A change detection algorithm has been developed for identifying areas of movement and calculating displacements in time-lapsed three-dimensional images of rock surfaces. The images are collected using 3D digital imaging techniques with no constraints on sensor type or survey control. The only assumption is that successive images are of the same rock slope surface. The change detection algorithm uses surface meshing and an Iterative Closest Point algorithm to compare time-lapsed images, find corresponding points, register the images through a minimization technique, and finally calculate the displacement between identified corresponding point/surface pairs. Two synthetic case studies indicate that the change detection algorithm can accurately identify areas of movement in time-lapsed images, is capable of measuring displacement to within 4 mm, and performs at low computational cost. The change detection algorithm has been used to produce intensity mapped displacement images providing graphic visualizations of rock slope movement. It is expected that the algorithm will be modified with future field case studies and provide the basis for a slope monitoring system independent of imaging sensor, requires no initial survey control, and provides millimeter precision and wide area coverage of slope wall movements under any environmental conditions.


Between 1995 and 2005 approximately 10% of all surface mine fatalities were the result of either slope failure or other failures of ground such as bench failures, rockfalls, waste dump failures, and stockpile movements [1, 2]. Additionally, unexpected rockfall or movement cause operating disruptions, unsafe working conditions, and economic losses. Ground movement, rockfall, and slope failures also pose a significant problem to transportation agencies. Thousands of miles of highway in the United States are bordered by rock slopes. In addition to older, problematic highway cuts, the growth of transportation networks, and their users, continues to drive the excavation of highways into areas of rugged and mountainous terrain. The result is an ever growing catalog of rock cuts that expose highway users to rockfall hazards, and transportation agencies to economic and social liabilities. Be it for open pit mining or infrastructure engineering, the identification and evaluation of high risk slopes, exposed rock surfaces, and debris piles remains a prohibitive task and one that is complicated by the broad range of geologic conditions that influence these hazards. Currently there are several ways to reduce hazards associated with potential slope instability: improved design, increased secondary support (e.g., wider catchments), mitigation by scaling or removing loose material, and monitoring for advanced warning of failure. Unfortunately, even the most well designed or most conservatively designed slope may be subject to instability. Consequently, monitoring and inspection of slopes and rock surfaces for signs of instability are necessary to insure worker safety and efficient operating conditions. Common slope monitoring systems include a combination of sensors such as: inclinometers, tension cracks meters, extensometers, and survey networks. Surveying instruments are routinely used in open pit mining because of the reliability and accuracy that is offered by the instruments [3]. When combined with environmental sensors, remotely controlled total stations can be used in a fully automated way as the primary measuring device, collecting and disseminating data in a timely and efficient manner.

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