# Integrated MASW and ERT Imaging for Geological Definition of an Unconfined Alluvial Aquifer Sustaining a Coastal Groundwater-Dependent Ecosystem in Southwest Portugal

^{1}

^{2}

^{3}

^{4}

^{5}

^{6}

^{7}

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^{†}

## Abstract

**:**

## 1. Introduction

## 2. Study Area

#### 2.1. Location and Climate

^{2}, has a mean elevation of 230 m a.s.l. (outlet is 2 m a.s.l. on the West and peak elevation is 290 m a.s.l. on the East), and its surface water and groundwater components flow westwards to the SAL [40]. The CSB is the main tributary to the SAL.

#### 2.2. Geological and Hydrogeological Setting

^{2}at the CSB outlet that contributes to the SAL (Figure 1b).

#### 2.3. Land and Groundwater Use

^{3}per hectare and year [42]. Irrigation water comes from upstream derivations, so the existing hand-made open wells are scarcely used. Irrigation is done through drip systems, which reduce evaporation and infiltration losses and increases salinity of irrigation return. At the west end of the study area, the SAL and some neighboring spaces (Figure 1d) were catalogued in 1993 as a Special Protection RAMSAR Area due to its ecological value for wildlife preservation [RAMSAR website: https://www.ramsar.org/wetland/portugal].

## 3. Methods

#### 3.1. Overall Framework for Data Collection

^{2}], S is the dimensionless aquifer storage coefficient or drainable porosity for unconfined aquifers, and T is aquifer transmissibility [L

^{2}T

^{−1}], which is the product of aquifer hydraulic conductivity [L T

^{−1}] and saturated thickness [L].

^{−1}; the average value of these measures was calculated for each well.

#### 3.2. MASW Surveys

^{−1}] model [27,28]. This technique allows for analyzing the fundamental and higher modes simultaneously, thus permitting to obtain more accurate VS models [25,26]. A roll-along setup with a land-streamer acquisition system was used to obtain a continuous 2D VS model. This procedure enables us to acquire data rapidly because it is not necessary to plant the geophones each time a measurement is made.

^{®}by the Kansas Geological Survey, The University of Kansas, USA. Data processing consisted of geometry edition, data filtering, muting (when needed), generation of overtones (frequency–time energy diagrams), and fundamental and higher modes (if present) identification. Finally, dispersion curves were determined and then subjected to a mathematical inversion process to obtain continuous 2D VS models. These were plotted using the triangulation with linear interpolation method, which gives good results for evenly distributed data over the mapping area.

#### 3.3. ERT Surveys

^{−1}] [29,31,38], as:

^{®}by PASI Instruments. A dipole–dipole array was the electrode disposition used since it provides good resolution both on vertical and horizontal directions. Configuration was: 6-m electrode spacing, 36-m maximum separation between dipoles, and 200 V as the input voltage applied.

^{®}by Geotomo Software, with a blocky constraint to minimize exaggeration of smooth model changes when subsurface changes are locally limited [53] and a severe reduction of side blocks effects to minimize exaggeration from robust inversion [54]. Iterations were limited to three so as not to create artifacts, since the inversion error had low reduction in third iteration comparatively to former iterations. In profiles ERT1 and ERT3, the electrode spacing unit was reduced by half (3 m) to minimize the effect of large near-surface resistivity variations, as proposed by [54]. Similarly to VS models, ER models were also plotted using the triangulation with linear interpolation method.

#### 3.4. Topographic Correction of 2D VS and ER Models

^{®}by ESRI.

## 4. Results

#### 4.1. Frequency for Geophysical Surveying

^{2}(Figure 1b), S = 0.05 [40], and T = 300 m

^{2}day

^{−1}and 800 m

^{2}day

^{−1}, measured respectively in dry and rainy phases by [10], and G varied in the 3.2–8.9-month range.

#### 4.2. Hydrogeophysical Basis for VS and ER Models Interpretation

^{−1}range are expected.

