolz (A.)</td>
      <td>37</td>
    </tr>
    <tr>
      <td>Storchack (D. A.)</td>
      <td>80</td>
    </tr>
    <tr>
      <td>Stouthamer (E.)</td>
      <td>81</td>
    </tr>
    <tr>
      <td>Stowell (J.)</td>
      <td>30</td>
    </tr>
    <tr>
      <td>Stransky (B.)</td>
      <td>14</td>
    </tr>
    <tr>
      <td>Strasser (M.)</td>
      <td>68</td>
    </tr>
    <tr>
      <td>Straub (S. M.)</td>
      <td>64</td>
    </tr>
    <tr>
      <td>Strick (R. J. P.)</td>
      <td>246</td>
    </tr>
    <tr>
      <td>Strutz (T.)</td>
      <td>175</td>
    </tr>
    <tr>
      <td>Stuart (J. E.)</td>
      <td>37</td>
    </tr>
    <tr>
      <td>Stutz (M.)</td>
      <td>110</td>
    </tr>
    <tr>
      <td>Suarez (G.)</td>
      <td>17</td>
    </tr>
    <tr>
      <td>Suckro (S.)</td>
      <td>253</td>
    </tr>
    <tr>
      <td>Sudo (M.)</td>
      <td>4, 20, 66</td>
    </tr>
    <tr>
      <td>Suetova (I. A.)</td>
      <td>249</td>
    </tr>
  </tbody>
</table>

<table>
  <tbody>
    <tr>
      <td>Sugioka (H.)</td>
      <td>43</td>
    </tr>
    <tr>
      <td>Sugrobov (V. M.)</td>
      <td>17</td>
    </tr>
    <tr>
      <td>Sun (C.)</td>
      <td>56</td>
    </tr>
    <tr>
      <td>Sun (Y.)</td>
      <td>4, 66</td>
    </tr>
    <tr>
      <td>Sun (Z.)</td>
      <td>47, 57, 233</td>
    </tr>
    <tr>
      <td>Suo (Y.)</td>
      <td>5</td>
    </tr>
    <tr>
      <td>Sutherland (R.)</td>
      <td>72, 74, 77</td>
    </tr>
    <tr>
      <td>Suwarsono</td>
      <td>103</td>
    </tr>
    <tr>
      <td>Suyehiro (K.)</td>
      <td>35, 37</td>
    </tr>
    <tr>
      <td>Suzuki (K.)</td>
      <td>66</td>
    </tr>
    <tr>
      <td>Suzuki (S.)</td>
      <td>44</td>
    </tr>
    <tr>
      <td>Svensson (M.)</td>
      <td>249</td>
    </tr>
    <tr>
      <td>Swan (A. R. H.)</td>
      <td>9, 120</td>
    </tr>
    <tr>
      <td>Symonds (P.)</td>
      <td>73</td>
    </tr>
    <tr>
      <td>Syvitski (J. P. M.)</td>
      <td>9</td>
    </tr>
    <tr>
      <td>Sá (J. P. M.) de</td>
      <td>105</td>
    </tr>
  </tbody>
</table>

