2
However, in 1952, the mine was closed again due to falling
market values for lead and zinc (Wilson, 1960). After inter-
mittent operations by various companies, the mine was
donated to the University of Arizona in 1975 for instruc-
tional and research purposes (Sternberg et al., 1988). The
University of Arizona’s Mining and Geological Engineering
Department is currently developing a new decline, known
as the East Decline, as shown in Figure 4. This decline fea-
tures a round-arched profile with dimensions of 5 m by
5 m. Development began in 2020, and approximately
150 meters of advancement has been completed so far. The
East Decline starts with shallow cover, beginning at about
3 meters at the portal and reaching a current maximum
depth of around 20 meters. The development has been car-
ried out using blasting and a rock bolting machine. Plans
for the site include using it as a testing ground for equip-
ment, machinery, and research conducted by the Mining
and Geological Engineering Department at the University
of Arizona.
Data collection of displacement is being conducted
with multi-point borehole extensometers (MPBXs), and
a remote, automated data acquisition system. The MPBXs
provide displacement data in millimeters (mm) and display
the axial movement of the rock mass, parallel to an MPBX.
For a comprehensive and sufficient analysis and assessment
of ground movement, a total of six MPBXs are used, on
the ribs (walls) and the back (roof) of the opening. In the
opening, two MPBXs are installed on the left rib with 2.5-
meter distance between them, another two are installed on
the right rib with 2.5-meter distance between them, and
finally, the last two are installed on the roof with 2.5 meters
between them as well. In Figure 1, the profile of the open-
ing where three MPBXs are installed at the East decline can
be seen.
BACKGROUND
To achieve a successful visualization, understanding the
goals of the visualization, and understanding what factors
to take into account is crucial. This understanding also
encompasses whether a visualization is necessary and what
type of visualization could be used. Over time statisticians,
mathematicians, and data analysts have brought forward
practices that best utilize and create visualizations of data
and scientific phenomena. However, other unique chal-
lenges occur when data is visualized in 3D. This type of
high-dimensional visualization shares a common challenge:
reducing dimensionality for effective display. In 2004, Dos
Santos &Brodlie (2004) proposed a unified approach for
filtering both multidimensional and multivariate data,
enabling the extraction of data based on constraints related
to their position or value within an n-dimensional window,
as well as the selection of dimensions for display. Kehrer
&Hauser (2013) highlights the significance of visualiza-
tion and visual analysis in the exploration, analysis, and
presentation of scientific data, particularly in disciplines
where data and model scenarios are increasingly complex.
Scientific data often exhibit multifaceted characteristics,
being spatiotemporal, multivariate, multimodal, multirun,
or stemming from simulations. This complexity intro-
duces both opportunities and challenges for visualization
research, particularly when dealing with data of varying
dimensionality. Visualization and interaction techniques
Figure 1. Profile view of the opening with 3 out of 6 MPBXs installed around it
However, in 1952, the mine was closed again due to falling
market values for lead and zinc (Wilson, 1960). After inter-
mittent operations by various companies, the mine was
donated to the University of Arizona in 1975 for instruc-
tional and research purposes (Sternberg et al., 1988). The
University of Arizona’s Mining and Geological Engineering
Department is currently developing a new decline, known
as the East Decline, as shown in Figure 4. This decline fea-
tures a round-arched profile with dimensions of 5 m by
5 m. Development began in 2020, and approximately
150 meters of advancement has been completed so far. The
East Decline starts with shallow cover, beginning at about
3 meters at the portal and reaching a current maximum
depth of around 20 meters. The development has been car-
ried out using blasting and a rock bolting machine. Plans
for the site include using it as a testing ground for equip-
ment, machinery, and research conducted by the Mining
and Geological Engineering Department at the University
of Arizona.
Data collection of displacement is being conducted
with multi-point borehole extensometers (MPBXs), and
a remote, automated data acquisition system. The MPBXs
provide displacement data in millimeters (mm) and display
the axial movement of the rock mass, parallel to an MPBX.
For a comprehensive and sufficient analysis and assessment
of ground movement, a total of six MPBXs are used, on
the ribs (walls) and the back (roof) of the opening. In the
opening, two MPBXs are installed on the left rib with 2.5-
meter distance between them, another two are installed on
the right rib with 2.5-meter distance between them, and
finally, the last two are installed on the roof with 2.5 meters
between them as well. In Figure 1, the profile of the open-
ing where three MPBXs are installed at the East decline can
be seen.
BACKGROUND
To achieve a successful visualization, understanding the
goals of the visualization, and understanding what factors
to take into account is crucial. This understanding also
encompasses whether a visualization is necessary and what
type of visualization could be used. Over time statisticians,
mathematicians, and data analysts have brought forward
practices that best utilize and create visualizations of data
and scientific phenomena. However, other unique chal-
lenges occur when data is visualized in 3D. This type of
high-dimensional visualization shares a common challenge:
reducing dimensionality for effective display. In 2004, Dos
Santos &Brodlie (2004) proposed a unified approach for
filtering both multidimensional and multivariate data,
enabling the extraction of data based on constraints related
to their position or value within an n-dimensional window,
as well as the selection of dimensions for display. Kehrer
&Hauser (2013) highlights the significance of visualiza-
tion and visual analysis in the exploration, analysis, and
presentation of scientific data, particularly in disciplines
where data and model scenarios are increasingly complex.
Scientific data often exhibit multifaceted characteristics,
being spatiotemporal, multivariate, multimodal, multirun,
or stemming from simulations. This complexity intro-
duces both opportunities and challenges for visualization
research, particularly when dealing with data of varying
dimensionality. Visualization and interaction techniques
Figure 1. Profile view of the opening with 3 out of 6 MPBXs installed around it