^{−1}range with typical values of 40–80 μS cm

^{−1}in the rainy phase and higher than 100 μS cm

^{−1}in the dry phase (Figure 4b). The values in the rainy phase determine the expected GEC in aquifer H since aquifer recharge from P in the dry phase is negligible (Figure 4a). Applying the monthly E-to-P ratios deduced from daily P and T time series (Figure 3c) to the atmospheric bulk deposition EC in the rainy phases, monthly GEC results in the 200–400 μS cm

^{−1}range; GER being in 25–50 Ω m range using Equation (2). In addition to this theoretical appraisal, GEC was measured in pristine (without apparent human influence) Pliocene (W1 and W6) and Pleistocene (W5) wells to corroborate this regional GEC baseline (Table 3). GEC varied in the 200–500 μS cm

^{−1}range, which is similar to the theoretical range described above, thus GER being in 20–50 Ω m range. Unfortunately, there are no inland atmospheric bulk deposition EC data, although values 0.2-fold of the magnitude reported in the coastal fringe can be assumed after the decreasing inland gradient documented by [39]. Applying this decreasing gradient to the theoretical coastal GEC and GER, inland GEC and GER can be tentatively proposed in the 40–100 μS cm

^{−1}and 100–250 Ω m ranges, respectively. This theoretical GER baseline is a reference for interpreting experimental ER models in the study area.

#### 4.3. 2D VS Models

^{−1}range. In general, VS increases in depth according to the increasing age and compaction of sediments, from less than 200 m s

^{−1}for Holocene, 200–500 m s

^{−1}for Pleistocene, to more than 500 m s

^{−1}for Pliocene formations. This vertical VS distribution correlates well with regional geological information reported by [47,48] and geotechnical data from soundings S1 to S3 [40]. The horizontal continuity of this vertical VS distribution is frequently interrupted due to sedimentary processes (e.g., lateral facies changes, erosive channels) and action of faults, which determine the accommodation space for Holocene sedimentation, as discussed in Section 5.2.

^{−1}values are attributed to aquifer H, the underlying 18 m (central valley) and 20 m (valley boundaries) thick 200 < VS < 500 m s

^{−1}to Pleistocene sands and gravels, and the deeper 5 m (central valley) and 10 m (valley boundaries) thick VS ˃ 500 m s

^{−1}to Pliocene formations (Figure 5a).

^{−1}values are attributed to aquifer H, the underlying 20 m (central valley) and 15 m (northern valley boundary) thick 200 < VS < 500 m s

^{−1}to Pleistocene sediments, and the deeper 7 m (northern valley boundary) thick VS ˃ 500 m s

^{−1}to Pliocene formations (Figure 5b). At distances 220–310 m, the Pleistocene–Pliocene boundary is not identified because it is below the prospecting depth. As in site 1, site 3 shows contrasted VS values associated to geological formations having different age and compaction. However, at the southern sector, Holocene sediments and underlying Pleistocene sand dunes show similar VS < 200 m s

^{−1}, thus limiting to identify its boundary. As described in next Section 4.4, ERT enables disambiguating the boundary of these formations.

#### 4.4. 2D ER Models

^{−1}(Figure 5b).

## 5. Discussion

#### 5.1. Performance of VS and ER Models

^{2}), percent bias (PBIAS), root-mean-square error (RMSE), RMSE relative to standard deviation of the measured data (RSR), mean absolute error (MAE), and mean relative error (MRE) were used (Table 7). Description of statistics is detailed in [66,68,69,70].

#### 5.2. The Geological Model of the Cascalheira Stream Alluvial Aquifer

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**(

**a**) Location of the study area in southwest Portugal, showing the Santo André Lagoon (SAL) and its contributing basin, the Cascalheira Stream Basin (CSB), and the Sines (SI) and Monte Velho (MV) meteorological stations. (

**b**) Geological map (scale 1:50,000) of the CSB according to [43] and direct field observations. (

**c**) Land-use units of the CSB according to [42], aerial photographs, and direct field observations. (

**d**) Holocene alluvial aquifer (aquifer H) contributing to the SAL, showing geophysical surveys performed along the main groundwater flow path at sites 1 (MASW1, ERT1), 2 (ERT2), and 3 (MASW3, ERT3), aquifer monitoring points (handmade open wells W1 to W6), geotechnical soundings S1 to S3 after [40], stream water EC measurements within the SAL Water Quality Monitoring (SWQM) Programme, and geographical features and sites cited in the text.

