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Quantification of modeling errors in airborne TEM caused by inaccurate system description

机译:Quantification of modeling errors in airborne TEM caused by inaccurate system description

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摘要

Being able to recover accurate and quantitative descriptions of the subsurface electrical conductivity structure from airborne electromagnetic data is becoming more and more crucial in many applications such as hydrogeophysical and environmental mapping, but also for mining exploration. The effect on the inverted models of inaccurate system description in the 1D forward modeling of helicopter time-domain electromagnetic (TEM) data was studied. The most important system parameters needed for accurate description of the subsurface conductivity were quantified using a nominal airborne TEM system and three different reference models to ensure the generality of the conclusions. By calculating forward responses, the effect of changing the system transfer function of the nominal airborne TEM system was studied in detail. The data were inverted and the consequences of inaccurate modeling of the system transfer function were studied in the model space. Errors in the description of the transfer function influence the inverted model differently. The low-pass filters, current turn-off, and receiver-transmitter (Rx-Tx) timing issues primarily influenced the early time gates. The waveform repetition, gate integration, altitude, and geometry mainly influenced the late time gates. Depth of investigation is highly model dependent, but in general the early times hold information on the shallower parts of the model and the late times hold information on the deeper parts of the model. Amplitude, gain, and current variations affect the entire sounding and therefore the entire model. The results showed that all of these parameters should be measured and modeled accurately during inversion of airborne TEM data. If not, the output model can differ quite dramatically from the true model. Layer boundaries can be inaccurate by tens of meters, and layer resistivities by as much as an order of magnitude. In the worst cases, the measured data simply cannot be fitted within noise level.

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