T

<table>
  <tbody>
    <tr>
      <td>Tackley (P.)</td>
      <td>34</td>
    </tr>
    <tr>
      <td>Taddeucci (J.)</td>
      <td>33</td>
    </tr>
    <tr>
      <td>Tagami (T.)</td>
      <td>4</td>
    </tr>
    <tr>
      <td>Tagawa (M.)</td>
      <td>253</td>
    </tr>
    <tr>
      <td>Taira (A.)</td>
      <td>70</td>
    </tr>
    <tr>
      <td>Taira (K.)</td>
      <td>8, 59</td>
    </tr>
    <tr>
      <td>Takahashi (M.)</td>
      <td>43, 65, 209</td>
    </tr>
    <tr>
      <td>Takahashi (N.)</td>
      <td>12, 64, 67, 69, 70</td>
    </tr>
    <tr>
      <td>Takayanagi (H.)</td>
      <td>43, 52, 65, 209</td>
    </tr>
    <tr>
      <td>Tal (M.)</td>
      <td>81</td>
    </tr>
    <tr>
      <td>Talandier (J.)</td>
      <td>33</td>
    </tr>
    <tr>
      <td>Talsma (A.)</td>
      <td>56</td>
    </tr>
    <tr>
      <td>Tam (T. A. I.)</td>
      <td>44</td>
    </tr>
    <tr>
      <td>Tamaki (K.)</td>
      <td>63</td>
    </tr>
    <tr>
      <td>Tamura (A.)</td>
      <td>66</td>
    </tr>
    <tr>
      <td>Tamura (C.)</td>
      <td>55</td>
    </tr>
    <tr>
      <td>Tamura (Y.)</td>
      <td>63, 209</td>
    </tr>
    <tr>
      <td>Tan (H.)</td>
      <td>37</td>
    </tr>
    <tr>
      <td>Tang (X.)</td>
      <td>29, 56</td>
    </tr>
    <tr>
      <td>Tani (K.)</td>
      <td>64</td>
    </tr>
    <tr>
      <td>Tani (S.)</td>
      <td>79</td>
    </tr>
    <tr>
      <td>Taniguchi (H.)</td>
      <td>65</td>
    </tr>
    <tr>
      <td>Tappin (D. R.)</td>
      <td>30, 31</td>
    </tr>
    <tr>
      <td>Tapster (S.)</td>
      <td>35</td>
    </tr>
    <tr>
      <td>Tardy (M.)</td>
      <td>22–24</td>
    </tr>
    <tr>
      <td>Tasaka (M.)</td>
      <td>8, 58</td>
    </tr>
    <tr>
      <td>Taylor (B.)</td>
      <td>8, 30, 31, 35, 64, 65, 209</td>
    </tr>
    <tr>
      <td>Taylor (F. W.)</td>
      <td>30, 33</td>
    </tr>
  </tbody>
</table>
<table><tbody><tr><td>Taylor (R. N.)</td><td>63, 209</td></tr><tr><td>Taylor (R.)</td><td>60, 63, 218</td></tr><tr><td>Tejero (R.)</td><td>7</td></tr><tr><td>Teng (T.-L.)</td><td>4, 9</td></tr><tr><td>Tharp (M.)</td><td>6</td></tr><tr><td>Theberge (A. E. J.)</td><td>4, 12, 57</td></tr><tr><td>Theiling (C.)</td><td>246</td></tr><tr><td>Theunissen (T.)</td><td>42, 50, 51</td></tr><tr><td>Thirion (B.)</td><td>152</td></tr><tr><td>Thornburg (T. M.)</td><td>27</td></tr><tr><td>Thovert (J.-F.)</td><td>33, 81</td></tr><tr><td>Tian (J.)</td><td>57, 233</td></tr><tr><td>Tiao (G. C.)</td><td>154, 171</td></tr><tr><td>Tibaldi (A.)</td><td>246</td></tr><tr><td>Tiffin (D. L.)</td><td>37</td></tr><tr><td>Tikhonchuk (E. A.)</td><td>17</td></tr><tr><td>Tikoff (B.)</td><td>105, 116, 150</td></tr><tr><td>Timm (N. H.)</td><td>154</td></tr><tr><td>Tobino (H. J.)</td><td>67, 70</td></tr><tr><td>Toki (T.)</td><td>57</td></tr><tr><td>Tokmakian (R.)</td><td>43</td></tr><tr><td>Tokuyama (H.)</td><td>65</td></tr><tr><td>Tomaszewski (D.)</td><td>249</td></tr><tr><td>Tomiyama (N.)</td><td>55</td></tr><tr><td>Tonarini (S.)</td><td>190</td></tr><tr><td>Tong (H.)</td><td>6</td></tr><tr><td>Torii (Y.)</td><td>205</td></tr><tr><td>Torres (M.)</td><td>6</td></tr><tr><td>Torsvik (T.)</td><td>56</td></tr><tr><td>Tournon (J.)</td><td>22-25</td></tr><tr><td>Townend (J.)</td><td>77</td></tr><tr><td>Toy (V. G.)</td><td>28, 70</td></tr><tr><td>Tregoning (P.)</td><td>37</td></tr><tr><td>Treml (E. A.)</td><td>119</td></tr><tr><td>Trevethan (M.)</td><td>81</td></tr><tr><td>Trimmer (R.)</td><td>6</td></tr><tr><td>Trivedi (P. K.)</td><td>125</td></tr><tr><td>Tryon (M.)</td><td>18</td></tr><tr><td>Tsai (C.-H.)</td><td>51, 101</td></tr><tr><td>Tsai (M.-T.)</td><td>42, 44</td></tr><tr><td>Tsai (Y.-C.)</td><td>44</td></tr><tr><td>Tsuboi (T.)</td><td>56</td></tr><tr><td>Tsuji (T.)</td><td>67, 70</td></tr><tr><td>Tsukanov (N. V.)</td><td>15, 18</td></tr><tr><td>Tsumura (N.)</td><td>44, 66</td></tr><tr><td>Tsunogai (U.)</td><td>57</td></tr><tr><td>Tsuru (T.)</td><td>69</td></tr><tr><td>Tsutsumi (H.)</td><td>43, 44</td></tr></tbody></table>

