Geophysical tomography allows for the measurement of stress-induced density changes inside of a rock mass or sample by non-invasive means. Tomography is a non-destructive testing method by which sensors are placed around a sample and energy is introduced into the sample at one sensor while the other sensors receive the energy. This process is repeated around the sample to obtain the desired resolution. The received information is converted by a mathematical transform to obtain a tomogram. This tomogram shows a pixelated distribution of the density within the sample. Each pixel represents an average value at that point.

The project discussed in this paper takes the principle of ultrasonic tomography and applies it to geomechanics. A new instrumentation system was designed to allow rapid data collection through varying sample geometries and rock types with a low initial investment. The system is composed of sensors, an ultrasonic pulser, a source switchbox, and analog to digital converters; it is tied together using a LabVIEW virtual instrument.

LabVIEW is a graphical development environment for creating test, measurement, and other control applications. Using LabVIEW, virtual instruments (VIs) are created to control or measure a process. In this application LabVIEW was used to create a virtual instrument that was automated to collect the data required to construct a tomogram.

Experiments were conducted to calibrate and validate the system for ultrasonic velocity determination and stress redistribution tomography. Calibration was conducted using polymethylmethacrylate (PMMA or Plexiglas) plates. Uniaxial loads were placed on limestone and sandstone samples. The stress-induced density contrasts were then imaged using the acquisition system. The resolution and accuracy of the system is described.

The acquisition system presented is a low-cost solution to laboratory geophysical tomography. The ultimate goal of the project is to further the ability to non-invasively image relative stress redistribution in a rock mass, thereby improving the engineer's ability to predict failure.