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4 results for “Inverse theory”
Dataset: Mapping intrinsic and scattering attenuation in the southern Aegean crust using S-wave envelope inversion and sensitivity kernels derived from perturbation theory
<p><strong>Data Set S1: </strong>File “ds01.csv” contains the catalogue of relocated events used in this study. The columns in the file represent origin time (in year-month-day’H’hour’M’minute’S’seconds format), event longitude, event latitude, event depth in a sequential manner.</p> <p><strong>Data Set S2: </strong>File “ds02.zip” contains four ASCII data files (ray_prmtrs12.txt, ray_prmtrs24.txt, ray_prmtrs48.txt and ray_prmtrs816.txt). The data files contain scattering coefficient (<em>g<sup>*</sup></em>) and intrinsic coefficient (<em>b</em>) values in 1-2, 2-4 Hz, 4-8 Hz and 8-16 Hz bands respectively. The columns in the text files represent event latitude, event longitude, event depth, station latitude, station longitude, station velocity, envelope duration, <em>g<sup>*</sup></em>, <em>b</em>, early-S window length, percentage error in early-S window, percentage error for full envelope, and event origin time in a sequential manner.</p> <p><strong>Data Set S3: </strong>File “ds03.zip” contains four data files (envnodes15g_3_3_1-2.txt, envnodes15g_3_3_2-4.txt, envnodes15g_3_3_4-8.txt, and envnodes15g_3_3_8-16.txt), one BASH script containing GMT and Octave commands (Qs_envg.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above.</p> <p><strong>Data Set S4: </strong>File “ds04.zip” contains four data files (envnodes15b_3_3_1-2.txt, envnodes15b_3_3_2-4.txt, envnodes15b_3_3_4-8.txt, and envnodes15b_3_3_8-16.txt), one BASH script containing GMT and Octave commands (Qi_env.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above.</p> <p><strong>Data Set S5: </strong>File “ds05.zip” contains four data files (envnodes15a_3_3_1-2.txt, envnodes15a_3_3_2-4.txt, envnodes15a_3_3_4-8.txt, and envnodes15a_3_3_8-16.txt), one BASH script containing GMT and Octave commands (albd_env.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain Albedo (<em>B<sub>o</sub></em>) as % values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and <em>B<sub>o</sub></em> value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of <em>B<sub>o</sub></em> using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above. </p>
Dataset for "New Perspectives for Nonlinear Depth-inversion of the Nearshore Using Boussinesq Theory"
<pre>This dataset gathers all cross-spectral, spectral and bispectral data produced and used in the manuscript accepted for publication in Geophysical Research Letters: "New Perspectives for Nonlinear Depth-inversion of the Nearshore Using Boussinesq Theory", by K. Martins, P. Bonneton, O. de Viron, I. L. Turner, M. D. Harley and K. Splinter The sharing of the processed data is motivated by research reproductibility purposes and with the hope that it will foster efforts in improving the newly-proposed depth-inversion procedure. Three laboratory experiments are considered, namely the experiments reported in van Noorloos (2003), Michallet et al. (2011) and GLOBEX (e.g., see Ruessink et al., 2013). The processed data is organised in separate self-explanatory .mat files (generated with MATLAB software), gathering: - "cel_data" : wave phase velocities obtained from cross-spectral and cross-correlation analyses between adjacent wave gauges - "bulk_data" : range of bulk wave parameters computed across the different wave flumes - "bispectrum_data" : spectral and bispectral estimates computed across the different wave flumes The scripts for performing the depth-inversion as well as a for plotting the results are provided. The new Boussinesq depth-inversion procedure relies on the function "fun_compute_krms_terms.m", which is provided and originates from the bispectral analysis library accessible from the first author GitHub repository at https://github.com/ke-martins/bispectral-analysis. Acknowledgements: Kévin Martins greatly acknowledges the financial support from the European Union's Horizon 2020 research and innovation program under the Marie Skodowska-Curie Grant Agreement 887867 (lidBathy). We warmly thank Ap van Dongeren and Hervé Michallet for providing the raw data for the experiments described in van Noorloos (2003) and Michallet et al. (2011), respectively. The raw data from GLOBEX used in this research can be accessed on Zenodo at https://zenodo.org/record/4009405 and can be used under the Creative Commons Attribution 4.0 International license. The GLOBEX project was supported by the European Community’s Seventh Framework Programme through the Hydralab IV project, EC Contract 261520. References: Michallet, H., Cienfuegos, R., Barthélemy, E., & Grasso, F. (2011). Kinematics of waves propagating and breaking on a barred beach. European Journal of Mechanics - B/Fluids, 30 (6), 624 – 634. doi: 10.1016/j.euromechflu.2010.12.004 Ruessink, G. B., Michallet, H., Bonneton, P., Mouazé, D., Lara, J. L., Silva, P. A., & Wellens, P. (2013). GLOBEX: Wave dynamics on a gently sloping laboratory beach. In Coastal Dynamics ’13: Proceedings of the Seventh Conference on Coastal Dynamics, Arcachon, France. van Noorloos, J. C. (2003). Energy transfer between short wave groups and bound long waves on a plane slope (Master’s thesis, Delft University of Technology, Delft, The Netherlands). Retrieved from http://resolver.tudelft.nl/uuid:13616ff0-407d-43de-9954-ba707cd40d27</pre>
Application of inverse theory for high spatial resolution reconstructions of thermospheric vector wind fields from Doppler shifts measured by a ground-based network of all-sky Fabry-Perot interferometers
<p>Several types of all-sky viewing Fabry-Perot Interferometers (FPI) have been developed since the 1990s for ground-based remote sensing of thermospheric winds. The Scanning Doppler Imager (SDI) is one such instrument, which provides temporally simultaneous line-of-sight observations from hundreds of independent look directions per instrument exposure. A geographically distributed network of such instruments increases spatial coverage and, at many locations, also provides overlapping observations along multiple independent lines-of-sight. Together, these characteristics significantly increase the density and fidelity that is possible for reconstructed thermospheric vector wind fields, compared to a traditional narrow-field FPI, but at the cost of complexity and difficulty.<br><br></p> <p>Presently, we describe an application of inverse theory to reconstruct three-component vector thermospheric neutral wind fields using data from multiple SDI instruments. The salient features of the method used here are the ability to reconstruct three-component winds on a dense grid that is sampled regularly in latitude, longitude, and time, without assuming any a-priori underlying structure of the winds. This requires solving an inverse problem that does not in general yield a unique solution unless additional constraints are enforced. We describe this step, also known also as regularization, along with the strategy used to maximize the spatial resolution of the derived wind fields by automatically determining the minimum level of regularization that can produce stable inversions. We present example results obtained from applying this technique to one night of data from a network of SDIs in Alaska, and discuss the implications of these results for current understanding of thermospheric dynamics.</p>
Application of inverse theory for high spatial resolution reconstructions of thermospheric vector wind fields from Doppler shifts measured by a ground-based network of all-sky Fabry-Perot interferometers
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