<table><tbody><tr><td>Tsutsumi (Y.)</td><td>64</td></tr><tr><td>Tu (Y.)</td><td>48</td></tr><tr><td>Tukey (J. W.)</td><td>125, 155, 162</td></tr><tr><td>Turcotte (D. L.)</td><td>22</td></tr><tr><td>Turko (N. N.)</td><td>6</td></tr><tr><td>Turnbull (J.)</td><td>76, 77</td></tr><tr><td>Turner (M.)</td><td>56</td></tr><tr><td>Turnewitsch (R.)</td><td>29</td></tr><tr><td>Turtle (E. P.)</td><td>33</td></tr><tr><td>Tyler (P. A.)</td><td>81</td></tr></tbody></table>

U

<table><tbody><tr><td>Udintsev (G. B.)</td><td>198, 253</td></tr><tr><td>Uenzelmann-Neben (G.)</td><td>253</td></tr><tr><td>Uijttewaal (W. S. J.)</td><td>81</td></tr><tr><td>Ujiie (K.)</td><td>70</td></tr><tr><td>Ujiié (Y.)</td><td>50</td></tr><tr><td>Umland (J.)</td><td>80</td></tr><tr><td>Underwood (M. B.)</td><td>67, 70</td></tr><tr><td>Urabe (T.)</td><td>48</td></tr><tr><td>Uruski (C.)</td><td>72</td></tr><tr><td>Us (M.)</td><td>44</td></tr><tr><td>Usami (K.)</td><td>68</td></tr><tr><td>Ushakov (S. A.)</td><td>60</td></tr><tr><td>Ushakova (L. A.)</td><td>249</td></tr><tr><td>Uto (K.)</td><td>64</td></tr><tr><td>Uyeda (S.)</td><td>58, 66, 209</td></tr></tbody></table>

V

<table><tbody><tr><td>V. (L.)</td><td>253</td></tr><tr><td>Valenzuela (R. W.)</td><td>28</td></tr><tr><td>Vallier (T. L.)</td><td>30</td></tr><tr><td>Van Ark (E. M.)</td><td>39, 74</td></tr><tr><td>Van Wess (J. D.)</td><td>28</td></tr><tr><td>Vanderplas (J.)</td><td>152</td></tr><tr><td>Vanreusel (A.)</td><td>26, 29</td></tr><tr><td>Varoquaux (G.)</td><td>152</td></tr><tr><td>Vermeesch (P.)</td><td>150</td></tr><tr><td>Vervoort (J. D.)</td><td>246</td></tr><tr><td>Vetrov (A. A.)</td><td>233</td></tr><tr><td>Vicenzi (E. P.)</td><td>190</td></tr><tr><td>Viglione (A.)</td><td>9</td></tr><tr><td>Vigneresse (J. L.)</td><td>116</td></tr><tr><td>Villaseor (A.)</td><td>244</td></tr><tr><td>Vils (F.)</td><td>60, 63, 218</td></tr><tr><td>Vita-Finzi (C.)</td><td>28</td></tr><tr><td>Voelker (D.)</td><td>5, 29</td></tr><tr><td>Vogt (P. R.)</td><td>7</td></tr><tr><td>Voight (J. R.)</td><td>233</td></tr></tbody></table>