**Figure 2.**Conceptualization of the hydrogeological functioning of aquifer H during (

**a**) the dry phase in summer and (

**b**) predominant rainy phase in winter. Scale-out scheme after [7,46,51], and direct field observations. SAL, Santo André lagoon; LPS, low-permeability (clay-rich) lagoon sediments; ITL, inland temporary lagoon.

**Figure 3.**For natural years 1997–2019 in the SAL area, (

**a**) normalized North Atlantic Oscillation (NAO) index (purple bars) [NAO website: http://www.cpc.ncep.noaa.gov/]; (

**b**) annual precipitation (P) time series from Sines meteorological station and cumulative deviation (CD) from average annual P, mm year

^{−1}; and (

**c**) annual actual evapotranspiration (E) time series from Sines meteorological station and CD from average annual E, mm year

^{−1}. For (

**b**,

**c**), the average (0.5 percentile) of yearly data series is indicated. Vertical dotted line indicates the study year 2014.

**Figure 4.**For year 2014 in the SAL area, (

**a**) 24-h P and T distribution, after P (mm) and T (°C) records from Sines meteorological station (Figure 1a); (

**b**) monthly distribution of average precipitation-weighed EC after EC (μS cm

^{−1}) and P (mm) records from Monte Velho meteorological station (Figure 1a), and some Cascalheira stream water EC (μS cm

^{−1}) measurements at site 2 after the SWQM Programme (Figure 1d); and (

**c**) piezometry (m a.s.l.) in handmade open wells W1 to W6 (Table 3) ordered according to the monitored geological formation (Pliocene, Pleistocene, Holocene) and groundwater flow zone (recharge, transit, discharge). Vertical black dotted lines indicate geophysical surveying (Table 2) and groundwater monitoring (Table 3) as ERT (ER) and MASW (MA) surveys, and piezometry (PI). Vertical green dotted lines indicate human actions modifying water dynamics as SAL aperture (SA) and groundwater pumping for irrigation (IR).

**Figure 5.**MASW1 and MASW3 surveys at sites 1 (

**a**) and 3 (

**b**), respectively. A preliminary geological interpretation of VS values is included, showing projected (Pj) log of geotechnical sounding S1 after [40], and water table recorded in September 2014 in wells W4 (site 1) and W1 (site 3) as in Figure 4. Profiles are topographically corrected and its vertical-to-horizontal scale ratio is 1:1. CS, Cascalheira Stream; H, Holocene; Pe, Pleistocene; Pe-d, Pleistocene sand dunes; Pi, Pliocene; and F1 to F3, NW–SE strike-slip faults. The dotted-line rectangle is the area covered by time-lapse ERT surveys at sites 1 and 3.

**Figure 6.**At sites 1 (

**a**), 2 (

**b**), and 3 (

**c**), time-lapse ERT1, ERT2, and ERT3 surveys in March, June, September, and December 2014 (rows 1 to 4), and relative difference, RD = (z − z*)/z*, of nodal ER data from March, June, and December lapses (z) regarding nodal ER data from the September lapse (z*) (rows 5 to 7). The projected (Pj) logs of geotechnical soundings S2 and S3 after [40], and water table recorded in each lapse in sites 1 (well W4), 2 (well W2), and 3 (well W1) as in Figure 4 are shown; in the top left-hand corner of plots, green and red vertical arrows show, respectively, water-table raise and depletion relative to previous lapse. Preliminary geological and hydrogeological interpretations of ER models after integration of geological findings from VS models (Figure 5) and aquifer conceptualization (Figure 2) are included; red acronyms denotes groundwater types, and blue arrows and acronyms denote natural processes and human-induced actions determining transient water transferences and fluxes. Profiles are topographically corrected and its vertical-to-horizontal scale ratio is 1:1. CS, Cascalheira Stream; H, Holocene; Pe, Pleistocene; and F8, NE–SW strike-slip fault.

