Automatic filtering of ERT monitoring data in mountain permafrost

Abstract

Continuous monitoring of Electrical Resistivity Tomography (ERT) surveys can be a powerful tool for all kind of long‐term applications in the field of hydrogeophysics and cold‐region geophysics due to its high sensitivity to changes in water and ice content of the near subsurface. However, the large amount of data often calls for autonomous data processing schemes. In this study, a new filter algorithm designed to automatically detect and delete measurement errors from multiple ERT monitoring data is presented. Three successive filter steps were developed in order to eliminate technical errors, overall high‐value outliers and relative outliers within single data levels. The filter procedure is site‐independent and was tested on four different mountain permafrost sites in the Swiss Alps, representing various landforms (talus slope, rock plateau, rock glacier, bedrock). The filter performance is assessed by analysing the effect of the filter procedure on the mean apparent resistivity and on the resulting data misfit of the inversion and both, after the entire filter procedure as well as after each individual filter step. The new filter procedure is expected to yield rapid and high‐quality filtering for monitoring applications. In our study, the procedure is developed to support early detection of electrical resistivity changes associated with freezing and thawing events in permafrost conditions. The filter is applied to 128 ERT data sets from permafrost monitoring stations in Switzerland, including a four year long (2005–2008) ERT monitoring data set from the high‐mountain permafrost monitoring station Stockhorn, which serves as an illustrating example.

Resolution capacity of geophysical monitoring regarding permafrost degradation induced by hydrological processes

“…In combination with higher measurement errors in permafrost environments (up to 20 % instead of the 5 % used in the synthetic experiments; see e.g. Rosset et al, 2013) and only little a priori information about the real subsurface structures, an interpretation of 4PM results can be much more complex and difficult.…”

Effect of electrode shape on grounding resistances — Part 2: Experimental results and cryospheric monitoring

“…However, stepping down to transmission of lower currents negatively affects the signal-to-noise ratio (S/N), with implications for data quality. Several authors have argued that high grounding resistances reduced the quality of measurements (Dabas et al, 2000;Baines et al, 2002;Lundström et al, 2009;Hilbich et al, 2011;Rosset et al, 2013) using it as an indicator of data quality in some cases (Branco et al, 2013).…”

Soil Moisture Data for the Validation of Permafrost Models Using Direct and Indirect Measurement Approaches at Three Alpine Sites

“…Finally, a standard tomographic inversion scheme (Res2dinv, Loke, 2006) is applied to invert the set of measured apparent resistivities to the specific resistivities of the subsurface materials. More details regarding data processing, inversion parameter and associated uncertainties are given in Hilbich et al (2009Hilbich et al ( , 2011 and Rosset et al (2013).…”

“…There a fixed ERT profile was already installed in 1999 with manual measurements in a seasonal context, and since 2009 resistivity values are measured by A-ERT yielding temporally high-resolution data along a 60 m long survey line (Hilbich et al, 2011). In this A-ERT set-up, an automated data filtering procedure is applied to the dataset to eliminate physically implausible values, overall high-and low-value outliers as well as temporal outliers within the data series and a standard inversion scheme is applied to produce specific resistivity data (Hilbich et al, 2011;Rosset et al, 2013).…”