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Observation of Rockfall in the Thermal Infrared

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Rock Mechanics and Rock Engineering
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Rockfalls pose a significant risk to personnel and equipment in open pit mines, yet there is currently no widely adopted tool for the detection and real-time monitoring of these hazards. This paper explores the use of thermal infrared cameras to observe, detect, and record rockfall events in surface mining operations, with the aim of protecting mine workers from the dangers of rockfalls. The primary objective is to determine the effectiveness of thermal cameras in detecting rockfalls in a range of environmental conditions. A mobile monitoring platform (MMP) was developed and equipped with a variety of long-wave infrared (LWIR) thermal imaging systems, including both scientific and security-grade cameras. Data have since been collected from nine open pit mining operations across the western United States and southern British Columbia, Canada. Six thermal cameras have been deployed and determined effective in detecting rockfall across a temperature range from − 27 °C to 52 °C. Research findings confirm the utility of thermal infrared imagers in rockfall detection throughout the diurnal cycle (24 h/day), enhancing situational awareness for miners and the potential for integration into geotechnical slope monitoring systems. It was also observed that falling blocks smaller than camera pixel resolution can be detected from thermal video due to temperature/emissivity changes resulting from scours, craters, and dust plumes made by the blocks as they descended slopes. This paper demonstrates LWIR thermal cameras' practical applications and limitations for rockfall detection in various geologic and climate conditions, provides recommendations for collecting and analyzing rockfall-related thermal imaging data, and outlines a path forward for the development of rockfall detection algorithms.
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Vol.:(0123456789)
Rock Mechanics and Rock Engineering
https://doi.org/10.1007/s00603-024-04254-1
ORIGINAL PAPER
Observation ofRockfall intheThermal Infrared
EdwardC.Wellman1 · KirkW.Schafer1· ChadP.Williams1· GreatnessH.Ojum1· JuliaJ.Potter1·
LeonardD.Brown1 · BenjaminMeyer1· BradleyJ.Ross1 · JohnKemeny1
Received: 31 May 2024 / Accepted: 24 October 2024
© The Author(s) 2024
Abstract
Rockfalls pose a significant risk to personnel and equipment in open pit mines, yet there is currently no widely adopted
tool for the detection and real-time monitoring of these hazards. This paper explores the use of thermal infrared cameras
to observe, detect, and record rockfall events in surface mining operations, with the aim of protecting mine workers from
the dangers of rockfalls. The primary objective is to determine the effectiveness of thermal cameras in detecting rockfalls
in a range of environmental conditions. A mobile monitoring platform (MMP) was developed and equipped with a variety
of long-wave infrared (LWIR) thermal imaging systems, including both scientific and security-grade cameras. Data have
since been collected from nine open pit mining operations across the western United States and southern British Columbia,
Canada. Six thermal cameras have been deployed and determined effective in detecting rockfall across a temperature range
from −27°C to 52°C. Research findings confirm the utility of thermal infrared imagers in rockfall detection throughout
the diurnal cycle (24h/day), enhancing situational awareness for miners and the potential for integration into geotechnical
slope monitoring systems. It was also observed that falling blocks smaller than camera pixel resolution can be detected from
thermal video due to temperature/emissivity changes resulting from scours, craters, and dust plumes made by the blocks as
they descended slopes. This paper demonstrates LWIR thermal cameras' practical applications and limitations for rockfall
detection in various geologic and climate conditions, provides recommendations for collecting and analyzing rockfall-related
thermal imaging data, and outlines a path forward for the development of rockfall detection algorithms.
Highlights
Long-wave thermal infrared cameras can be utilized for rockfall detection applications under a wide range of conditions.
Thermal infrared cameras have 24-hour monitoring capability and can detect and record rockfall events day or night.
Thermal cameras can detect rockfalls smaller than the cameras pixel resolution by the thermal signature of rockfall events.
Thermal cameras were integrated on a mobile monitoring platform and can be integrated with other slope monitoring
systems.
Keywords Rockfall· Thermal infrared· Slope monitoring· Instrumentation· Cameras
* Edward C. Wellman
ecwellman@arizona.edu
Kirk W. Schafer
kirkschafer@arizona.edu
Chad P. Williams
cpwilliams@arizona.edu
Greatness H. Ojum
ghojum@arizona.edu
Julia J. Potter
juliajpotter@arizona.edu
Leonard D. Brown
ldbrown@arizona.edu
Benjamin Meyer
benjaminmeyer@arizona.edu
Bradley J. Ross
bjr@arizona.edu
John Kemeny
kemeny@arizona.edu
1 The University ofArizona, Tucson, AZ, USA
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
E.C.Wellman et al.
1 Introduction
Rockfall is a known hazard, and research on its characteri-
zation and control has developed out of progress in under-
standing landslide movements (Turner and Schuster 2012).
In open-pit mining, mitigations for rockfall are a standard of
practice in bench design based on work by (Ritchie 1963),
further developed by (Call and Savely 1990; Ryan and Pryor
2000). However, rockfall and other slope hazards in the open
pit mining environment continue to be critical risks for the
mining industry (Read and Stacey 2009). As the indus-
try evolves and the focus on slope optimization increases,
managing these risks will require innovative monitoring
and slope management strategies (Ross 2017). The mining
industry has made great progress in monitoring solutions,
measuring slope movement at rates of millimeters per year
to tens of centimeters per day (Sharon and Eberhardt 2020).
However, monitoring for rapid movement due to rockfall has
not seen similar advancements. Currently, there is no widely
adopted tool for real-time detection, tracking, and alarming
for rockfalls, and rockfall mitigation is largely limited to
‘manual’ methods, such as trigger lines and human spot-
ters. While important progress has been made in analyzing
rockfall sources and deposition zones with unmanned aerial
vehicle (UAV) and light detection and ranging (LiDAR) sys-
tems, this requires significant post-processing time (Walton
etal. 2023; Graber and Santi 2023; Wang etal. 2022). Two
Doppler radar systems are in commercial development for
rockfall detection, but neither has been widely adopted in the
US mining industry, and there remains a notable absence of
widely adopted tools in this domain (Viviani etal. 2020).
The Geotechnical Center of Excellence (GCE) at the Uni-
versity of Arizona has identified thermal infrared cameras as
a potential solution for detecting, tracking, and alarming for
rockfall in open pit mining environments. Thermal cameras
are particularly valuable as a monitoring tool due to their
ability to provide situational awareness throughout the diur-
nal cycle and in various lighting and atmospheric conditions
where visual imagers have reduced effectiveness. This allows
for near-continuous real-time monitoring, which is crucial
for the detection of potential hazardous movements and the
initiation of preventative measures to protect both personnel
and equipment. Thermal cameras can detect rockfall due to
the unique thermal signatures of rocks that dislodge from
an open pit slope when compared with those of the in-situ
rocks in the slope face. Rocks or dust particles on the surface
of a slope are exposed to different amounts of sunlight and
atmospheric elements than unexposed rocks. This variation
in thermal properties enables the use of thermal imaging
to detect falling rocks, which appear as thermal anomalies
when contrasted against the background of the stable slope.
Furthermore, as rocks travel down-slope, a thermal trail is
created by the rock impacting and scouring the slope’s sur-
face, which can be observed in thermal recordings.
The GCE is actively researching the application of
thermal imagers as rockfall monitoring tools through two
NIOSH-funded initiatives: the Application Testing Project
(Phase 1) and the ongoing Automated Rockfall Recognition
Project (Phase 2). This paper discusses the findings from
Phase 1, which gages the effectiveness and reliability of
thermal imaging cameras to detect and document rockfall
events in surface mining operations, thereby enhancing the
safety of mine workers from rockfall hazards. Specific goals
of this work include:
Characterization and alignment of necessary parameters
for rockfall detection with capabilities of existing com-
mercial off-the-shelf (COTS) thermal imaging systems.
Implementation of a testbed Mobile Monitoring Platform
(MMP) to enable systematic evaluation of six representa-
tive COTS cameras with mine operators.
Development of a process model to inform detection and
identification of rockfalls from thermal imaging videos
and understand the feasibility of human monitoring with
thermal imaging systems.
Evaluation of the MMP prototype system by conducting
a series of tests under a variety of realistic mining condi-
tions at nine open-pit mines across the western US and
Canada.
An essential aim of this project is to identify and develop
cost-effective, readily available rockfall monitoring systems
that can either complement existing slope monitoring frame-
works or be independently installed for regular mine surveil-
lance. The efforts completed thus far have confirmed the
viability of using long-wave infrared (LWIR) thermal video
cameras for detecting rockfalls and established a foundation
for developing an automated rockfall detection system. A
review of previous work is provided and the methodology
for thermal rockfall detection is explained in detail, includ-
ing documentation of key parameters that influence the
effectiveness of thermal rockfall detection. The development
of a mobile monitoring platform (MMP) and the criteria for
thermal camera selection are described. Subsequent sections
detail MMP deployments and associated testing, and sum-
marize limitations and best practices for rockfall detection.