<table><tbody><tr><td>Von Rosenberg (J.)</td><td>6</td></tr><tr><td>Vu (P. H.)</td><td>45</td></tr><tr><td>Völker (D.)</td><td>27, 29</td></tr></tbody></table>

W

<table><tbody><tr><td>Wada (H.)</td><td>56</td></tr><tr><td>Wadge (G.)</td><td>249</td></tr><tr><td>Wakita (K.)</td><td>67</td></tr><tr><td>Walker (S. L.)</td><td>44, 57</td></tr><tr><td>Wallace (G.)</td><td>6</td></tr><tr><td>Wallace (L. M.)</td><td>75, 77</td></tr><tr><td>Wang (C.)</td><td>56</td></tr><tr><td>Wang (C.-C.)</td><td>51</td></tr><tr><td>Wang (F.)</td><td>4</td></tr><tr><td>Wang (H.)</td><td>77</td></tr><tr><td>Wang (H.-F.)</td><td>48, 101</td></tr><tr><td>Wang (J.)</td><td>37, 47</td></tr><tr><td>Wang (L.)</td><td>233</td></tr><tr><td>Wang (M.)</td><td>57, 233</td></tr><tr><td>Wang (P.)</td><td>5</td></tr><tr><td>Wang (S. M.)</td><td>45</td></tr><tr><td>Wang (T. K.)</td><td>42, 47, 50</td></tr><tr><td>Wang (X.)</td><td>50, 57, 233</td></tr><tr><td>Wang (Y.)</td><td>5</td></tr><tr><td>Wang (Z.)</td><td>41, 43, 47</td></tr><tr><td>Warner (J. C.)</td><td>119</td></tr><tr><td>Warren (B. A.)</td><td>20</td></tr><tr><td>Watts (A. B.)</td><td>7, 58, 78</td></tr><tr><td>Watts (D. G.)</td><td>162</td></tr><tr><td>Watts (K. E.)</td><td>15</td></tr><tr><td>Wdinrebe (W.)</td><td>244</td></tr><tr><td>Weatherall (P.)</td><td>6, 79, 80</td></tr><tr><td>Webb (L. E.)</td><td>35</td></tr><tr><td>Webb (T. J.)</td><td>233</td></tr><tr><td>Webster (J.)</td><td>5</td></tr><tr><td>Wefer (G.)</td><td>68</td></tr><tr><td>Wehbring (R.)</td><td>114</td></tr><tr><td>Wei (Y.)</td><td>233</td></tr><tr><td>Weibenga (W. A.)</td><td>36</td></tr><tr><td>Weinberger (P. J.)</td><td>110</td></tr><tr><td>Weinrebe (W.)</td><td>27, 29, 77, 202</td></tr><tr><td>Weinstock (C. L.)</td><td>232</td></tr><tr><td>Weiss (R.)</td><td>152</td></tr><tr><td>Weissel (J. K.)</td><td>93, 219</td></tr><tr><td>Welber (M.)</td><td>81</td></tr><tr><td>Well (A. D.)</td><td>105, 136</td></tr><tr><td>Wells (R. E.)</td><td>18</td></tr><tr><td>Wen (S.)</td><td>43, 53</td></tr><tr><td>Wendt (J. I.)</td><td>30, 31</td></tr></tbody></table>
<table><tbody><tr><td>Wenzhöfer (F.)</td><td>29, 30, 233</td></tr><tr><td>Werner (M.)</td><td>80</td></tr><tr><td>Werner (R.)</td><td>246</td></tr><tr><td>Wessel (P.)</td><td>48, 78, 80, 83, 86, 101, 103, 104, 244, 248, 249</td></tr><tr><td>Westfall (P.)</td><td>126</td></tr><tr><td>Weston (J.)</td><td>80</td></tr><tr><td>Wheat (C. G.)</td><td>233</td></tr><tr><td>White (T.)</td><td>116</td></tr><tr><td>Whiteway (T.)</td><td>43, 58</td></tr><tr><td>Wickham (H.)</td><td>154, 155</td></tr><tr><td>Widiyantoro (S.)</td><td>5, 8, 15, 17</td></tr><tr><td>Wiedicke (M.)</td><td>27, 29</td></tr><tr><td>Wiens (D. A.)</td><td>56</td></tr><tr><td>Wigley (R.)</td><td>79</td></tr><tr><td>Wiles (G.)</td><td>16</td></tr><tr><td>Willbold (M.)</td><td>60, 63, 218</td></tr><tr><td>Williams (A.)</td><td>8</td></tr><tr><td>Williams (L. J.)</td><td>165</td></tr><tr><td>Wilson (K.)</td><td>77</td></tr><tr><td>Wintersteller (P.)</td><td>6</td></tr><tr><td>Wirth (R.)</td><td>68</td></tr><tr><td>Wobbe (F.)</td><td>48, 80, 83, 244, 248</td></tr><tr><td>Woelki (D.)</td><td>205</td></tr><tr><td>Wolf (F.)</td><td>76, 77</td></tr><tr><td>Wolf (R.)</td><td>24</td></tr><tr><td>Wolff (G. A.)</td><td>81</td></tr><tr><td>Wolfson-Schwehr (M.)</td><td>70</td></tr><tr><td>Wood (R. A.)</td><td>74</td></tr><tr><td>Wood (W. T.)</td><td>77</td></tr><tr><td>Woodhead (J. D.)</td><td>31, 209</td></tr><tr><td>Woodhouse (J. H.)</td><td>80</td></tr><tr><td>Worthington (T. J.)</td><td>30</td></tr><tr><td>Wright (D. J.)</td><td>31, 81</td></tr><tr><td>Wright (J.)</td><td>242</td></tr><tr><td>Wu (F. T.)</td><td>44, 45</td></tr><tr><td>Wu (G.)</td><td>37</td></tr><tr><td>Wu (S.)</td><td>41, 43, 44</td></tr><tr><td>Wu (T.-R.)</td><td>45</td></tr><tr><td>Wu (W.-N.)</td><td>48</td></tr><tr><td>Wu (X.)</td><td>4</td></tr></tbody></table>

