Articles | Volume 7, issue 1
Research article
08 Feb 2018
Research article |  | 08 Feb 2018

A remote-control datalogger for large-scale resistivity surveys and robust processing of its signals using a software lock-in approach

Frank Oppermann and Thomas Günther

Abstract. We present a new versatile datalogger that can be used for a wide range of possible applications in geosciences. It is adjustable in signal strength and sampling frequency, battery saving and can remotely be controlled over a Global System for Mobile Communication (GSM) connection so that it saves running costs, particularly in monitoring experiments. The internet connection allows for checking functionality, controlling schedules and optimizing pre-amplification. We mainly use it for large-scale electrical resistivity tomography (ERT), where it independently registers voltage time series on three channels, while a square-wave current is injected. For the analysis of this time series we present a new approach that is based on the lock-in (LI) method, mainly known from electronic circuits. The method searches the working point (phase) using three different functions based on a mask signal, and determines the amplitude using a direct current (DC) correlation function. We use synthetic data with different types of noise to compare the new method with existing approaches, i.e. selective stacking and a modified fast Fourier transformation (FFT)-based approach that assumes a 1∕f noise characteristics. All methods give comparable results, but the LI is better than the well-established stacking method. The FFT approach can be even better but only if the noise strictly follows the assumed characteristics. If overshoots are present in the data, which is typical in the field, FFT performs worse even with good data, which is why we conclude that the new LI approach is the most robust solution. This is also proved by a field data set from a long 2-D ERT profile.

Short summary
We present a new versatile datalogger that can be used remotely for a wide range of applications in geosciences such as environmental and groundwater monitoring or in applied geophysics. The recorded signals will be processed using a new software approach, which will improve the data quality for very poor signal-to-noise ratios. We show this improvement by comparing it with traditional software-algorithm-processing synthetic data sets and real data collected in the field.