Recommendations for operational implementation on mine
sites and future research directions are also outlined.
2 Related Work
Geohazards in the open pit mining environment have long
been identified as a critical risk for the US mining indus-
try (Girard etal. 1998; Girard 2001) and are expected to
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Observation ofRockfall intheThermal Infrared
remain so (Ross 2017). As a notable example, a major
mining landslide occurred at the Bingham Canyon Mine
in Utah in April 2013. The Manefay Slide was one of the
largest recorded mass slide movements in history, with
approximately 145 million tons of rock causing damage
to mining infrastructure, roads, and equipment. Signifi-
cantly, no lives were lost, and no injuries occurred, due
to proactive planning for the potential slide at the mine,
including implementing robust geotechnical monitoring
protocols and a trigger action response plan (TARP) before
the failure (Ross 2017). The Manefay Slide occurred at
night and could not be observed or recorded by stand-
ard video, which limited the initial understanding of the
failure mechanics. This event highlighted the need for
improved situational awareness, particularly during low-
light conditions. Furthermore, a maturing mining industry,
with a limited number of new projects coming online, is
driving both slope optimization projects (Mahayasa etal.
2020) and mine expansions with multiple vertical coinci-
dent mining cuts (Williams etal. 2020), projects which
may increase the likelihood of both mining-induced rock-
fall and slope failures. Studies also indicate a correlation
between increasing rockfall frequency and major slope
failures (Rosser etal. 2007; Schafer etal. 2023). Technolo-
gies that enable intensive slope monitoring and accurate
detection of rockfall events would greatly mitigate these
risks, enhancing miner safety.
2.1 Technologies forGeohazard Monitoring
The mining industry has made significant improvements in
slope deformation monitoring (Vaziri etal. 2010; Pieraccini
and Miccinesi 2019) using interferometric radars, prisms
measured by robotic total stations, and GPS. These systems
have proven effective in predicting the timing of slope col-
lapses (Rose and Hungr 2007). For example, Williams etal.
(2021) used satellite-based Interferometric Synthetic Aper-
ture Radar (InSAR) to perform a post-assessment of slope
movements using five years of data preceding the Manefay
Slide at Bingham Canyon Mine. A long trend of progres-
sive movement was detected at least two years before the
collapse, suggesting that satellite InSAR monitoring may
be beneficial to supplement ground-based systems for early
detection and warning of major slide events.
Despite great progress in slope monitoring, limited
methods exist to detect rockfalls in real-time in the min-
ing environment. Currently, it is possible to monitor rock-
fall sources and deposition zones with LiDAR and pho-
togrammetry systems. For example, a five-camera array
was used to compute a local photogrammetry model of
a slope at daily intervals; changes in the slope geometry
due to rockfall were correlated with environmental fac-
tors such as periods of precipitation (Walton etal. 2023).
Although useful for identifying rockfall trends, LiDAR
and photogrammetric systems require substantial calibra-
tion and post-processing, have limited fidelity to detect
smaller areas of apparent change without additional inter-
ventions, and are generally not real-time.
Ground-based Doppler radar systems have been inves-
tigated to enable real-time rockfall monitoring in both
mining and non-mining applications (Carlà etal. 2024;
Schneider etal. 2023; Viviani etal. 2020). For example,
Doppler radar was recently used in a transportation sce-
nario to alarm for rocks falling down a slope, which would
automatically trigger a stoplight and prevent traffic from
entering a hazard area (Schneider etal. 2023). The Dop-
pler radar setup was shown to have reasonable sensitivity
that varied by distance; the system could detect rocks with
volumes larger than 0.1 m3 at 100m and volumes larger
than 1.0 m3 at 1km, respectively. Similarly, the RockS-
pot system is an interferometric Doppler radar system and
recent commercial product designed to track fast-moving
rockslides and avalanches in a variety of non-mining
applications (Viviani etal. 2020). Although Doppler radar
monitoring systems have tremendous potential to detect
rockfalls, they have several drawbacks, which include: (1)
detection capabilities that are substantially reduced when
ground surfaces are not regular and bare (Carlà etal.
2024); (2) limitations on the minimum rock volume that
can be tracked at greater distances (Schneider etal. 2023);
and (3) the requirement for minimum fall velocities of
at least several meters per second to be detected (Meier
etal. 2017). Additionally, Doppler radar systems incur
substantial costs, and although there are Doppler radar
systems in commercial development for rockfall detection,
these systems have not yet been widely adopted by the US
mining industry.
Seismic sensors have also been demonstrated in real-time
rockfall monitoring applications. Using a series of ground
sensors, Collins etal. (2014) could distinguish rockfall
events from other types of signals due to human-produced
noise, such as the passage of vehicles and trains. The system
was used in a non-mining application to alarm for rockfalls
near railroad tracks. Although the system could reliably
detect rockfalls with a low false positive rate, it required sub-
stantial setup, including 12 sensors buried in a trench at the
base of a steep slope, and could only detect rockfall events in
the immediate area after the conclusion of the fall event. The
post-facto nature of the alarming and limited detection area
reduces the utility of such sensors for rockfall monitoring in
mining applications.
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E.C.Wellman et al.
2.2 Feasibility ofaThermal Imaging Platform
Infrared cameras and thermal imaging systems have been
explored in mining and other geohazard applications since
the 1970s. In early work, Watson (1975) demonstrated
the potential of thermal imaging for measuring geologic
properties in applications that included geothermal map-
ping and thermal inertia mapping. A computational model
was developed to compute mean diurnal temperature and
day–night difference, independent of the thermal inertia of
the ground, and compute geothermal flux and discriminate
geologic materials. From this data, the authors could predict
optimum times in the diurnal cycle for data acquisition and
indicated use cases that included monitoring effusive volcan-
ism, detecting fractures, and discriminating geologic materi-
als. The US Bureau of Mines further identified the potential
for thermal imaging systems to detect loose rock, misfires,
shorted power cables, overheated equipment bearings, and
combustion (Stateham 1976). Nonetheless, limitations in
cost, resolution, portability, image quality, and the lack of
commercially available systems hindered further research
and widespread adoption in the mining industry.
Technological advancements have mitigated these limita-
tions and the potential for thermal infrared imaging systems
for rock mechanics applications has since been demonstrated
(Guerin etal. 2019; Mineo and Pappalardo 2021; Zhao etal.
2021). For example, Guerin etal. (2019) investigated the
application of thermal infrared imaging to assess the stabil-
ity of two partially detached rock sheets on El Capitan’s
nearly 3000-foot-tall cliff face in Yosemite Valley, Califor-
nia. El Capitan is a steep, granitic exfoliating face with per-
sistent cliff-parallel exfoliation joints that result in the for-
mation of large granite sheets that remain partially attached
to the face by rock bridges. The presence of rock bridges or
the areas of intact rock where the cliff-parallel cracks have
not yet propagated play a crucial role in the stability of these
“sheets.” By analyzing the thermal signatures of the rock
sheets, they were able to identify where rock bridges were
present due to differential cooling and heating between the
unattached portion of the sheet, which are cooler due to air
circulation, and the portions of the sheet that are attached,
which are warmer. Due to the orientation of the rock face,
the optimal time to identify these rock bridges was on a
fall evening between 5:45pm and 9:40pm. Similarly, Zhao
etal. (2021) used infrared imagers mounted in tandem with
visible light cameras on a UAV to identify ground fissures
on a mine site. The authors were able to characterize fissure
detection errors (at 1.8% and 6.5% for average error in length
and maximum width, respectively) and identify optimal day
times for detection (between 3:00 am and 5:00 am). Such
works suggest the potential of COTS infrared cameras for
detecting and reliably tracking rockfalls in real time.
3 Thermal Imaging forRockfall Detection
Thermal imagers or infrared cameras have been used in a
variety of applications, including building inspections, medi-
cal diagnostics, industrial maintenance, education, and secu-
rity. Thermal cameras can depict temperature variations due
to the fact that every object emits infrared (IR) radiation
(heat). The amount of radiation emitted by an object var-
ies depending on its material properties. Thermal imagers
work by detecting and measuring the IR radiation emitted
by objects and converting this information to a false-color
visual image (Vollmer and Möllmann 2018). Different colors
or shades in the resulting image correspond to different tem-
peratures. This allows for visualization of the heat emitted
by various objects in the scene and helps in identifying vari-
ations in temperature that may signify different conditions
or anomalies.