**Figure 7.**For VS and ER models in sites 1 to 3 (columns 1 to 5), histograms of measured (M) and predicted (P) data sets (rows 1 and 2), histograms of logarithmic M and P data sets with the fitted lognormal density functions (rows 3 and 4), and logarithm M vs. P data pairs with the 1:1 relationship. n = number of data. ±1σ = standard deviation. p = p-value from a Kolmogorov–Smirnov goodness-of-fit test.

**Figure 8.**(

**a**) Regional tectonic setting of CSB area, after [43,44,45], showing rose diagrams of faults orientation clustered by age of geological domains as in Figure 1b. (

**b**) Local tectonic setting of aquifer H after [40,43,47], direct field observations, aerial photographs, and MASW and ERT surveys, showing theoretical structural and deformation schemes, and the conjugate NW–SE and NE–SW strike-slip fault systems. (

**c**) Geological model of aquifer H, showing geological cross-sections 01 (site 1), 02 (site 2), and 03 (site 3) from integrated MASW (Figure 5) and time-lapse ERT (Figure 6) surveys at sites 1 to 3 and direct field observations, and a new geological cross-section called 00 and the southern end of the geological cross-section 03 inferred from geological mapping and direct field observation only; the vertical-to-horizontal scale ratio being 1:2.

Acronym | Definition |
---|---|

Aquifer H | Holocene alluvial aquifer |

CSB | Cascalheira Stream Basin |

CVMAE | Normalized MAE |

CVRMSE | Normalized RMSE |

CVSTD | Normalized STD |

EC | Electrical conductivity |

ER | Electrical resistivity |

ERT | Electrical resistivity tomography |

GDE | Groundwater-dependent ecosystem |

GEC | Groundwater electrical conductivity |

GER | Groundwater electrical resistivity |

lnNSE | Logarithmic form of NSE |

MAE | Mean error |

MASW | Multichannel analysis of surface waves |

MRE | Mean relative error |

NAO | North Atlantic Oscillation |

NSE | Nash–Sutcliffe efficiency coefficient |

PBIAS | Percent bias |

R^{2} | Coefficient of determination |

RD | Relative difference |

RMSE | Root-mean-square error |

RSR | RMSE relative to STD |

SAL | Santo André Lagoon |

STD | Standard deviation of the measured data |

SWQM | SAL water quality monitoring |

VS | Shear-wave velocity |

WFD | European Water Framework Directive |

Site | Profile ID ^{1} | Length, m | Prospecting Depth, m | Date |
---|---|---|---|---|

1 | MASW1 | 230 | 30 | 23 June 2014 |

ERT1 | 90 | 13 | 13 March 2014 | |

4 June 2014 | ||||

12 September 2014 | ||||

10 December 2014 | ||||

2 | ERT2 | 78 | 15 | 13 March 2014 |

4 June 2014 | ||||

12 September 2014 | ||||

10 December 2014 | ||||

3 | MASW3 | 310 | 27 | 23 June 2014 |

ERT3 | 108 | 13 | 12 March 2014 | |

3 June 2014 | ||||

10 September 2014 |

^{1}ID and location as in Figure 1d.

Site | ID ^{1} | Elevation, m a.s.l. | Aquifer and Flow Zone | Variable ^{2} | GEC ^{3} | GER ^{4} |
---|---|---|---|---|---|---|

Upstream | W6 | 35.18 | Pliocene, recharge | PL, GEC | 200 | 50 |

1 | W5 | 14.06 | Pleistocene, transit | PL, GEC | 500 | 20 |

W3 | 9.00 | Pleistocene, discharge | PL | |||

W4 | 10.07 | Holocene, recharge | PL | |||

2 | W2 | 8.82 | Holocene, transit | PL | ||

3 | W1 | 4.57 | Pliocene, discharge | PL, GEC | 393 | 25 |

^{1}ID and location as in Figure 1d.

