tTEM20AAR: a benchmark geophysical dataset for unconsolidated
ﬂuvio-glacial sediments

Neven, Alexis; Maurya, Pradip Kumar; Christiansen, Anders Vest; Renard, Philippe

doi:https://doi.org/10.5194/essd-2020-390

Preprints

https://doi.org/10.5194/essd-2020-390

Preprints

20 Jan 2021

Review status: a revised version of this preprint is currently under review for the journal ESSD.

tTEM20AAR: a benchmark geophysical dataset for unconsolidated ﬂuvio-glacial sediments

Alexis Neven¹, Pradip Kumar Maurya², Anders Vest Christiansen², and Philippe Renard^1,3 Alexis Neven et al.

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¹Centre of Hydrogeology and Geothermics, University of Neuchâtel, Neuchâtel, Switzerland
²Department of Earth Sciences, Aarhus University, Aarhus C, Denmark
³Department of Geosciences, University of Oslo, Oslo, Norway

Received: 15 Dec 2020 – Accepted for review: 18 Jan 2021 – Discussion started: 20 Jan 2021

Abstract. Quaternary deposits are complex and heterogeneous. They contain some of the most abundant and extensively used aquifers. In order to improve the knowledge of the spatial heterogeneity of such deposits, we acquired a large (more than 1400 hectares) and dense (20 m spacing) Time Domain ElectroMagnetic (TDEM) dataset in the upper Aare Valley, Switzerland. TDEM is a fast and reliable method to measure the magnetic field directly related to the resistivity of the underground. In this paper, we present the inverted resistivity models derived from this acquisition, and all the necessary data in order to perform different inversions on the processed data (https://doi.org/10.5281/ZENODO.4269887 (Neven et al., 2020)). The depth of investigation ranges between 40 to 120 m depth, with an average data residual contained in the standard deviation of the data. These data can be used for many different purposes: from sedimentological interpretation of quaternary environments in alpine environments, geological and hydrogeological modeling, to benchmarking geophysical inversion techniques.

Alexis Neven et al.

Status: final response (author comments only)

RC1:
'Comment on essd-2020-390', Anonymous Referee #1, 15 Feb 2021

General comments

Neven et al. publish what appears to be a good quality dataset that has a good potential for being adopted by the community. The geological context, acquisition parameters and procedure are well described and appropriate references are given for the reader to obtained additional information if needed. The authors are distributing the processed data as well as 1D inversion results. It is thus possible for researchers to immediately use the results, but also to extend the level of information attainable with the data by performing sophisticated inversion processes. The file formats are simple and also clearly defined, which should facilitate adoption of the dataset.

Specific comments

Are the data and methods presented new?

            Yes for the data. The methods used have been published before.

Is there any potential of the data being useful in the future?

            Yes

Are methods and materials described in sufficient detail?

            Overall, yes, but details about the method used to detect coupled structures (line 102) should be added.

Are any references/citations to other data sets or articles missing or inappropriate?

            No, except @ line 69: ref to Christiansen is incomplete

Is the data set accessible via the given identifier?

            Yes

Is the data set complete?

            Yes

Are error estimates and sources of errors given (and discussed in the article)?

            Yes, to some extent

Are the accuracy, calibration, processing, etc. state of the art?

            Yes

Are common standards used for comparison?

            N/A

Are there any inconsistencies within these, implausible assertions or data, or noticeable problems which would suggest the data are erroneous (or worse)?

            No

As the data set usable in its current format and size?

            Yes

Are the formal metadata appropriate?

            Yes

Is the length of the article appropriate?

            Overall, yes. In the conclusion, the authors mention that various so far unexplained geological structures are revealed by the new data, and I think that the paper would greatly benefit if 1-2 examples could be given.

Is the overall structure of the article well-structured and clear?

            Yes

Is the language consistent and precise?

            Yes

Are mathematical formulae, symbols, abbreviations, and units correctly defined and used?

            Overall, yes, but section 3 (Data Validation) starts directly with an equation, and a sentence should be added first to introduce the topic.