Figure1 shows a comparison between a visible light
image (a) and two false-color thermal images (b, c) of an
office scene including desk, laptop, monitor and mini-fridge,
and in the lower right frame a heating conduit pipe extending
from the wall. These images were taken with the 12-meg-
apixel rear camera sensor of a Galaxy S23 Ultra cellular
phone and cropped to match view of thermal image (center)
grayscale thermal image taken from a FLIR FC-632-ID cam-
era from the same vantage point (right) false-color thermal
image of the same scene using the Ironbow color palette.
The grayscale palette is a white-hot scale, and the Ironbow
scaled image has yellow as hot scaling to cooler colors in
blue. Ironbow is a false-color palette developed by FLIR.
The hot yellow and red colors are intended to reference the
hot metal elements on a stove, while the cool, blue colors
indicate cold temperatures similar to ice and water (ITC
2020). Note the thermal images’ visibility of the refrigera-
tor’s heater coils (lower right) and the laptop power supply
(just left of center).
3.1 Thermal Infrared intheEM Spectrum
The infrared region of the electromagnetic (EM) spectrum
spans wavelengths between 740nm and 300μm and is com-
monly broken up into near-infrared (0.7–1.4μm), short-wave
infrared (1.4–3μm), medium-wave infrared (3–8μm), and
long-wave infrared (8–15μm) (Peckham et. al 2015). Fig-
ure2 shows the electromagnetic spectrum with an expanded
view of the infrared region. Short-wave infrared (SWIR)
cameras primarily capture light that is reflected by an object,
whereas mid-wave (MWIR) and long-wave (LWIR) infra-
red cameras detect the heat emitted by objects (Vollmer and
Möllmann 2018). Most commercially available thermal
imagers operate in the MWIR and LWIR bands, which rely
entirely on the IR radiation emitted by objects and, therefore,
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Observation ofRockfall intheThermal Infrared
require no external illumination (ITC 2020). Imagers that
operate in the MWIR typically require the use of cooled
bolometers. These cooled MWIR imagers are highly accu-
rate and have many different applications, including gas
detection; however, they are typically more expensive than
uncooled LWIR cameras. Most LWIR imagers operate at
ambient temperatures and are more suitable for extended
use in harsh environments.
3.2 Rockfall Detection
Thermal cameras can detect rockfall by capturing the ther-
mal signatures of blocks of rock as they detach and move.
When a rock dislodges, it exposes rock not previously illu-
minated by sunlight or in contact with the atmosphere. This
results in a difference in temperature between the dislodged
rock and the newly exposed insitu rock at the slope face.
Thermal cameras can detect this temperature variation,
which enables differentiation between the falling rock as a
thermal anomaly when contrasted against the background
of the stable slope. It was also observed that as rocks detach
and travel down-slope, falling blocks leave a visible thermal
trail that results from the rock impacting and scouring the
surface. Additionally, dust clouds and plumes are visible
after rock impacts. This allows for the detection of fall-
ing rocks that are smaller than the camera pixel resolution
from thermal video due to temperature/emissivity changes
resulting from scours and craters made by the blocks as they
descend slopes. Figure3 shows a schematic of this mecha-
nism. An example of rockfall and associated thermal trail is
depicted in Fig.4.
4 Development ofaThermal Imaging
Monitoring Platform
A mobile monitoring platform (MMP) was designed and
constructed in collaboration with IDS GeoRadar to evalu-
ate the effectiveness of detecting rockfall from thermal
video. The MMP was developed as a means of transport-
ing the cameras and associated equipment to various mine
and project sites, managing thermal video recording and
archiving, and monitoring camera performance and record-
ing targets. Pictures of the MMP are shown in Fig.5. The
system includes the mechanical, power, communications,
computing, and recording components necessary for
thermal camera deployment and evaluation. Key design
considerations included the need for long-term deploy-
ment without access to line power or communications,
Fig. 1 Comparison of visible and false-color thermal images a visual,
b grayscale, c Ironbow
Fig. 2 Infrared (IR) and adjacent spectral regions with expanded view
of thermal IR, From Vollmer & Möllmann, 2018
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E.C.Wellman et al.
durability of the system given the harsh conditions pre-
sent in mining environments, and the need for secure and
consistent mounting of the test equipment. The hardware
components of the MMP are detailed in Table1.
4.1 Camera Selection
Six thermal infrared cameras were acquired and tested as
part of this project. Table2 lists general camera specifica-
tions, including resolution and field of view (FOV).
Experience with the cameras summarized in Table2 has
shown that camera resolution and FOV are the most impor-
tant considerations when selecting a thermal camera for
rockfall monitoring applications as these are not changeable
after purchase. The appropriate resolution and FOV should
be determined on a site-by-site basis. Controlling factors
include the distance from the planned camera installment
location to the target slope, the extent of the area to be moni-
tored for rockfall, and the minimum size of movement to be
detected. The following subsections summarize additional
considerations when selecting a thermal camera for rockfall
detection, including the differences between security- and
scientific-grade cameras and cameras with cooled micro
bolometers versus uncooled micro bolometers.
4.1.1 Security‑ Versus Scientific‑Grade Cameras
The project evaluated three security-grade cameras and
two scientific cameras. Security-grade cameras depict
thermal differences but do not provide precise radiomet-
ric data or exact temperature measurements. In contrast,
Fig. 3 Schematic showing how rocks commonly impact the slope
between detachment and final resting location
Fig. 4 Rockfall is illustrated over four frames. The rockfall is initi-
ated from the active excavation face (a). The rock falling on the bench
below (b), The rock fragments into two pieces as it continues falling
(c). The rock fragments are at rest at the end of their travel paths on a
talus pile 4 benches below (d)
Fig. 5 Mobile Monitoring Platform (MMP) with four thermal cam-
eras deployed (a) and the interior of the MMP (b)
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Observation ofRockfall intheThermal Infrared
scientific-grade thermal cameras provide radiometric data,
enabling temperature measurements for every pixel. Rock-
falls were detected using recordings from both types of
cameras. Most rockfall logging was conducted using footage
from Camera 2, which is a security-grade camera due to its
wide field of view. However, rockfalls were also confirmed
using footage from other cameras whenever possible. Fur-
ther research is needed to determine if slight temperature
changes may indicate rockfalls not previously identified
visually in security-grade footage.
4.1.2 Cooled versusUncooled Microbolometers
The main imaging sensor in a thermal camera is a microbo-
lometer, which is similar to a charge-coupled device (CCD)
in an optical imaging device. The microbolometer meas-
ures electromagnetic radiation, particularly in the infrared
and millimeter-wave regions of the spectrum. It operates
based on the principle that the absorption of radiation by a
material causes a change in its temperature, which is then
measured as a change in its electrical resistance (Vollmer
and Möllmann 2018). While bolometers focus on detecting
thermal changes caused by radiation absorption, CCDs cap-
ture and convert optical photons into electrical signals, pri-
marily used in visible light and some parts of the ultraviolet
and near-infrared spectra. MWIR cameras require a cooled
microbolometer, whereas LWIR cameras typically operate
with uncooled microbolometers. All cameras utilized for
this research operate within LWIR, between 12 and 14μm.
Limited testing of MWIR cameras (between 3 and 5μm)
was also conducted. For example, at the Mine 4 deploy-
ment site (Sect.5), data from a cooled microbolometer
MWIR camera (PT602 CZ) owned by the mine operator was
compared with the uncooled LWIR cameras on board the
MMP. It was found that both the cooled MWIR camera and
uncooled LWIR cameras could detect temperature changes
due to rockfall at the distances tested (between 2,000 and
2,500m). However, cooled MWIR cameras must be periodi-
cally pointed at the sky to reset the microbolometer, which
reduces their effectiveness as a monitoring device. Based on
these findings, LWIR cameras were found to be preferable
due to their lower price and improved durability in compari-
son with MWIR.
4.2 Camera / Imager Setup
This subsection examines key parameters that influence the
effectiveness of thermal imaging for rockfall detection and
considerations for post-processing. Phase 1 required exten-
sive recording and archiving of thermal video as rockfalls
were identified from recorded video by researchers to assess
the detection capabilities. Efforts are underway to automate
this process, reducing the need to record video for alarming
and related real-time applications. However, video archiving
has applications for future research in this project and others.
Guidelines to configure parameters for archiving and real-
time automation applications are outlined below.
