2.3 Electrical resistivity tomography (ERT) 2D and pseudo-3D
The geophysical surveys were designed to provide the best possible
understanding of subsurface hydrodynamics, i.e. , groundwater
behavior and infiltration zone architecture. This technique is rarely
applied in wetlands because the equipment is difficult to install in the
field (long power cables and batteries). Therefore, electrical
tomography was performed to estimate the flow direction of groundwater,
showing the direction of the superficial water flow and a horizontal
infiltration pattern in the center of the concavity (compartment 3).
The ERT technique involves the DC Resistivity method for geophysical
acquisition data, measuring the electric resistivity parameter using
electrodes fixed on the surface of the ground, connected to the
measuring instrument by cable assembly (Keller & Frischknecht, 1966;
Telford, Geldart & Sheriff, 2004).
The data was acquired using the Schlumberger arrangement, with 2 m
between each iron electrode. We assembled nine 80-meter lines in the
direction N45°W, spaced 10 meters apart, and one 80-m line perpendicular
to all the others (Figure 5).
For data acquisition, we used the Terrameter LS (ABEM Instrument,
Sweden), which can measure resistivity from a single pre-programmed
module for transmitting and receiving automated signals, with 250 W, 1
µV resolution, and a maximum current of 2.5 A (ABEM 2012). This
equipment was calibrated to the following specifications: transmission
of 200 mA for 1s for each measurement. During acquisition, the data were
stored in the equipment’s internal memory, then exported via USB
interface as DAT file format compatible with the input format of word
processing software.
The data were processed and manipulated to obtain a two-dimensional
electrical profile and three-dimensional models of resistivity (Ω.m).
All data were processed in the Res2Dinv software, which automatically
performs the following tasks: (1) determining a two-dimensional (2D)
model of resistivity of the subsurface, using data obtained through
electrical imaging surveys (Griffiths & Barker, 1993); (2) quickly
generating 2D inversion models for DC Resistivity, using the algorithm
of smoothness constrained least-square method; and (3) performing
topographic modeling when reversing the data set, which incorporates the
topography within the mesh model (Geotomo Software 2003). The
topographic data was corrected by the GNSS in field.
The Geotomo Software applies the smooth inversion method, which is the
mathematical method of least squares that uses the optimization
Gauss–Newton and quasi-Newton, described by Loke & Barker (1996). This
method is based on cells; the software recognizes the terrain surface as
rectangular blocks, with constant values for each parameter studied
(DeGroot-Hedlin & Constable, 1990). The results are 2D models of
inversion of resistivity data, based on distance (length of the
acquisition lines) and depth, and presented in logarithmic graphic scale
and intervals of color interpolation values.
The numeric data of the 2D inversion for each compartment were assembled
in a single spreadsheet, compiling the positions of readings along the
lines (variable x), spacing between the lines (variable y), depth
modeled by inversion (variable z), and the value of electrical
resistivity (R), which is subsequently used to generate the pseudo-3D
visualization models. A pseudo-3D model is a method used for rendering
visual-data with a sense of depth (Z axis).
The pseudo-3D model was processed in the Oasis Montaj platform, where
the 2D data obtained in the Res2Dinv software were interpolated using
the minimum curvature method to generate pseudo-3D visualization models.
The models can be visualized as layers, according to the chosen depth.
Visualization of pseudo-3D models generated from geophysical data
contributes to our understanding of complex geological structures and
hydrological problems. This methodology has many environmental
applications and is described in other studies (Moreira et al., 2012,
2016a, b, 2017, 2018; Veloso, Moreira & Cortes, 2015; Cortes, Moreira
& Veloso, 2016; Vieira et al. 2016; Helene, Moreira & Carrazza, 2016;
Carrazza, Moreira & Helene, 2016).