3D assessment of an underground mine pillar by combination of photogrammetric and geoelectric methods

Abstract

3D assessment of an underground mine pillar by combination of photogrammetric and geoelectric methods. ABSTRACTThe monitoring of underground cavities plays a key role in risk management policies. Mine and underground quarry stakeholders require relevant methodologies and practices to define and assess hazards associated with these structures. To monitor these structures, geophysical methods may offer an interesting compromise among operating cost, invasiveness, and risk assessment reliability. The use of conventional 3D-electric resistivity imaging (ERI) software validated on relatively flat media is not sufficient to efficiently assess complex 3D geometries such as underground mine pillars. We have developed a new approach to evaluate pillar condition by means of a sequential use of two techniques. First, the photogrammetric method yields a detailed 3D model of the pillar geometry from a set of pictures. Second, 3D-ERI is performed based on this suitable geometry. The methodology is tested on a synthetic model to evaluate the effect of various geometry resolutions on the inversion. We also evaluated the combination of the effect of measurement and geometry error. We performed a quasi 3D-ERI survey (three parallel electrode lines) on a real limestone mine pillar to determine the benefits and limitations of the combined procedure. First results revealed the capacity of the photogrammetric methods to obtain a high-precision geometry and its key role during the inversion process. Second results of the real case study revealed that a highly accurate geometry is required to detect accurately conductive anomalies in a complex 3D context.

3D electrical imaging of the inner structure of a complex lava dome, Puy de Dôme volcano (French Massif Central, France)

“…To perform the 3D inversion of our resistivity data, we use an inversion code developed by Fargier et al (2017). This algorithm is based on a conventional Gauss-Newton smoothness-constrained method with an Occam-type regularization de Groot-Hedlin et Constable, 1990;Lines et Treitel, 1984).…”

“…Gresse et al, 2017;Revil et al, 2010;Rosas-Carbajal et al, 2016;Soueid Ahmed et al, 2018). A complete description of this inversion code is given by Fargier et al (2017). The RMS (Root Mean Square) error, representing the difference between the model response and the measured data, quantifies the reliability of the inversion models.…”

“…Given the spatial geometry of the datasets, we were able to carry out a 3D inversion approach, in order to better constrain the inner structure of the volcano. For this purpose, we used a recent inversion code developed by Fargier et al (2017). Our inversion strategy was first to study the whole lava dome, and then to focus on its summit area only.…”

OhmPi: An open source data logger for dedicated applications of electrical resistivity imaging at the small and laboratory scale

“…Research efforts over the last few decades have led to the development of accurate and robust multi-electrode and multichannel resistivity meters used in the area of Electrical Resistivity Imaging (ERI), inspiring breakthroughs in many fields of the geosciences (hydrogeology [1] , environment [2] , geology [3] ) and in civil engineering [4] .…”

“…In the environmental and civil engineering fields, geophysical monitoring has become key to assessing natural phenomena [4] , [13] , [14] , [15] , [16] , [17] . First applied to monitoring mid-term temporal phenomena (over a few weeks) [14] , [18] , this method is now dedicated to monitoring “long-term” phenomena (several years) [19] , [20] , [21] , [22] , [23] .…”