Table 1 Components on the mobile monitoring platform (MMP)
Component Notes
Trailer Manufactured by Aluminum Trailer Company Trailers and modified for monitoring applications by IDS
GeoRadar
Battery array 8 × deep cycle 12-V marine batteries (Full River Battery Co) enclosed in Quickbox battery housing
Solar panels 6 × Q.PEAK DUO-G5 315–330 solar panels – three permanent affixed to the roof of the MMP; three stored
inside the MMP for deployment as needed. Panels provide a maximum of 330 watts of power at 34-V into
the battery array
Charge controller Outback Power Flex Max 80 Charge Controller; regulates the voltage and current during charging and prevents
backflow of current to the solar panels when not producing electricity
Battery charger DeltaQ IC 1200W charger; regulates charging under line power
Cellular Sierra Wireless RV50 modem and Proxicast antenna
Ethernet Wired ethernet port available for connection to deployment site networks
Video Management FLIR Meridian Net Video Recorder (NVR) was used to manage video recording
Control System FLEXSQ5 Supervisory Control and Data Acquisition (SCADA); records system power status, stores tempera-
ture, precipitation, and wind data
Solar Irradiance IMT Solar irradiance sensor Si-V-10TC; measures the power per unit area received from the sun in the form of
electromagnetic radiation
Temperature Two thermistors, one external and one internal to the MMP; measures temperature
Rain Gage Rainwise Tipping Bucket Rain Gage 111-PVMet 500; measures precipitation
Anemometer TheisClima Anemometer; measures wind speed and direction
Thermal cameras on board the MMP are summarized in Table2
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E.C.Wellman et al.
4.2.1 Focus, Range, andDistance
Focus, range, and distance are basic operating parameters for
most visual cameras. For analysis and viewing, the images
should be in focus, have the correct settings on the tempera-
ture range for the camera, and be taken at an appropriate
distance to the slope. Focus, range, and distance are fixed
and cannot be changed once a video is made. In addition to
the field of view, post-processing video data cannot change
these parameters. In our setup (Table2), the science grade
camera and the PTZ camera have software tools to adjust
focus. The security cameras have fixed focal lengths and
cannot be adjusted. The temperature range and the scales
for the security cameras are automatically set by the camera
software and cannot be manually adjusted. The distance to
the slope cannot be changed by zooming. With the exception
of the PTZ camera zoom controls, the resultant pixel dimen-
sion or resolution cannot be increased by post-processing.
4.2.2 Video Frame Rate
Frame rate is one of the most important camera settings to
be aware of when deploying thermal cameras for rockfall
detection. Frame rate affects opportunities to see objects in
motion. Moving objects are more likely to be detected at
higher frame rates; at lower framerates, the likelihood of
detecting event initiation decreases, and some minor rockfall
events that occur over short periods may not be detected at
all. The frame rate also affects the ability to track falling
rocks as they move down-slope because at lower frame rates,
it becomes significantly more difficult to identify whether a
moving object in one frame is the same object that was mov-
ing in a previous frame. The cameras tested during project
Phase 1 can collect video at up to 30 frames per second (fps).
Thermal video was recorded at between 1 and 30 fps, with a
typical framerate of 15 fps during Phase 1. Subsequent data
analysis and automation efforts show that detection results
improve at higher frame rates.
4.2.3 Effective Resolution
Effective resolution is determined by a combination of the
camera resolution, camera field of view, and the range or
distance between the camera and the slope. The effective res-
olution affects the ability to detect rocks of varying sizes and
temperatures. Each pixel's output (e.g., brightness, color) is
determined by the average thermal emissivity of the entire
area imaged by the pixel. This means that a lower-resolution
camera will have each pixel representing a larger target area
than a higher-resolution camera with a similar field of view.
For example, a 14° field of view (focal length = 29mm) at a
distance of 500m will result in a pixel spot size of 300mm
for a 320 × 240 resolution camera and a pixel size of 200mm
for a 640 × 480 resolution camera. Target effective resolu-
tion should be determined by the size and the type of rocks
or falling objects to be detected. Figure6 shows how pixel
resolution varies with distance to the pit slope for the cam-
eras trialed for this project.
4.2.4 Video Compression
Video streams from the cameras are encoded using the
H.264 or MPEG-4 format. These are video compression
standards defined by the Moving Picture Experts Group
(MPEG) and documented under formal standards (ISO/IEC
2019). Video compression reduces the file size and band-
width required to transmit video data by removing redun-
dant visual information either spatially (within a frame) or
temporally (across successive frames). Compression impacts
the effectiveness of detecting rockfalls. Higher compression
rates may blur subtle temperature differences and/or smooth
out temperature changes over short periods of time. Lower
compression rates make it easier to accurately identify rock-
falls' thermal signatures. The level of compression, which
impacts file size and consequently bandwidth and/or storage
requirements, must be balanced with the desired accuracy.
4.2.5 Span andContrast
Each camera has an ultimate temperature range, or span,
within which temperature differentials can be measured.
Maximum and minimum temperatures within the camera's
field of view are matched to the high and low points on a
user-selected color palette, and the resulting thermal record-
ings are based on this mapping. This mapping is typically
adjusted automatically within the user-defined range by the
Table 2 Specifications of
thermal infrared cameras trialed
for rockfall detection
Camera ID Imager Type Resolution (H × V) Field of view (H × V)
1 FLIR A400 Scientific 320 × 240 14° × 10°
2 FLIR FC-632-ID Security 640 × 480 32° × 26°
3 AXIS Q1941-E Security 320 × 240 6.2° × 4.6°
4T AXIS Q8752-E Security /Pan-
Tilt-Zoom
640 × 480 17° × 12.8°
4V 1920 × 1080 58.5° × 35°
5 FLIR A700 Scientific 640 × 480 42° × 32°
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Observation ofRockfall intheThermal Infrared
thermal camera. The scientific cameras tested allow the
user to narrow these ranges as appropriate by project site
and subject matter. Caution should be used when select-
ing a temperature range, particularly for security cameras
as data outside that range are lost and cannot be restored
after the fact unless radiometric data are available for post-
processing. The automatically defined temperature range
settings for all cameras trialed were generally acceptable
for rockfall detection.
During setup, thermal cameras must be optimized for
image contrast based on the temperatures of features visible
in the frame. For example, the sky can have temperatures
ranging from −20 to −40° C, which is much colder than
the typical open pit mine slope. Figure7a shows an image of
an open-pit mine site in which one-third of the camera view
was pointed at the sky. The overall slope height is 300m
and the bench increment is 15m. Note that the slope appears
whitewashed in the thermal infrared due to the wide contrast
between the temperature of the sky and the rock that makes
up the slope. Figure7b shows the same slope with improved
contrast after pointing the camera down to remove the sky
from the frame. This adjustment increases the base tempera-
ture by 0–10° C and narrows the temperature span. Thermal
contrast can be adjusted in post-processing by rescaling the
data although this requires significant effort.
4.3 Thermal Video Recording andRockfall
Detection Methodology
The MMP is equipped with a FLIR Meridian Net Video
Recorder (NVR), which serves as the system’s video
management system. The NVR allows for video recording
from multiple cameras simultaneously and is the primary
mechanism by which camera and video archiving settings
are controlled. Rockfall identification can be performed in
real-time using the NVR’s monitoring feature during deploy-
ments or from archived thermal video recordings. The lat-
ter allows for more efficient rockfall/event logging as the
user can scan through video at a speed faster than in real
time. The majority of rockfall identification completed for
this project was done by scanning through recorded video
in Adobe Premiere Pro, which allows for identifying and
exporting the locations of initiation and end frames for a
rockfall event. Rockfalls were identified by project engi-
neers and undergraduate researchers and stored in a series of
spreadsheets. Data collected for each rockfall include date,
initiation time, end time, distance traveled (estimated based
on the bench increment), number of pixels impacted by the
fall, and pixel coordinates of the initiation and resting loca-
tions. The following workflow was developed for identifying
rockfalls in the thermal video recordings:
Fig. 6 Total effective resolution versus distance between camera and
slope (Range)
Fig. 7 Comparison in contrast of two thermal images with (a) and
without, (b) sky view
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E.C.Wellman et al.
Prior to logging, a reference image is taken from the
Camera 2 footage and stored in the log file to provide a
spatial frame of reference. The image is labeled to note
working levels and select benches, as well as any other
notable mine construct in the field of view.
Design bench height and approximate slope angle are
noted in the log sheet, as well as any other additional
information that may be useful in locating interpreted
rock falls within the frame.
The reviewer watches the videos at a playback speed
determined based on the resolution, environmental con-
ditions, and other factors influencing the visibility of
rockfall. Experience has shown that moving the time-
slide forwards and backward at a rate of 20–40min of
video time per 2–4 actual seconds (i.e., 300–600 × real-
time) is a useful starting point. Depending upon the fre-
quency of rockfall events and environmental conditions,
this process takes between 10min and 3h for each 12-h
video.
