DATA of "Resolving the 2D temporal evolution of subglacial water flow with dense seismic array observations."

The data set contains all data presented in the paper: <strong>Observing the subglacial hydrology network and its dynamics with a dense seismic array</strong> published in PNAS ( https://doi.org/10.1073/pnas.2023757118 ) See our online presentation of this dataset: https://meetingorganizer.copernicus.org/EGU2020/EGU2020-10710.html. The present data and code concerns the source location obtained with matched-field-processing analysis and the hydraulic potential calculation (Shreve, R. L. Movement of Water in Glaciers. <em>J. Glaciol.</em> <strong>11</strong>, 205–214 (1972)). We perform source location over 1-sec long signal segment of the vertical component only. We filter the signal within the [3-7] Hz frequency range and coherently apply the MFP each 0.1 Hz within this range. To maximize our algorithm efficiency and minimize computational costs we use a gradient-based minimization algorithm (Nelder-Mead optimization) to converge to the best match between the trial and the observed phase delays rather than an exhaustive grid-search exploration. The convergence criterion is reached when the variance of values obtained over the last 5 iterations of the optimization is smaller than 1e^-2 with a maximum of 3000 iterations. Our 29 different starting points used for optimization are located 250 m below the glacier surface and they uniformly cover an area of 800 x 800 m² centered on the array. We set the initial velocity to 1800 m.sec ^-1. The 29 punctual locations found per signal segment (1 sec) after convergence are located all in the same place if a clear global convergence exists (i.e. high MFP output) or at up to 29 different locations if up to 29 local minima exist (i.e. low MFP output). Timeseries of physical quantities can be found here https://doi.org/10.5281/zenodo.3701520 Spatial observations acquired during the same period can be found here https://doi.org/10.5281/zenodo.3971815 The RESOLVE project has been supported by a grant from LabEx OSUG@2020 (Investissement d’avenir – ANR10LABX56) and by the IDEX Université Grenoble Alpes. Most of the computations presented in this paper were performed using the GRICAD infrastructure (https://gricad.univ-grenoble-alpes.fr), which is supported by Grenoble research communities, and with the CiGri tool (https://github.com/oar-team/cigri) developed by Gricad, Grid5000 (https://www.grid5000.fr) and LIG (https://www.liglab.fr/). You can find more information on the method and seismic dataset used in this paper here: https://zenodo.org/deposit/5645545