^{2}PL is piezometric level as in Figure 4.

^{3}GEC is groundwater electrical conductivity in μS cm

^{−1}measured on 20 August 2014 when the multi-parameter probe was available; wells W3 and W4 were not accessible.

^{4}GER is groundwater electrical resistivity in Ω m after GEC reversion using Equation (2).

**Table 4.**Summary of some reference VS ranges compiled from the scientific literature for different sediments and rocks, and its equivalence to the geological materials described in the SAL area.

Geomaterial | VS, m s^{−1} | Reference | Equivalence ^{1} |
---|---|---|---|

Soft clay | 80–200 | [34] | Holocene clay |

Loose sand | 80–250 | [34] | Holocene sand |

Loose sand and gravel | 100–200 | [59] | Holocene sand and gravel |

Anthropogenic filling | 50–100 | [59] | Holocene floodplain |

Cropland and organic soil | 50–150 | [59] | Holocene floodplain |

Stiff clay | 200–600 | [34] | Pleistocene clay |

Dense sand | 150–500 | [34] | Pleistocene sand dunes |

Soft-stiff sand | 300–500 | [59] | Pleistocene sand |

Stiff gravel | 300–600 | [34] | Pleistocene conglomerate |

Cemented clay | 600–1000 | [59] | Pliocene marl |

Cemented sand | 500–900 | [59] | Pliocene calcarenite |

Cemented gravel | 500–900 | [34] | Pliocene conglomerate |

Weathered carbonate bedrock | 600–1000 | [34] | Jurassic marls |

Weathered crystalline bedrock | 800–1200 | [59] | Variscan weathered metapelites |

Hard carbonate bedrock | 1200–2500 | [34] | Jurassic carbonates |

Hard crystalline bedrock | 1500–2500 | [59] | Variscan metapelites |

^{1}Geological description and location in Figure 1c.

Profile ID ^{1} | AV VS ^{2} | SD VS ^{3} | CV VS |
---|---|---|---|

MASW1 | 273.1 | 161.4 | 0.59 |

MASW3 | 215.1 | 126.8 | 0.59 |

^{1}ID and location as in Figure 1d.

^{2}AV and SD are average and standard deviation of VS in m s

^{−1}.

^{3}CV is dimensionless coefficient of variation of VS (SD-to-AV ratio) as a fraction.

Profile ID ^{1} | Time-Lapse ^{2} | AV ER ^{3} | SD ER ^{3} | CV ER ^{4} | AV EC ^{3} | RD ER ^{5} |
---|---|---|---|---|---|---|

ERT1 | March | 44.06 | 23.06 | 0.52 | 300 | 0.043 |

June | 43.37 | 22.78 | 0.53 | 310 | 0.028 | |

September | 42.15 | 22.83 | 0.54 | 320 | 0 | |

December | 38.18 | 19.96 | 0.52 | 340 | −0.104 | |

ERT2 | March | 48.85 | 33.18 | 0.68 | 250 | 0.034 |

June | 49.00 | 31.51 | 0.64 | 250 | 0.037 | |

September | 47.17 | 37.12 | 0.79 | 270 | 0 | |

December | 45.69 | 30.78 | 0.67 | 270 | −0.032 | |

ERT3 | March | 37.34 | 20.65 | 0.55 | 580 | −0.027 |

June | 38.40 | 22.69 | 0.59 | 580 | 0.002 | |

September | 38.33 | 22.87 | 0.60 | 590 | 0 |

^{1}ID and location as in Figure 1d.

^{2}Dates are referred to year 2014 as in Table 2.

^{3}AV and SD are average and standard deviation of ER in Ω m and of EC in µS cm

^{−1}.

^{4}CV is dimensionless coefficient of variation of ER (SD-to-AV ratio) as a fraction.

^{5}RD = (z − z*)/z* is dimensionless relative difference of nodal ER data in a time-lapse ERT (z) regarding nodal ER data in the September 2014 reference time-lapse ERT (z*), as a fraction.