<table><tbody><tr><td>Wue (H.-Y.)</td><td>67, 70</td></tr><tr><td>Wörner (G.)</td><td>246</td></tr></tbody></table>

X

<table><tbody><tr><td>Xing (J.)</td><td>4, 66</td></tr><tr><td>Xu (H.)</td><td>57, 233</td></tr><tr><td>Xu (X.)</td><td>50</td></tr><tr><td>Xu (Y.)</td><td>29, 31, 56, 215</td></tr><tr><td>Xuan (S.)</td><td>37</td></tr></tbody></table>

Y

<table><tbody><tr><td>Yagi (Y.)</td><td>68</td></tr><tr><td>Yale (M. M.)</td><td>80, 101</td></tr><tr><td>Yamada (Y.)</td><td>67, 70</td></tr><tr><td>Yamaguchi (T.)</td><td>52</td></tr><tr><td>Yamaki (S.)</td><td>15</td></tr><tr><td>Yamamoto (S.)</td><td>44, 66</td></tr><tr><td>Yamamoto (Y.)</td><td>67, 70</td></tr><tr><td>Yamasaki (M.)</td><td>50</td></tr><tr><td>Yamashiro (T.)</td><td>59</td></tr><tr><td>Yamazaki (T.)</td><td>43, 65, 209</td></tr><tr><td>Yamazaki (Y.)</td><td>18, 68</td></tr><tr><td>Yanagimoto (D.)</td><td>8, 29, 59</td></tr><tr><td>Yancey (P. H.)</td><td>61, 215, 218, 232, 233</td></tr><tr><td>Yang (A.)</td><td>202</td></tr><tr><td>Yang (C.-C.)</td><td>44</td></tr><tr><td>Yang (G.)</td><td>37</td></tr><tr><td>Yang (H.)</td><td>246</td></tr><tr><td>Yang (J.)</td><td>29, 56</td></tr><tr><td>Yang (P.-H.)</td><td>116</td></tr><tr><td>Yang (Y.)</td><td>29, 56</td></tr><tr><td>Yanovskiy (F. A.)</td><td>17</td></tr><tr><td>Yayanos (A. A.)</td><td>31, 44, 211, 233</td></tr><tr><td>Yeh (Y.-C.)</td><td>45, 51</td></tr><tr><td>Yeh (Y.-L.)</td><td>43, 53</td></tr><tr><td>Yogodzinski (G. M.)</td><td>20, 66, 246</td></tr><tr><td>Yokoyama (Y.)</td><td>48</td></tr><tr><td>Yoneshima (S.)</td><td>35, 37</td></tr><tr><td>Yoshida (M.)</td><td>15, 18, 39, 42, 77, 218, 222</td></tr><tr><td>Yoshii (T.)</td><td>67</td></tr></tbody></table>