When possible, footage from multiple cameras, includ-
ing the optical feed from Camera 4, is used to confirm
interpreted rockfalls.
5 Rockfall Observation Testing
The primary objective of this study is to evaluate the abil-
ity to detect rockfall events from thermal imaging data in
surface mining operations as a method to protect mine work-
ers from the risk of rockfalls. An important component of
this goal is determining camera reliability and effectiveness
across the diverse environments that host open-pit mining
operations. Different climates may impact thermal imaging
performance due to variations in temperature, humidity, and
atmospheric conditions. Furthermore, different geologic
environments may introduce challenges, such as reflections,
thermal conductivity variations, and background noise. The
selection of deployment locations was strategically aimed
at testing in diverse geologic and climatic environments to
determine thermal camera utility and reliability across a
broad spectrum of operational environments.
5.1 Summary ofDeployments
Data have been collected from open-pit mine slopes during
nine deployments to eight different sites since January 2021.
Testing locations have included various environments from
mid-summer heat and monsoon rains in Arizona (AZ) to the
depth of winter in the Rocky Mountains of British Columbia
(BC). Initial tests of the system were completed at Univer-
sity of Arizona’s San Xavier (SX) Mine in January 2021,
where the MMP was pointed at an excavated slope on the
property to verify system functionality before its deployment
in open pit mine operations. Once initial testing was com-
plete, deployment on operating mine sites commenced in
February 2021. The MMP was initially deployed to two open
pit copper mines (Mines 1 and 2) in southern Arizona with
three cameras (Cameras 1, 2, and 3 in Table2). Camera 4
was added to the system prior to deployment at Mine 3,
a gold mine located in Colorado. Table3 summarizes the
deployment dates, locations, and geologic settings. Table4
provides details about the environmental conditions experi-
enced during each deployment.
5.2 Controlled Drop Tests
Controlled drop tests are an experimental procedure devel-
oped to evaluate the ability of thermal cameras to detect
falling rocks of different sizes in a controlled and measur-
able manner. During a controlled drop test, rocks of known
dimensions and characteristics are systematically released
from a specified height onto a target area. Before the test,
the rocks are measured, photographed, and often marked or
painted to identify approximate size class, with the MMP
strategically positioned to capture the event. Controlled drop
tests were performed during initial testing at the SX mine
and deployments at Mine 2 and 6.
5.2.1 SX Mine Controlled Drop Test
A controlled drop test was conducted at the SX Mine during
initial system testing in January 2021. Rocks were dropped
from the top of a 4 to 5m slope composed of fill mate-
rial. The MMP was located approximately 70m from the
monitored slope. Approximately 40 natural rocks ranging
from 5 to 50cm in diameter were dropped by hand or fork-
lift. At this range, Camera 2 had the lowest resolution at
the range tested. Camera 2 also had a sky within its field
of view, which decreased the contrast of the thermal video
and resulted in lower effective detection limits. The small-
est blocks detected by Camera 2 were 20cm in diameter.
Cameras 1 and 3 had higher resolution at this range and did
not have any sky in the field of view, which allowed for the
detection of smaller blocks. Camera 1 could detect blocks as
small as 10–12cm in diameter, and Camera 3 was capable
of detecting blocks as small as five cm. This trial laid the
groundwork for testing procedures and provided valuable
information about best practices for future deployments
(e.g., limiting sky view).
5.2.2 Mine 2 Controlled Drop Test
Mine 2 is a porphyry copper deposit located in Arizona,
approximately ninety miles north of the Mine 1 deployment
site. Mine elevations range between approximately 850 and
1400m. The bench increment is approximately 15m and
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Observation ofRockfall intheThermal Infrared
the inter ramp slope angle is between 46 and 48 degrees.
The MMP was deployed at an overlook on the south side of
the pit on the 1,200m level. Thermal camera recording took
place between March 9 and March 19, 2021. All cameras
were pointed to the north to observe a south-facing pit slope,
which consists of competent granitic to granodiorite rock
benches filled with talus.
Figure8 shows the scene observed from Camera 2, with
the initiation location for a controlled drop test on March 19.
The dashed lines illustrate the scenes for the other two cam-
eras on the MMP during deployment. The vertical distance
between the test initiation and the pit bottom is approxi-
mately 300m. The test consisted of a loader pushing mate-
rial over the slope's edge. Approximately 35 natural rocks of
known dimensions between 30cm and 5m were dropped to
evaluate the cameras’ ability to detect rocks of varying sizes.
Each of the rocks was photographed, dimensioned, painted,
and numbered by their approximate size class before testing.
The test took place approximately 900m from the MMP,
resulting in pixel dimensions of 50cm, 80cm, and 45cm for
Cameras 1, 2, and 3, respectively. While reviewing the ther-
mal video recordings, GCE personnel could detect all rocks
included in the test. Notably, as the falling rocks traveled
down-slope and impacted and scoured the pit wall, changes
in the temperature of the slope were detected, indicating that
rocks smaller than pixel dimensions may be detected by their
impact on the slope.
5.2.3 Mine 6 Controlled Drop Test
The MMP was deployed at Mine 6, a porphyry copper
deposit located in British Columbia, Canada, between Octo-
ber 26, 2021 and January 24, 2022. The timing and location
of both Mines 6 and 7 were selected to evaluate thermal
camera performance during freezing conditions. The Mine 6
site elevation ranges from about 1060 to 1550m. The MMP
was deployed at a buttress on the east side of the pit on
the 1200m level. The cameras were initially pointed to the
Table 3 Summary of deployment locations
ID & Loc Dates Geologic setting Notes
1 AZ 2/9–3/2/21 Porphyry copper skarn Evaluated thermal camera performance in winter desert conditions. Observed numerous
rockfall events, both natural and mining-induced
2 AZ 3/9–3/19/21 Copper porphyry Evaluated thermal camera performance in winter desert conditions, including controlled
drop test
3 CO 4/7–5/14/21 Epithermal gold Site selected to observe high elevation, freeze / thaw conditions. Observed numerous
rockfall events, both natural and mining-induced
4. UT 5/26–8/3/21 Multi-metal porphyry Monitored rockfalls associated with an active slope movement
4. UT 10/23/23–5.27/24 Site selected to observe freeze / thaw conditions
5 AZ 8/23–9/30/21 Copper porphyry Observed numerous rockfall events, both natural and mining-induced
6 BC 10/26–1/24/22 Copper porphyry Evaluated thermal camera performance in freezing conditions, including controlled drop
test
7 BC 1/27/21-
4/13/22
Coal Evaluated thermal camera performance in freezing conditions. Observed numerous
rockfall events, both natural and mining-induced
8 AZ 6/21-
9/13/22
Copper porphyry Evaluated thermal camera performance in summer desert conditions. Observed numer-
ous rockfall events, both natural and mining-induced
Table 4 Summary of
environmental conditions by
deployment
*Combination of snow and rain
**does not account for frozen vs liquid precipitation, thus values are extremely high
Mine ID Tot deployment
precip. (mm)
Max Precip/
24h (mm)
Max Temp C° Min Temp C° Ave Temp C°
1 Trace Trace 27.3 0.6 13.8
2 2 2 27.7 4.8 14
3 40 * 12 * 26.5 -8.4 7.7
4 29 8 39.1 6.2 25.8
5 30 8 39.5 16.7 27.5
6 102 * 18 * 14 – 28 – 1.8
7 2358 ** 1505 ** 20.4 – 30 – 6
8 123 32 50.8 16.7 30.1
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E.C.Wellman et al.
southwest toward a fault zone approximately 2000m away
that was known to be moving. In January 2021, the cam-
eras were pointed toward an overspill slope for controlled
rockfall drop testing. The range to the overspill drop test
location was 1500m. Four controlled drops were conducted
with boulders of varying dimensions, as detailed in Table5.
All of the controlled drop tests were observed on thermal
imagery. Figure9 shows a picture of a loader carrying one
of the test rocks (a) and the results of the test as seen through
Camera 2 (b).
5.2.4 Controlled Drop Testing Comments
Field observations were primarily conducted during the con-
trolled drop tests. These tests allowed us to directly correlate
the thermal camera recordings with the falling rocks' known
initiation points, travel paths, and end locations. However,
the long-term trials, which spanned several months, were
designed to monitor rockfall events over extended periods
without real-time field verification. Due to the nature of
these trials, verifying the initiation points or travel paths of
the detected events in real-time was not feasible. The review
of thermal camera data was performed retrospectively, intro-
ducing a time lag that further complicated direct comparison
with field conditions.
