Early-Time, Multi-Component, Mobile TEM for Deep Metal Detection
“This paper was the start of development of the system that became the Dynamic NanoTEM™ transceiver. We realized very early that there was a significant value in collecting a large time bandwidth and several receiver orientations. Use of numerous time windows and multiple receiver orientations allowed Zonge to develop some of the first discrimination technologies used in the UXO detection environment.”
Scott Urquhart, president and managing geophysicist, Zonge International
Environmental and Engineering Geophysical Society (EEGS), 2002 SAGEEP proceedings, Las Vegas, Nevada.
Norman R. Carlson* and Kenneth L. Zonge*, Zonge Engineering & Research Organization, Tucson, AZ.
Paper — [pdf] ENV_TEM_DeepMetalSAGEEP2002
Data examples from a recent project show very interesting and useful characteristics of the early-time data from the horizontal components of TEM surveys used in deep metal detection for targets such as USTs, UXOs, and utilities. Normally, most deep metal detection surveys utilize a system in which one or two TEM measurements of the vertical component (Hz) are acquired. These systems usually acquire data at relatively late times (hundreds of microseconds after transmitter turn-off), to allow the background earth response to decay to zero. A good example is the popular Geonics EM-61 system. By recording data at numerous time windows for all three components (Hx, Hy, and Hz), however, from early times (a few microseconds) through late times, additional significant information is acquired.
In one recent project, a 55-acre site was surveyed (by another contractor) with an EM-61, and several subsurface targets were identified for excavation. Small areas around these targets, totaling only 2.25 acres, were re-surveyed using a multi-component, early-time system. In addition to verifying the targets, four additional anomalies that were not evident in the Hz data (EM-61 data) were detected, including two buried powerlines. Examination of the horizontal component data of the field also appears to be particularly useful in discriminating targets. For example, linear features such as pipelines and powerlines are easily distinguished from three-dimensional targets with only a single line of data, instead of requiring an array of lines to interpret the targets based on the geometry of anomalies on adjacent lines.