Statistics and Equation ^{1} | Definition, Range, and Match | Site 1 | Site 2 | Site 3 | ||
---|---|---|---|---|---|---|

VS | ER | ER | VS | ER | ||

NSE: Nash–Sutcliffe efficiency coefficient $=1-\frac{{{\displaystyle \sum}}_{i=1}^{n}{\left({D}_{mi}-{D}_{pi}\right)}^{2}}{{{\displaystyle \sum}}_{i=1}^{n}{\left({D}_{mi}-\overline{{D}_{m}}\right)}^{2}}$ | NSE indicates a perfect match between measured (M) and predicted (P) data. NSE ranges from −∞ to 1. Match is satisfactory from ˃0.7. | 0.90 | 0.98 | 0.84 | 0.90 | 0.98 |

lnNSE: logarithmic form of NSE $=1-\frac{{{\displaystyle \sum}}_{i=1}^{n}{\left(\mathrm{ln}\left({D}_{mi}\right)-\mathrm{ln}\left({D}_{pi}\right)\right)}^{2}}{{{\displaystyle \sum}}_{i=1}^{n}{\left(\mathrm{ln}\left({D}_{mi}\right)-\mathrm{ln}\left(\overline{{D}_{m}}\right)\right)}^{2}}$ | lnNSE emphasizes low values, and NSE the high ones. Match is satisfactory from ˃0.7. | 0.88 | 0.97 | 0.84 | 0.88 | 0.98 |

R^{2}: coefficient of determination$={\left(\frac{{{\displaystyle \sum}}_{i=1}^{n}\left({D}_{mi}-\overline{{D}_{m}}\right)\left({D}_{pi}-\overline{{D}_{p}}\right)}{\sqrt{{{\displaystyle \sum}}_{i=1}^{n}{\left({D}_{mi}-\overline{{D}_{m}}\right)}^{2}}\sqrt{{{\displaystyle \sum}}_{i=1}^{n}{\left({D}_{pi}-\overline{{D}_{p}}\right)}^{2}}}\right)}^{2}$ | R^{2} indicates the degree of linear relationship between M and P data. R^{2} ranges from 0 to 1. Match is satisfactory from ˃0.7. | 0.90 | 0.98 | 0.90 | 0.91 | 0.98 |

PBIAS: percent bias $=\frac{{{\displaystyle \sum}}_{i=1}^{n}\left({D}_{mi}-{D}_{pi}\right)}{{{\displaystyle \sum}}_{i=1}^{n}\left({D}_{mi}\right)}\times 100$ | PBIAS calculates the average tendency of the P data to be higher or lower than their M counterparts. The optimal value is 0. Perfect match is 0. Acceptable match is in the ±25% range. | −0.07 | 0.24 | 0.73 | 0.24 | 0.20 |

RMSE: root-mean-square error $=\sqrt{{\sum}_{i=1}^{n}{\left({D}_{mi}-{D}_{pi}\right)}^{2}}$ | RMSE calculates the precision of the P data. Perfect match is 0. Increasing RMSE values indicate that matching worse, typically due to outliers. | 11.99 | 1.61 | 1.49 | 12.89 | 2.14 |

RSR: RMSE relative to standard deviation of the measured data $=\frac{RMSE}{ST{D}_{m}}=\frac{\sqrt{{{\displaystyle \sum}}_{i=1}^{n}{\left({D}_{mi}-{D}_{pi}\right)}^{2}}}{\sqrt{{{\displaystyle \sum}}_{i=1}^{n}{\left({D}_{mi}-\overline{{D}_{p}}\right)}^{2}}}$ | RSR ranges from 0 to ∞. The lower the RSR, the lower the RMSE and the better the model performance. Acceptable match is ˂0.5. | 0.01 | 0.07 | 0.54 | 0.00 | 0.01 |

MAE: mean absolute error $=\frac{1}{n}{\sum}_{i=1}^{n}\left|{D}_{pi}-{D}_{mi}\right|$ | MAE is the absolute difference in the P and M data. Perfect match is 0. | 1.13 | 1.04 | 1.04 | 1.11 | 1.07 |