Additionally, safety considerations limited our ability to
confirm specific details, such as initiation points and travel
paths, during these trials. Access to areas where rockfalls
occurred was restricted to ensure the safety of personnel,
preventing us from conducting in-field verification of all
detected events. The primary focus was on the detection
capability of the thermal cameras rather than on the precise
tracking of rockfall dynamics in real time. Work is currently
underway to automate detection and tracking of rockfall
from thermal video. An algorithm has been developed that
can accurately record initiation point, travel path, and end
location, as well as estimate block size based on the number
of disturbed pixels. The results of this work will be provided
in a future publication.
5.3 Observation ofRockfall intheThermal Infrared
Over 1,000 rockfalls have been manually identified from
the thermal video recorded during this project. This section
documents examples of rockfalls at a variety of deployment
sites. These and additional examples have been compiled
Fig. 8 View from Camera 2 during Mine 2 deployment. Dashed lines
represent fields of view from Cameras 1 (orange) and 3 (yellow)
Table 5 Details of the controlled drop tests at Mine 6
Drop # # of Boulders Boulder size (m)
1 1 3
2 1 1.5
3 ~ 30–40 0.3–0.5
4 3 1.8, 1.5, 1
Fig. 9 Controlled drop test –
mine loader carrying a boulder
to drop site (a) and view from
Camera 2 during test (b)
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Observation ofRockfall intheThermal Infrared
into a video intended to illustrate the capabilities of thermal
infrared cameras in detecting rockfall. The video, which can
be viewed here(https:// arizo na. hosted. panop to. com/ Panop
to/ Pages/ Viewer. aspx? id= b2d5d dfa- 9ce8- 42fc- bb76- aea60
17116 c6), presents several of the more significant rockfall
events identified during the first six deployments (Table3).
Table6 provides screenshots, a short description of each
event, and the time of the screenshot in the video. Each
rockfall event can be viewed from the multiple cameras on
the MMP, but upon compiling clips needed for the video,
specific camera views were chosen based on how well they
illustrated each rockfall event.
The full thermal array data from Camera 1 (FLIR A400)
provide valuable insights into the mechanism by which ther-
mal cameras detect rockfall. This is illustrated in Fig.10,
which shows the scene through Camera 1 when deployed
at Mine 4, just before (a) and after (b) a rockfall. The graph
(c) on Fig.10 shows temperature data for a pixel located
at the rockfall initiation point, indicating an approximate
3.4°C drop in temperature between pre- and post-rockfalls.
The MMP was approximately 2,250m from the slope when
the images in Fig.10 were taken. The height of the benches
shown in this image is approximately 15m, and the pixel
dimensions are approximately 1.4m. The rockfall was
approximately 4 by 8 pixels across, and the rock traveled 4
to 5 benches down-slope.
Figure11 illustrates another example of rockfall, this time
viewed through Camera 4, which is the thermal component
of the pan-tilt-zoom security-grade camera. The rockfall
occurred at approximately 9:55 AM on May 2, 2021, while
deployed at Mine 3. The cameras were located approxi-
mately 700m from the slope. The rockfall was contained
to a single, roughly 15-m bench. The dislodged block of
rock was approximately 8 × 8 pixels (~ 5m) in diameter. The
images in Fig.11 show the dislodged block and thermal
anomaly left by the void just after the rock dislodges (a), a
thermal trail that is left by the block impacting the bench as
it travels down-slope and/or smaller pieces of material that
dislodge shortly after the larger block (b), and the result-
ing void and associated thermal anomaly after the block is
deposited on the bench below (c). The first frame shows the
void left immediately after the block dislodges, as well as
an estimation in size of the void and the block of rock. An
image of the same area approximately 8ms later is shown
in the second frame; note the visible thermal trail created
by impacts from the falling block and/or smaller pieces of
material that came after the initial block dislodged. The third
frame shows the same area approximately 5s after the initia-
tion of the rockfall, with an outline of the final extents of the
void post-rockfall and associated thermal anomaly. The void
increases in size from roughly 8 × 8 pixels to 13 × 10 pixels
between the frames shown on the left and right in Fig.11,
indicating additional material dislodged from the slope after
the initial block fell.
6 Discussion
Thermal infrared cameras have demonstrated significant
potential as a tool for monitoring and detecting rockfalls
in open pit mining operations. The research presented here
highlights their effectiveness in diverse environmental con-
ditions and suggests the potential value of integrating infra-
red imaging into existing geotechnical slope monitoring sys-
tems. The following subsections discuss several observations
made throughout the course of this project, including the
use of thermal cameras for enhanced situational awareness,
potential predictive capabilities, practical applications and
limitations, and directions for future research.
Trials of thermal infrared camera systems continue by the
Geotechnical Center of Excellence and other researchers.
Warren etal. 2022 has used thermal infrared rockfall detec-
tion in ongoing research on reviewing catch bench design
criteria, with the potential to revisit and update rockfall trap
design criteria for mines.
6.1 Camera Performance
The testing conducted across the mine sites using six ther-
mal cameras provides observations into the cameras' com-
parative performance under varying environmental condi-
tions. While the cameras successfully operated across the
full range of conditions without any significant failures, the
performance differences primarily stem from two key fac-
tors: the camera's field of view (FOV) and resolution. These
parameters influenced the effectiveness of rockfall detection
as they directly impacted the camera's ability to capture and
distinguish thermal variations on the monitored slopes.
No adverse issues were encountered with any of the fixed
and stationary mounted thermal cameras. However, the vis-
ual image recording on the AXIS Q8752-E pan-tilt-zoom
camera had a continuing issue with video packet loss on the
transfer between the camera and the video recording system.
The issue was also encountered with one of our mine col-
laborators on an independent system. This was attributed to
the higher data volume on a 1080p camera and 24-bit color.
This packet drop issue was not resolved during the trials.
It is worth reiterating that the field of view and the resolu-
tion are the most critical factors in selecting an appropriate
thermal camera for rockfall detection. Cameras with higher
resolution and wider FOVs provide larger areal coverage
and more detailed thermal imagery, enabling the detection
of smaller rockfall events. Conversely, cameras with nar-
row FOVs and lower resolution are more limited in their
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E.C.Wellman et al.
coverage area, restricting the detection of rockfall events
outside the camera's field of view.
All of the trialed camera systems had web browser inter-
faces for controlling the camera software. The cameras were
all controlled by a video recording system. Although the web
interface varied, these were not significant to the operation
of the camera systems.
Detecting rockfalls effectively was closely tied to the
camera’s focus, range, and distance settings. Adjusting
these parameter settings during project setup will ensure
the cameras can accurately capture and identify rockfall
events.
Table 6 Screenshots and descriptions of rockfalls depicted in the highlights video
00:07 rockfall detected from thermal footage collected at Mine 1. In this image, warmer pixels are yellow and
colder pixels are purple. The cursor is pointing out a falling rock that was measured to be around 13°C and
shows as a yellow dot. This rockfall occurred at night, so the falling rock appears hotter than the cool nighttime
surface of the pit slope. The boulder that is rolling splits in two as it falls down the talus pile to its final resting
place
00:23 controlled drop test of a 5m boulder down a 300m high slope at Mine 2. The bench increment is 15m.
As the boulder rolls down the slope, it fragments into several pieces. Impacts, dust clouds, and scouring can be
observed in cooler dark gray and black. During this test, it was observed that falling blocks smaller than pixel
size can be detected by their thermal trails. The cooler portion of the lower right-hand side of the video show
groundwater seepage. The double bench increment in this scene is 30m
0:59 rockfall observed at Mine 3. As the video zooms, the rockfall can be observed on the bench below the open
stope. This event is discussed in detail in Sect.5.2. The video shows the void left immediately after the block
dislodges. Note the visible thermal trail created by impacts from the falling block and/or smaller pieces of
material that came after the initial block dislodged. The segment ends with the rockfall resting on the bench
immediately below the void. The bench increment in this scene is 15m
1:20 rockfall observed in thermal camera while deployed at Mine 4. The event is first shown in footage from
Camera 1 and again in Camera 3. This video clip is pixelated due to compression settings. The rockfall
proceeds down slope for approximately 5 to 7 benches. This rockfall and corresponding temperature drop is
discussed below and shown in Fig.10. The bench increment in this scene is 15m
2:17 multi-bench rockfall recorded at Mine 5. The rockfall initiated from an active wedge and traveled approxi-
mately 8 benches before being stopped on a bench above the haul ramp. This orientation illustrated the ability
of the cameras to identify rockfall moving perpendicular to the camera view angle. The bench increment in this
scene is 15m
2:41 rockfall during a controlled drop test at Mine 6. The controlled drop test is discussed in Sect.5.2.2. The
slope height of the talus covered slope is approximately 150m
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Observation ofRockfall intheThermal Infrared
6.2 Situational Awareness
A key advantage of thermal infrared cameras is their ability
to provide continuous situational awareness regardless of
lighting conditions. Traditional cameras are limited by the
availability of light, whereas thermal cameras can operate
effectively throughout the day and night. This capability is
crucial for the timely detection of hazardous movements
and the initiation of response measures. Figure12a, b shows
examples of thermal infrared images in both day and night
conditions. In these images, warmer colors are represented
by shades of yellow and cooler colors are represented by
shades of purple and black. For example, an active rockfall
has been initiated by shovel spillover in both Fig.12a and b.
