Seismic and Electrical Resistivity Tomography of Lagoon Pingo, Svalbard in 2019

• Main Authors: Hammock, Craig P., Kulessa, Bernd
• Format: Dataset
• Language: English
• Published: Zenodo 2021
• Subjects: pingo; electrical resistivity tomography; seismic reflection; seismic refraction; permafrost; Arctic; Svalbard; Norway; Tromsø; Davidson; Arctic Gas Hydrate, Environment and Climate; Norwegian Polar Institute; open system Pingo; UNIS; University of Tromsø
• Online Access: https://dx.doi.org/10.5281/zenodo.4593831; https://zenodo.org/record/4593831

This mixed dataset contains raw and processed seismic and electrical resistivity tomography (ERT) data from Lagoon Pingo, Svalbard. Lagoon Pingo is a coastal open-system pingo with year-round methane release located in a glacio-isostatically uplifting fjord-valley. This dataset arises from our geophysical investigations of Lagoon Pingo, which aims to characterise and understand how pingo structure and composition facilitates methane release through continuous permafrost. Two active source seismic profiles were collected using two Geometrics Geode 24-channel seismographs between 21/08/2019 and 29/08/2019, which together cover the longest lateral extent of the open-system pingo. Seismic profiles consisted of 48 electrodes spaced at 4 m intervals, with shots taken at 4 m intervals between electrodes and up to 100 m off-end using a sledgehammer on a metal baseplate. This is complemented by 5 ERT profiles taken between 05/04/2019 and 10/04/2019, collected using an ABEM SAS1000 Terrameter, with short profiles containing 21 electrodes covering the width of the pingo, and longer profiles of 53 electrodes covering the longest lateral extent. Data were collected using a Wenner configuration and automatic selection of input current. Dataset Contents This dataset contains the following data: Raw seismic shots taken from Lagoon Pingo in SEG-2 format ReflexW files for the seismic tomography and wavefront inversion derived from the first onset of these single shots The processed common offset gather (a.k.a. "supergather") derived from these shots Raw, unfiltered ERT data in ASCII format Inverted ERT profiles, as obtained following inversion in ResIPy, with files in ASCII and .vtk format File Structure Seismics Raw S1 – 71 folders with one for each shotpoint, each containing 10 .SG2 SEG-2 files (710 files total) S2 – 71 folders with one for each shotpoint, mostly containing 10 .SG2 SEG-2 files (some folders have a slightly different number of shots due to miscounting when taking the data; 718 files total) A separate readme file (markdown format: .md), which provides information regarding filenames and their relative locations. Processed Seismic Refraction Tomography – 1 folder (containing 5 files) and 7 separate files) Traveltimes – 1 ASCII file. Wavefront Inversion – 4 ASCII files Supergather – 1 ReflexW processed file ERT Raw – each folder (5 total) pertains to one profile, with each containing the following (20 files in 5 folders): 2 csv files highlighting electrode and finer resolution (2.5 m) topography 2 ASCII files with ERT data – normal and reciprocal for each profile. Inverted – each folder (5 total) pertains to one profile, with each containing the following (5 x 11 files; 55 total files): 2 .vtk files which allow visualisation of the inversion and relative location of the electrodes respectively in an open-source program such as Paraview An ASCII file called electrodes.dat, which provides the line topography used in the profile 3 ASCII files, called “f001_err.dat”, “f001_res.dat” and “f001_sen.dat”, which provide the error, resistivity and sensitivity data of the final inversion respectively An ASCII file called mesh.dat, which provides the mesh constraints used in the final inversion An ASCII file called protocol.dat, which provides the measurement configuration for data used in the inversion An ASCII file called R2.in, which provides the parameterisation used for the inversion An ASCII file called R2.out, which provides the output log from the inversion An ASCII file called res0.dat, which provides the parsed data input for the inversion The total size of the repository is approximately 1.12 GB. Methodology Seismic data were acquired using two Geometrics Geode 24 Channel seismographs in profiles that consisted of 48 100 Hz geophones at 4 m spacings. Zero offset shots were gathered using a sledgehammer source on a metal baseplate at 4 m intervals between geophones and up to 10 m off-end, with additional shots taken at 10 m up to 100 m off the end of the profile. Seismic data were processed in REFLEXW version 9.5 (Sandmeier, 2020). First, shot geometry was adjusted according to GPS points taken using a Garmin eTrex handheld GPS at c. 5 m accuracy, and topography obtained from a 2010 summer DEM (Norwegian Polar Institute, 2014). Second, traces that were exceptionally noisy or occurred due to dead electrodes were removed. The common offset gather was produced using 4 m spaced shots through the following steps: Adjustment of traceheaders to use relative geometry, and the merging of all shotgathers from both lines into a single profile; A resorting of shots according to offset between source and receiver; Splitting the merged file according to common offsets between traces; Stacking the split profiles containing common offsets, to provide a singular trace for each offset value; A recombination of all common offset