MRE: mean relative error $=\frac{1}{n}{\sum}_{i=1}^{n}\frac{\left|{D}_{pi}-{D}_{mi}\right|}{{D}_{mi}}$ | MRE is the relative difference in the P and M data. Perfect match is 0. | 0.02 | 0.01 | 0.01 | 0.02 | 0.02 |

CVMAE: normalized MAE $=\frac{MAE}{\overline{{D}_{m}}}$ | Perfect match is 0. Acceptable match is ˂0.3. | 0.01 | 0.04 | 0.04 | 0.01 | 0.04 |

CVRMSE: normalized RMSE $=\frac{RMSE}{\overline{{D}_{m}}}$ | Perfect match is 0. Acceptable match is ˂0.3. | 0.07 | 0.06 | 0.06 | 0.07 | 0.09 |

CVSTD: normalized STD $=\frac{ST{D}_{m}}{\overline{{D}_{m}}}$ | Perfect match is 0. Acceptable match is ˂0.3. | 0.01 | 0.06 | 0.05 | 0.01 | 0.08 |

n | 240 | 63 | 51 | 320 | 81 | |

MIN_{m} | 102.00 | 15.28 | 29.04 | 113 | 6.33 | |

MIN_{p} | 79.25 | 15.85 | 30.12 | 80.01 | 75.90 | |

MAX_{m} | 948.00 | 98.17 | 51.93 | 831.00 | 6.48 | |

MAX_{p} | 1015.61 | 96.88 | 49.48 | 867.02 | 72.24 | |

$\overline{{D}_{m}}$ | 236.67 | 33.11 | 37.61 | 192.46 | 24.73 | |

$\overline{{D}_{p}}$ | 237.60 | 32.82 | 36.64 | 190.03 | 24.57 |

^{1}Over the exploration space (a given geophysical profile), n is total number of data, MIN

_{m}and MAX

_{m}are minimum and maximum measured (M) values, MIN

_{p}and MAX

_{p}are minimum and maximum predicted (P) values, and $\overline{{D}_{m}}$ and $\overline{{D}_{p}}$ are mean of the M and P data sets. ${D}_{mi}$ and ${D}_{pi}$ are M and P value at observation point i. RMSE, MAE, MIN

_{m}, MIN

_{p}, MAX

_{m}, MAX

_{p}, $\overline{{D}_{m}}$, and $\overline{{D}_{p}}$ are in Ω m for ER models, and in m s

^{−1}for VS models. NSE, lnNSE, R

^{2}, RSR, and MRE are dimensionless. PBIAS, CVMAE, CVRMSE, and CVSTD are dimensionless, as a fraction.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Paz, M.C.; Alcalá, F.J.; Medeiros, A.; Martínez-Pagán, P.; Pérez-Cuevas, J.; Ribeiro, L.
Integrated MASW and ERT Imaging for Geological Definition of an Unconfined Alluvial Aquifer Sustaining a Coastal Groundwater-Dependent Ecosystem in Southwest Portugal. *Appl. Sci.* **2020**, *10*, 5905.
https://doi.org/10.3390/app10175905

**AMA Style**

Paz MC, Alcalá FJ, Medeiros A, Martínez-Pagán P, Pérez-Cuevas J, Ribeiro L.
Integrated MASW and ERT Imaging for Geological Definition of an Unconfined Alluvial Aquifer Sustaining a Coastal Groundwater-Dependent Ecosystem in Southwest Portugal. *Applied Sciences*. 2020; 10(17):5905.
https://doi.org/10.3390/app10175905

**Chicago/Turabian Style**

Paz, Maria Catarina, Francisco Javier Alcalá, Ana Medeiros, Pedro Martínez-Pagán, Jaruselsky Pérez-Cuevas, and Luís Ribeiro.
2020. "Integrated MASW and ERT Imaging for Geological Definition of an Unconfined Alluvial Aquifer Sustaining a Coastal Groundwater-Dependent Ecosystem in Southwest Portugal" *Applied Sciences* 10, no. 17: 5905.
https://doi.org/10.3390/app10175905