Articles | Volume 15, issue 3
https://doi.org/10.5194/essd-15-1389-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-1389-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
24 Mar 2023
Data description paper |
| 24 Mar 2023
DL-RMD: a geophysically constrained electromagnetic resistivity model database (RMD) for deep learning (DL) applications
Muhammad Rizwan Asif
CORRESPONDING AUTHOR
Hydro-Geophysics Group (HGG), Department of Geoscience, Aarhus
University, Aarhus C, 8000, Denmark
Department of Electrical and Computer Engineering, Aarhus University, Aarhus N, 8200, Denmark
Aarhus University Centre for Water Technology (WATEC), Aarhus University, Aarhus C, 8000, Denmark
Nikolaj Foged
Hydro-Geophysics Group (HGG), Department of Geoscience, Aarhus
University, Aarhus C, 8000, Denmark
Aarhus University Centre for Water Technology (WATEC), Aarhus University, Aarhus C, 8000, Denmark
Thue Bording
Hydro-Geophysics Group (HGG), Department of Geoscience, Aarhus
University, Aarhus C, 8000, Denmark
Aarhus GeoInstruments, Åbyhøj, 8230, Denmark
Jakob Juul Larsen
Department of Electrical and Computer Engineering, Aarhus University, Aarhus N, 8200, Denmark
Aarhus University Centre for Water Technology (WATEC), Aarhus University, Aarhus C, 8000, Denmark
Anders Vest Christiansen
Hydro-Geophysics Group (HGG), Department of Geoscience, Aarhus
University, Aarhus C, 8000, Denmark
Aarhus University Centre for Water Technology (WATEC), Aarhus University, Aarhus C, 8000, Denmark
Related authors
No articles found.
Hafsa Mahmood, Ty P. A. Ferré, Raphael J. M. Schneider, Simon Stisen, Rasmus R. Frederiksen, and Anders V. Christiansen
EGUsphere, https://doi.org/10.5194/egusphere-2023-1872, https://doi.org/10.5194/egusphere-2023-1872, 2023
Short summary
Short summary
Temporal drain flow dynamics and understanding of their underlying controlling factors are important for water resource management in tile-drained agricultural areas. This study examine whether simpler, more efficient machine learning (ML) models can provide acceptable solutions compared to traditional physics based models. We predicted drain flow time series in multiple catchments subject to a range of climatic and landscape conditions.
Mathias Vang, Denys Grombacher, Matthew P. Griffiths, Lichao Liu, and Jakob Juul Larsen
Hydrol. Earth Syst. Sci., 27, 3115–3124, https://doi.org/10.5194/hess-27-3115-2023, https://doi.org/10.5194/hess-27-3115-2023, 2023
Short summary
Short summary
In this paper, we use a novel surface nuclear magnetic resonance (SNMR) method for rapid high-quality data acquisition. The SNMR results from more than 100 soundings in three different case studies were used to map groundwater. The soundings successfully track the water table through the three areas and are compared to boreholes and other geophysical measurements. The study highlights the use of SNMR in hydrological surveys and as a tool for regional mapping of the water table.
Nikhil B. Gaikwad, Lichao Liu, Matthew P. Griffiths, Denys Grombacher, and Jakob Juul Larsen
Geosci. Instrum. Method. Data Syst. Discuss., https://doi.org/10.5194/gi-2023-5, https://doi.org/10.5194/gi-2023-5, 2023
Preprint under review for GI
Short summary
Short summary
The work presents simulations, modelling, and experimental verification of a novel steady-state surface NMR transmitter used for the non-invasive exploration of groundwater. The paper focuses on three main aspects of high current transmitter instrumentation, i.e., thermal management, current drooping, and pulse stability. This work will interest readers in geoscientific instrument prototyping for groundwater exploration using portable geoscientific instrument.