The image in Fig.12a was taken at night, when the spillover
is hotter than the surface of the slope. Figure12b shows the
same slope approximately 12h later during daylight condi-
tions, when the spillover is cooler than the surface.
Thermal cameras are also able to pierce particulates like
dust, smoke, and precipitation more effectively than visible
light cameras. Figure13a, b shows a comparison between
a visible light image and a thermal infrared image, which
was taken at the same time and depicted the same slope.
These images are screen shots from a video recorded on
a particularly dusty day at Mine 5. Note that the benches
are almost entirely obscured by the dust in the visible light
image (Fig.13a), but the thermal infrared image provides
a clear view of the benches and several haul trucks on the
slope. There is also an active rockfall taking place at the time
these images are taken, which is lost in the lower benches
of Fig.13a but can be tracked in completion in the thermal
infrared image in Fig.13b.
Observations made during this study have also shown
that thermal cameras are capable of detecting and delineat-
ing groundwater seeps in open pit walls. Pore pressure from
groundwater is one of the most important factors determin-
ing the stability of mining excavations, and groundwater
seeps are a direct observation of water conditions in an
Fig. 10 Pre- and Post-Rockfall Images with associated temperature
drop
Fig. 11 Example rockfall as seen from Camera 4T
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E.C.Wellman et al.
unexcavated rock mass (Beale and Read 2013). Groundwa-
ter seeps are readily detected using thermal imagery due to
the large temperature differential typically observed between
wet and dry rocks (Fig.14), and they commonly occur along
geologic discontinuities, such as faults, joints, and bedding.
Long-term observations of seep distribution and relative
magnitude (flow) using thermal imagery could provide
information about the dynamic behavior of groundwater
from perturbations, such as blasting, mining, dewatering,
and precipitation, and allow for delineation of geotechnical
y significant rock discontinuities.
Thermal imaging can differentiate rockfall paths from
groundwater seepage. Groundwater seepage typically
exhibits a constant temperature that does not fluctuate
significantly between day and night. These seepage areas
are generally static, maintaining a consistent size and tem-
perature throughout the deployments during which they
were identified. This stability makes them distinct from
rockfall events.
In contrast, rockfall traces are characterized by their
transient nature. When a rockfall occurs, the disturbed
rock surfaces exhibit temperature changes that are initially
distinct from the surrounding ambient temperature. How-
ever, these temperature differences gradually diminish,
with rockfall traces typically reverting to ambient tem-
perature within 12 to 48h. The duration of this tempera-
ture reversion is influenced by the volume of the rockfall,
with larger events taking longer to return to the ambient
conditions. Additionally, rockfall traces tend to occur over
narrow paths, distinguishing them from the more stable
and larger zones associated with groundwater seepage.
This difference in thermal behavior between rockfall
paths and groundwater seepage, along with their distinct
spatial characteristics, allows for reliable differentiation
using thermal cameras, even under similar environmental
conditions.
The ability to map seeps using thermal imagery can
increase our understanding of slope behavior as it pertains
to groundwater, resulting in more informed slope movement
mitigation strategies and dewatering efforts that are critical
to maintaining stable slopes. Figure14a, b, c, d illustrates
the capabilities of thermal imaging in detecting seeps by
Fig. 12 Thermal image depicting the same pit slope during a night-
time / low-light and b daylight conditions
Fig. 13 Comparison between visible light (a) and thermal infrared
image, b at the same time
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Observation ofRockfall intheThermal Infrared
comparing visible light and thermal infrared images from
two open pit mines.
6.3 Freeze–Thaw Cycles andRockfall
While deployed at an open pit operation in Colorado (Mine
3), the thermal cameras recorded several cycles through
the freeze–thaw transition. Figure15 illustrates the view
through Camera 2, in which approximately 230m of slope
is visible. The bench increment is 15m in this image.
Four rockfalls were observed on the slope between May
2nd and May 5th, a period in which temperatures rise and
fall above and below freezing several times. A time series
graph with temperature and solar irradiance shown in rela-
tion to the times of the for rockfall events is provided in
Fig.15. These rockfalls were all single bench rockfalls,
with the material retained on the catch bench. Three rock-
falls occurred during the day when the rock surface was
heating, while one rockfall occurred at approximately 8:00
PM, roughly 4h after the temperature dropped below 0°
C Fig.16.
6.4 Rockfalls Preceding Major Landslides
The data collected during Phase 1 include thermal record-
ings of two large slope displacements that took place at Mine
4 on May 31, 2021 and July 21, 2021 referred to here as
the Primary Failure and Secondary Failure, respectively.
Recording began at approximately 8:30 AM on May 26th,
just over four days before the Primary Failure occurred.
Real-time review of the footage indicated an increase in
rockfall during this time. Schafer etal. (2023) document a
detailed analysis of this data, which shows an exponential
or power law increase in the total number of rockfalls in
the days leading to both the primary and secondary fail-
ures. Schafer presents a method for predicting the timing
of slope failure based on the observed systematic decrease
in the time interval between rockfalls in the days leading to
failure. Figure17 depicts rockfall locations overlaid on a
still image from the thermal camera taken roughly 10min
before the primary failure (a) and a cumulative frequency
plot of observed rockfalls between initial deployment and
time-of-failure (b).
6.5 Limitations
Reduced visibility due to rain, snow, fog, smoke, and dust
is the primary limiting factor for detecting rockfall from
Fig. 14 Visible light (a, c)
and thermal infrared (b, d)
images of open pit mine slopes
with active groundwater seeps
present
Fig. 15 View from Camera 2 while deployed at Mine 3 in May 2021.
Dashed lines show the field of view of Cameras 1 (green), 3 (yellow),
and 4 (blue)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
E.C.Wellman et al.
thermal video. While thermal cameras generally provide
more clarity than optical cameras in these conditions, pre-
cipitation and particulate matter can scatter infrared radi-
ation and reduce the effectiveness of thermal imaging in
extreme scenarios (e.g., heavy rainfall, blizzard conditions).
Figure18 compares optical and thermal images taken at
the same time during moderate (Fig.18a and b) and heavy
(Fig.18c and d) snowfall. The 2nd phase of the project will
identify approximate cutoff points for environmental data at
which rockfall can no longer be detected.
At the same price point as optical cameras, COTS ther-
mal cameras have lower spatial resolution, which can result
in images with limited detail and clarity. Thermal cameras
should be selected on a project-specific basis. Distance from
the slopes to monitoring should be considered in relation
to camera resolution and field of view. The ability to detect
rockfalls from thermal imaging can also be impaired by
reduced image contrast due to sky view. The sky is always
cooler than the pit slope and terrain and including sky in the
field of view will expand the temperature scale. The amount
of sky view in the thermal camera frame should be reduced
as much as possible to avoid a reduction in contrast.
7 Conclusions
This paper documents observations of rockfall in the thermal
infrared and demonstrates the significant potential of ther-
mal cameras as a tool for monitoring and detecting rockfalls
in open pit mining operations. Key findings and implications
include:
1. Effectiveness of thermal cameras. The deployment of
five different long-wave infrared (LWIR) thermal imag-
ing systems across eight different open pit mines has
proven effective in detecting rockfalls under a wide
range of conditions. These systems successfully detected
rockfall events at temperatures ranging from −27°C
to 52°C, confirming their reliability across diverse cli-
matic settings.
2. 24-h monitoring capability. One of the most significant
advantages of thermal infrared cameras is their ability
to operate continuously, providing rockfall monitoring
and overall situational awareness throughout the diurnal
Fig. 16 Temperature and solar
irradiance data with respect to
rockfalls
Fig. 17 Location of the Leo Failures (a) and Cumulative Frequency
of Rockfalls prior to May 31(b)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Observation ofRockfall intheThermal Infrared
cycle. Unlike traditional visual cameras, thermal cam-
eras are not dependent on ambient light, making them
highly effective for nighttime monitoring and more
effective in reduced visibility due to dust, smoke, and
moderate precipitation.