Are figures and tables correct and of high quality?

            The figures are of good quality. I think however that additional subfigures are needed. For instance, Figure 4 should also include the cross section with sharpness constraint inversion. Figure 5 should also show the results of a 1D inversion with sharpness constraint applied. A noisy sounding should also be shown, for the reader to appreciate the overall quality of the data.

Other comments

            Around line 45, the authors should add that the dataset could also be used in future studies where other geophysical methods are used, to complement the analysis by performing joint inversion.

Citation: https://doi.org/10.5194/essd-2020-390-RC1
- AC1: 'Reply on RC1', Alexis Neven, 16 Feb 2021
  
  Thanks for the positive evaluation and all the comments. We agree with the suggestions, they will improve the manuscript. All text modifications will be included in the revised version. We will also include both regularizations in all the figures presenting resistivity models and include some examples of unexplained geological structures.
  
  Citation: https://doi.org/10.5194/essd-2020-390-AC1
RC2:
'Comment on essd-2020-390', Anonymous Referee #2, 08 Mar 2021

General comments :

The dataset presented here by Neven et al. appear to be complete and of good quality. The dataset comprising both the processed data as well the 1D inversion results (two different inversions) seems really interesting. It could very well be used, as suggested by the authors, as a benchmark to test inversion algorithm in particular as plenty of additional data appears to be available on this site.

The file format and its description are clear and easy to read.

Overall, the article presented here is of very good quality.

Specific comments :

It would be interessting to see in figure 5 and/or 6, some sort of comparison of the two inversion results with sharp and smooth models as well between the conservative and standard DOI.

I understand the with TEM data, there is obviously a DOI below which the resolution is very poor (as discussed here) but the is also close to the surface a depth above which the resolution is also very poor. Did you look at that in this dataset ?

Do you consider the turn-off time to be constant ? Could it not change, even very little, when the properties of the very near surface change ?

Technical corrections :

Here are a few typing errors that should be corrected :

L19 “ conduct to “ should be "lead to"

L66 “high efficient data collection” should be changed to either highly efficient or high efficiency data collection

L69 incomplete citation

L69-70 For consistency, choose “tTEM system” or “tTEM-system” and use it everywhere

L130-132 Again with “DOI-standard” or “DOI standard”

L131 weakly instead of weekly

Table 1 Precise in the first line “Tx No of turn” to prevent any uncertainty.

Citation: https://doi.org/10.5194/essd-2020-390-RC2
- AC2: 'Reply on RC2', Alexis Neven, 12 Mar 2021
  
  Thanks for your comments. First of all, be assured that all the typos will be corrected. We will also include both regularizations and DOIs in the figures.
  
  There is indeed a depth above which the TEM is poorly sensitive, due to the turn-off time. For the acquisition parameters we used, this depth is about 2 to 3 meters. We will add a note in the text about this matter.
  
  Finally, the turn-off time of the primary field induction loop is controlled by the transmitter current and the temperature of the electronics. It doesn’t depend on the near-surface change. The system is water-cooled and regulated within a temperature limit (below 45 Celsius) so that the turn-off time doesn’t change or so minimum that it does not have any influence on early time gates. This matter is also explained in the reference tTEM paper (Auken, et al. 2018) in more detail.
  
  Citation: https://doi.org/10.5194/essd-2020-390-AC2

Alexis Neven et al.

Data sets

tTEM20AAR: a tTEM geophysical dataset Alexis Neven, Pradip Kumar Maurya, Anders Vest Christiansen, and Philippe Renard https://doi.org/10.5281/zenodo.4269887

Alexis Neven et al.

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Short summary

The shallow underground is constituted of sediments that present high spatial variability. This upper layer is the most extensively used for resource exploitation (groundwater, geothermal heat, construction materials, ...). Understanding and modeling the spatial variability of these deposits is crucial. We present a high-resolution electrical resistivity dataset that covers the upper Aare valley in Switzerland. These data can help developing methods to characterize these geological formations.


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