<table><tbody><tr><td>Yoshikawa (S.)</td><td>34</td></tr><tr><td>Yoshioka (S.)</td><td>205</td></tr><tr><td>Yoshizawa (K.)</td><td>43</td></tr><tr><td>Yu (G.-K.)</td><td>4, 42-44</td></tr><tr><td>Yu (H.)</td><td>29, 56</td></tr><tr><td>Yu (H.-S.)</td><td>47, 246</td></tr><tr><td>Yu (P.)</td><td>5</td></tr><tr><td>Yu (Q.-Y.)</td><td>116</td></tr><tr><td>Yu (S.-B.)</td><td>44, 83</td></tr><tr><td>Yuasa (M.)</td><td>63, 64, 209</td></tr><tr><td>Yuen (D. A.)</td><td>45, 58, 70</td></tr><tr><td>Yulianto (F.)</td><td>103</td></tr><tr><td>Yumul (G. P. J.)</td><td>44</td></tr></tbody></table>

Z

<table><tbody><tr><td>Zack (T.)</td><td>190</td></tr><tr><td>Zahirovic (S.)</td><td>56</td></tr><tr><td>Zayakin (Y. A.)</td><td>17</td></tr><tr><td>Zeileis (A.)</td><td>127</td></tr><tr><td>Zeng (Z.)</td><td>50</td></tr><tr><td>Zenkevich (N. L.)</td><td>6</td></tr><tr><td>Zhan (W.)</td><td>60, 61, 63</td></tr><tr><td>Zhang (B.)</td><td>6</td></tr><tr><td>Zhang (C. L.)</td><td>57, 233</td></tr><tr><td>Zhang (D.)</td><td>116</td></tr><tr><td>Zhang (F.)</td><td>60, 61, 63</td></tr><tr><td>Zhang (G.)</td><td>55, 198</td></tr><tr><td>Zhang (J.)</td><td>47</td></tr><tr><td>Zhang (L.)</td><td>246</td></tr><tr><td>Zhang (X.-H.)</td><td>57, 233</td></tr><tr><td>Zhang (Y.)</td><td>5</td></tr><tr><td>Zhang (Y.-Z.)</td><td>57, 233</td></tr><tr><td>Zhang (Z.)</td><td>55, 198</td></tr><tr><td>Zhao (D. P.)</td><td>7</td></tr><tr><td>Zhao (D.)</td><td>44, 45</td></tr><tr><td>Zhao (M.)</td><td>47</td></tr><tr><td>Zheng (H.-W.)</td><td>34</td></tr><tr><td>Zhou (Z.)</td><td>34, 58, 59, 168</td></tr><tr><td>Zhu (G.)</td><td>34</td></tr><tr><td>Ziebis (W.)</td><td>18</td></tr><tr><td>Zimmermann (K.)</td><td>253</td></tr><tr><td>Zonenshain (L.)</td><td>14, 190</td></tr><tr><td>Zou (L.)</td><td>57, 233</td></tr><tr><td>Zou (Y.)</td><td>6</td></tr></tbody></table>
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