3. Detection of rockfalls smaller than pixel resolution. It
was observed that thermal cameras could detect rock-
falls even when the falling blocks were smaller than the
camera’s pixel resolution. This capability is attributed to
the distinct thermal signatures left by scours and craters
made by falling rocks as they impact the slope.
4. Operational integration. The development of the MMP
has enabled the systematic deployment and evaluation
of thermal cameras in real-world mining conditions. The
deployments conducted using the MMP have proven
the ability for thermal cameras to withstand operational
conditions commonly present on mine sites, while col-
lecting valuable data that can be integrated into existing
geotechnical slope monitoring frameworks.
5. Challenges and limitations. Despite their advantages,
thermal infrared cameras have limitations, such as
reduced effectiveness in extreme weather conditions
and lower spatial resolution, compared to optical cam-
eras. The study highlighted the importance of camera
selection and parameter setup, including optimization
to minimize sky view and enhance thermal contrast.
The findings from this study lay the groundwork for
future research and development aimed at integrating
thermal imaging into comprehensive geotechnical moni-
toring systems, ultimately enhancing the safety and effi-
ciency of mining operations. While human observers can
see rockfalls in thermal images, long-term and continu-
ous observations still present a substantial task burden
to human operators. A significant direction for future
work includes the ability to automatically detect, track,
and alarm rockfalls in real time using computer vision
and machine learning algorithms. Technical challenges to
automation include real-time motion detection, reliable
classification of observed events as rockfall or non-rock-
fall and tracking of time series so that rockfall trajectories
may be established. In addition to alarming, an automated
rockfall detection system would facilitate the cataloging
and classifying rockfall events. When used in parallel
with other slope monitoring instrumentation, including
prisms, radar, and environmental data (e.g., displacement,
velocity, temperature, air pressure, precipitation, seismic
readings, etc.), such a system would yield new opportuni-
ties for correlating rockfalls with slope displacement and
environmental factors to identify times of increased rock-
fall risk. Phase 2 of this work will address many of these
challenges.
In addition to real-time monitoring, this work and prior
studies suggest a range of other geotechnical use cases
for thermal imagery with the potential to enhance miner
safety (Schafer et. al 2023, Guerin et. al 2019, Rosser et.
al 2007), which include:
Fig. 18 Optical and thermal
images taken during moderate
(a, b) and heavy (c, d) snowfall
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
E.C.Wellman et al.
Detection, delineation, and mapping of groundwater
seeps over time, which can provide information about
the state of potential destabilizing groundwater in the
slope (Sect.6.2).
Evaluation and documentation of catch bench perfor-
mance through comparison of tracked rockfall trajecto-
ries with bench design widths.
Monitoring large-scale slope movements to inform slope
management and time-of-failure predictions (Sect.6.4).
Further research is needed to better understand and docu-
ment the potential applications of thermal imaging tech-
nology.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s00603- 024- 04254-1.
Acknowledgements This work was supported in part by NIOSH BAA
awards 75D301-20-R-67845 and 75D301-22-R-14875. The Univer-
sity of Arizona School of Mining and Mineral Resources, Geotechni-
cal Center of Excellence, would also like to acknowledge permission
to publish data acquired at active mine sites. Research Collabora-
tors on this project included ASARCO, Capstone Mining Corpora-
tion, Freeport-McMoRan, Newmont, Rio Tinto, and Teck Resources.
Their support of this project and research to improve mine safety is
acknowledged. Additionally, the authors would like to acknowledge
the contributions of the following researchers, including James Nickels,
Christian Ortmann, and Bobby Prescott, who contributed to the project
over the last four years.
Funding The findings and conclusions in this paper are those of the
authors and do not necessarily represent the official position of the
National Institute for Occupational Safety and Health, Centers for Dis-
ease Control and Prevention. Mention of any company or product does
not constitute endorsement by NIOSH.
Data Availability The datasets generated and analyzed during the cur-
rent study are available from the University of Arizona, School of Min-
ing & Mineral Resources, Geotechnical Center of Excellence upon
reasonable request. For any inquiries regarding the data, please contact
the Julia Potter, Director at the Geotechnical Center of Excellence.
Declarations
Conflict of Interest The authors declare that they have no conflict of
interest.
Open Access This article is licensed under a Creative Commons
Attribution-NonCommercial-NoDerivatives 4.0 International License,
which permits any non-commercial use, sharing, distribution and repro-
duction in any medium or format, as long as you give appropriate credit
to the original author(s) and the source, provide a link to the Creative
Commons licence, and indicate if you modified the licensed material.
You do not have permission under this licence to share adapted material
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the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this licence, visit http://crea-
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Rockfall is a common hazard along US Interstate Highway 70 in Glenwood Canyon, Colorado. While natural rock slopes source a substantial number of rockfalls, there remains a need to better understand typical rockfall frequencies from these slopes and to relate these frequencies to available rockfall records. In addition, the 2020 Grizzly Creek Fire in the area presents an opportunity to characterize the post-wildfire response of granitic rock slopes. To address these needs, we monitored 4 natural rock slopes of different lithologies and burn severities from 6 to 18 months after the start of the fire using an Unpiloted Aerial Vehicle and Structure from Motion photogrammetry. A total of five rockfalls (0.08–0.68m3) were detected during the monitoring interval at two slopes, while the other two showed no rockfalls. While overall rockfall activity is relatively low, the observed activity is in good agreement with results of previous study in the area, rockfall records for the highway, and analysis of a historical photo of one slope. No increase in rockfall due to the fire was observed during the monitored period, implying that the fire did not significantly affect the studied slopes or that any increase in activity had already returned to background levels by the start of monitoring. Four of the five rockfalls detected were spatially correlated with seeps in the slope, and three were temporally correlated with late winter snowfall and spring thawing, highlighting the importance of water as a consideration in selecting rock slopes for hazard monitoring and mitigation.
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Discontinuity extraction and interpretation of fractured masses is of high importance when analyzing rock slope stability. Regarding high-steep slopes, which are areas that are difficult to reach, traditional methods to obtain discontinuities, such as the sample window method (SWM), are unlikely to be implemented, resulting in challenges for the identification of potential rockfalls. With the development of the unmanned ariel vehicle (UAV) technology, discontinuity extraction can overcome by noncontact photogrammetry. However, there is still a lack of comprehensive and practical solutions to fulfill rockfall identification from field investigation to in-door analysis. For this purpose, a practical case study was carried out in Wanzhou, Chongqing, China, where a 400 m vertical rock slope prone to rockfall was collected as a typical example. The centimeter-level 3D Textured Digital Outcrop Model (TDOM) and dense Point Cloud (PC) were established using high-resolution photos acquired by nap-of-the-object photogrammetry. The discontinuity of the fractured mass was interpreted by fully taking advantage of both 2D images (texture information-dominated) and 3D PCs (depth information-dominated). Furthermore, a new parameter rock cavity rate (RCR) and the corresponding semiautomatic extraction method based on point clouds are proposed. Subsequently, the possibility of various failure modes and their joint combinations were determined by kinematic analysis. Finally, the rock slope stability was determined using a matrix that considers the slope mass rating (SMR) value and the parameter RCR. The proposed process flow and relevant techniques in this study provide an operable and practical solution for further application regarding discontinuity interpretation and potential rockfall identification on high-steep slopes.
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The identification and treatment of mining-induced ground fissures are of great significance to mine safety and ecological and environmental protection. In this study, a novel method for ground fissure identification and exploration by infrared remote sensing onboard an unmanned aerial vehicle (UAV) was proposed. Using this method, a region of interest (ROI) that includes ground fissures directly above the middle of a long wall face, No. 12401 in the Shangwan coal mine, was monitored continuously during the day and night. Direct field measurements of ground fissure properties were also conducted to provide a calibration dataset for UAV measurements. Using the direct visible image at 5:00 pm as a reference, the average errors of the length and maximum width of Fissure I obtained from infrared images from 9:00 pm to 5:00 am on the next day, were estimated to be 1.8% and 6.5%, respectively. The diurnal variation of the fissure temperature is sinusoidal, and the range of temperature variation in the fissure decreases with the increase in depth. There is an apparent difference between the two common types of fissures depending on whether the fissure has a direct connection to an aquifer or a goaf. In this study, UAV, infrared thermal imager, and visible light camera data were successfully employed to effectively identify mining-induced ground fissures. In addition, the fissure detection error was validated, and the appropriate time for utilizing this method was obtained. Our results show that to identify the two aforementioned types of fissures, monitoring should be conducted between 3:00 am and 5:00 am. This study lays a foundation for the study and application of UAV and infrared thermal imagers for the identification of ground fissures induced by underground mining in large areas.