A Procedure to Determine the Optimal Sensor Positions for Locating AE Sources in Rock Samples

Within a research work aimed to better understand frost weathering mechanisms of rocks, laboratory tests have been designed to specifically assess a theoretical model of crack propagation due to ice segregation process in water-saturated and thermally microcracked cubic samples of Arolla gneiss. As the formation and growth of microcracks during freezing tests on rock material is accompanied by a sudden release of stored elastic energy, the propagation of elastic waves can be detected, at the laboratory scale, by acoustic emission (AE) sensors. The AE receiver array geometry is a sensitive factor influencing source location errors, for it can greatly amplify the effect of small measurement errors. Despite the large literature on the AE source location, little attention, to our knowledge, has been paid to the description of the experimental design phase. As a consequence, the criteria for sensor positioning are often not declared and not related to location accuracy. In the present paper, a tool for the identification of the optimal sensor position on a cubic shape rock specimen is presented. The optimal receiver configuration is chosen by studying the condition numbers of each of the kernel matrices, used for inverting the arrival time and finding the source location, and obtained for properly selected combinations between sensors and sources positions.

[1]  D. Lockner.  The role of acoustic emission in the study of rock fracture , 1993 .

[2]  C. Sondergeld,et al.  Acoustic Emission and SEM Analyses of Hydraulic Fractures Under Triaxial Stress Conditions , 2012 .

[3]  H. Reginald Hardy,et al.  The mechanism of array geometry in the control of AE/MS source location accuracy , 1988 .

[4]  R. V. Prasad,et al.  Microcracking and Fracture Process in Cement Mortar and Concrete: A Comparative Study Using Acoustic Emission Technique , 2013 .

[5]  Xinglin Lei,et al.  Detailed analysis of acoustic emission activity during catastrophic fracture of faults in rock , 2004 .

[6]  Tomoya Niwa,et al.  Acoustic emission monitoring of hydraulic fracturing laboratory experiment with supercritical and liquid CO2 , 2012 .

[7]  Grzegorz Kwiatek,et al.  Network sensor calibration for retrieving accurate moment tensors of acoustic emissions , 2013 .

[8]  Gene H. Golub,et al.  Calculating the singular values and pseudo-inverse of a matrix , 2007, Milestones in Matrix Computation.

[9]  M. S. King,et al.  Acoustic emissions associated with the formation of fracture sets in sandstone under polyaxial stress conditions ‡ , 2012 .

[10]  D. Amitrano.  Rupture by damage accumulation in rocks , 2006 .

[11]  P. Nelson,et al.  ESTIMATION OF ACOUSTIC SOURCE STRENGTH BY INVERSE METHODS: PART I, CONDITIONING OF THE INVERSE PROBLEM , 2000 .

[12]  S. Gruber,et al.  Environmental controls of frost cracking revealed through in situ acoustic emission measurements in steep bedrock , 2013 .

[13]  C. Stubbs,et al.  Weathering by segregation ice growth in microcracks at sustained subzero temperatures: Verification from an experimental study using acoustic emissions , 1991 .

[14]  J. Santamarina,et al.  Discrete Signals and Inverse Problems: An Introduction for Engineers and Scientists , 2005 .

[15]  P. Swanson.  Accuracy and precision of microseismic event locations in rock burst research studies , 1992 .

[16]  Dayong Hu,et al.  Near-field beamforming analysis for acoustic emission source localization. , 2012, Ultrasonics.

[17]  H. Reginald Hardy.  Acoustic emission, microseismic activity , 2003 .

[18]  T. Lokajíček,et al.  Determination of the anisotropy of elastic waves monitored by a sparse sensor network , 2013 .

[19]  J. Huba,et al.  Simulation of the seeding of equatorial spread F by circular gravity waves , 2013 .

[20]  Ai Ting,et al.  Space–time evolution rules of acoustic emission location of unloaded coal sample at different loading rates , 2012 .