traces; and a resorting of this according to the traceheader to provide a common offset gather; And lastly, divergence compensation for display. For seismic refraction: First onsets were obtained for each shotpoint within the 4 m spaced shots; Traveltime analysis was conducted as per standard REFLEXW procedure (Sandmeier, 2020); First arrivals were assigned, and the wavefront inversion (which uses a finite difference calculation) and tomography (which uses the Simultaneous Inversion Reconstruction Algorithm) modules of the REFLEXW software were initiated. The tomographic inversion was parameterised following initial seismic refraction analysis of the common offset gather as below: Space increment: 0.5 Maximum number of iterations: 20 Maximum time: 10800 seconds Threshold: 0.001 Model change A: 1 Model change B: 0.1 Convergence search: 5 Max def. change (%): 200 Def. data-variance: 0.01 Maximum beam width: 10 Start curved ray: 1 Average x smoothing: 2 Average z smoothing: 0 Minimum velocity: 1 Maximum velocity: 4000 Electrical resistivity tomography data were acquired using an ABEM SAS1000 Terrameter, through profiles of two different lengths: shorter transects consisting of 21 electrodes with 5 m spacing, and longer profiles of 53 electrodes with 5 m spacing. Wenner array configurations were used owing to variable and uncertain field site conditions, and input current was automatically determined by the instrument. To counteract electrode contact issues in frozen conditions, a saline solution was poured onto each electrode before measurements were made. The location of selected electrodes were taken with a Garmin eTrex handheld GPS at c. 5 m accuracy, and topography was obtained by interpolating these points to provide a location for each electrode, and deriving elevation from a 2020 Structure-from-Motion DEM (Hann and Dachauer, 2020). The inversion of data was completed using ResIPy version 3.3 (Blanchy et al., 2020). Within ResIPy, the following process was used: Import of data and parsing from ASCII format to that recognisable by ResIPy, and import of the calculated profile topography; Filtering of data, using a 10% threshold between normal and reciprocal measurements; Selection and fitting of an error model, with linear models used for profiles composed of 21 electrodes and power-law models for those composed of 53 electrodes. The construction of a quadrilateral mesh within the software, using a horizontal resolution of 2 cells per electrode and default parameters elsewhere; Inversion using the following parameterisation: Inversion type: Regularized Inversion with Quadratic Filtering [2] Target decrease: 0 Data type: Logarithmic [1] Regularization mode: Normal [0] Value for tolerance: 1.0 Maximum number of iterations: 30 a_wgt, b_wgt: 0, 0 Minimum apparent resistivity: -10e10 Maximum apparent resistivity: 10e10 Maximum beam width: 10 Flux Type: 3D Value for res_matrix: Sensitivity matrix [1] Patch size x, y: 1, 1 Update the weights: Recommended [2] Value for alpha_aniso: 1 Where inverted data points occurred outside of a ±3% threshold, these points were removed and data subsequently reinverted using identical parameters. Spatial Extent For the seismic dataset, topographic information is included within the fileheader for each shot. For the ERT dataset, the location of each electrode is presented within the electrodes topography csv for each profile. For both datasets, co-ordinates are displayed in UTM 33N (ESPG: 25833). : This data has been uploaded in support of a paper submission to Journal of Geophysical Research: Earth Surface. Acknowledgements: Fieldwork was supported by grants from the Swansea University College of Science Research Fund 2018/19 and the Near Surface Geophysics Group of the Geological Society of London's Postgraduate Fieldwork Fund, in addition to support by the Research Council of Norway through its Centres of Excellence funding scheme (project no: 223259). Fieldwork was conducted within the CLIMAGAS project, funded by the Research Council of Norway (project no: 294764). We would like to thank Sara Mollie Cohen and the UNIS logistics team for the loan of the ABEM SAS1000 Terrameter and for support in the field. Thanks to Richard Hann and Armin Dachauer for acquiring and providing a Structure-from-Motion DEM of Lagoon Pingo (Hann and Dachauer, 2020). Fieldwork assistance by Emma Ciric, James Davidson, Veerle van Winden, Viktor Kröger, Will Hartz, Mikkel Toft Hornum and Naomi Ochwat are gratefully acknowledged. CPH is funded by a postgraduate scholarship awarded by the College of Science, Swansea University and by the Centre for Arctic Gas Hydrate, Environment and Climate, University of Tromsø (grant number: 223259). : {"references": ["Blanchy, G., Saneiyan, S., Boyd, J., McLachlan, P., & Binley, A. (2020). ResIPy, an intuitive open source software for complex geoelectrical inversion/modeling. Computers & Geosciences, 137, 104423. https://doi.org/10.1016/j.cageo.2020.104423", "Hann, R. and Dachauer, A. (2020). Drone-based mapping of the Lagoon Pingo in Svalbard. DataverseNO Version 1. https://doi.org/10.18710/IMPEG8", "Norwegian Polar Institute (2014). Terrengmodell Svalbard (S0 Terrengmodell) [Data set]. Norwegian Polar Institute. https://doi.org/10.21334/npolar.2014.dce53a47", "Sandmeier, K. J. (2020). REFLEXW Version 9.5. Retrieved online: https://www.sandmeier-geo.de/Download/reflexw_manual_a4.pdf. Date accessed: 25th October 2020."]}