Using Pole–Pole Measurements to Calculate Arbitrary 4-Point Configurations—An Approach to Make Electrical Resistivity Tomography More Flexible and Time-Efficient

Abstract

Electrical resistivity tomography (ERT) is nowadays widely used in archaeological prospection. This study deals with an approach to make ERT more time-efficient and flexible. It is based on calculating arbitrary 4-point configurations by superposition of multiple pole–pole measurements. Investigating its applicability for archaeological purposes is the objective of this work. To do so, a synthetic study and a case study are conducted to gain insights into effects of nonideal field conditions, noise susceptibility and other challenges during processing and interpretation. Remains of an early modern manor in Noer served as an exemplary object of investigation. Their high resistivity contrast in relation to the surrounding soil makes them ideal for a functionality test. Beforehand, ground penetrating radar measurements were carried out to constrain the forward model used in the synthetic study. It turns out that the pole–pole conversion is well applicable for archaeological prospection under some conditions. The synthetic study shows that the approach is relatively prone to systematic errors. Therefore, it is recommended to locate the external electrodes at a distance of at least 0.7 times (preferably 1.7 times) the maximum internal electrode spacing from the area of investigation. Other error sources like nonideal electrode coupling must be excluded to keep relative noise levels below 1%. The pole–pole conversion can be considered reliable for absolute noise levels below 0.3 mV. Therefore, an A/D converter resolution of, for example, 16-bit should be sufficient for a dynamic range of ±10 V. If all conditions are met, the pole–pole conversion has a great potential to make ERT more time-efficient (up to 50%, depending on configuration sets) and flexible, as it allows to calculate nearly every arbitrary 4-point configuration in the given setup. Combined with optimization approaches like the ‘Compare R’ method, data sets can also be adapted for specific (archaeological) questions or conditions.