Hilary A. Dugan, Peter T. Doran, Denys Grombacher, Esben Auken, Thue Bording, Nikolaj Foged, Neil Foley, Jill Mikucki, Ross A. Virginia, and Slawek Tulaczyk
The Cryosphere, 16, 4977–4983, https://doi.org/10.5194/tc-16-4977-2022, https://doi.org/10.5194/tc-16-4977-2022, 2022
Short summary
Short summary
In the McMurdo Dry Valleys of Antarctica, a deep groundwater system has been hypothesized to connect Don Juan Pond and Lake Vanda, both surface waterbodies that contain very high concentrations of salt. This is unusual, since permafrost in polar landscapes is thought to prevent subsurface hydrologic connectivity. We show results from an airborne geophysical survey that reveals widespread unfrozen brine in Wright Valley and points to the potential for deep valley-wide brine conduits.
Pradip Kumar Maurya, Frederik Ersted Christensen, Masson Andy Kass, Jesper B. Pedersen, Rasmus R. Frederiksen, Nikolaj Foged, Anders Vest Christiansen, and Esben Auken
Hydrol. Earth Syst. Sci., 26, 2813–2827, https://doi.org/10.5194/hess-26-2813-2022, https://doi.org/10.5194/hess-26-2813-2022, 2022
Short summary
Short summary
In this paper, we present an application of the electromagnetic method to image the subsurface below rivers, lakes, or any surface water body. The scanning of the subsurface is carried out by sailing an electromagnetic sensor called FloaTEM. Imaging results show a 3D distribution of different sediment types below the freshwater lakes. In the case of saline water, the system is capable of identifying the probable location of groundwater discharge into seawater.
M. Andy Kass, Esben Auken, Jakob Juul Larsen, and Anders Vest Christiansen
Geosci. Instrum. Method. Data Syst., 10, 313–323, https://doi.org/10.5194/gi-10-313-2021, https://doi.org/10.5194/gi-10-313-2021, 2021
Short summary
Short summary
We have developed a towed magnetic gradiometer system for rapid acquisition of magnetic and magnetic gradient maps. This high-resolution system is flexible and has applications to utility detection, archaeology, unexploded ordnance, or any other applications where high-resolution maps of the magnetic field or gradient are required. Processing of the data has been simplified as much as possible to facilitate rapid results and interpretations.
Krista F. Myers, Peter T. Doran, Slawek M. Tulaczyk, Neil T. Foley, Thue S. Bording, Esben Auken, Hilary A. Dugan, Jill A. Mikucki, Nikolaj Foged, Denys Grombacher, and Ross A. Virginia
The Cryosphere, 15, 3577–3593, https://doi.org/10.5194/tc-15-3577-2021, https://doi.org/10.5194/tc-15-3577-2021, 2021
Short summary
Short summary
Lake Fryxell, Antarctica, has undergone hundreds of meters of change in recent geologic history. However, there is disagreement on when lake levels were higher and by how much. This study uses resistivity data to map the subsurface conditions (frozen versus unfrozen ground) to map ancient shorelines. Our models indicate that Lake Fryxell was up to 60 m higher just 1500 to 4000 years ago. This amount of lake level change shows how sensitive these systems are to small changes in temperature.
Alexis Neven, Pradip Kumar Maurya, Anders Vest Christiansen, and Philippe Renard
Earth Syst. Sci. Data, 13, 2743–2752, https://doi.org/10.5194/essd-13-2743-2021, https://doi.org/10.5194/essd-13-2743-2021, 2021
Short summary
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, etc.). 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 develop methods to characterize these geological formations.
Rasmus Bødker Madsen, Hyojin Kim, Anders Juhl Kallesøe, Peter B. E. Sandersen, Troels Norvin Vilhelmsen, Thomas Mejer Hansen, Anders Vest Christiansen, Ingelise Møller, and Birgitte Hansen
Hydrol. Earth Syst. Sci., 25, 2759–2787, https://doi.org/10.5194/hess-25-2759-2021, https://doi.org/10.5194/hess-25-2759-2021, 2021
Short summary
Short summary
The protection of subsurface aquifers from contamination is an ongoing environmental challenge. Some areas of the underground have a natural capacity for reducing contaminants. In this research these areas are mapped in 3D along with information about, e.g., sand and clay, which indicates whether contaminated water from the surface will travel through these areas. This mapping technique will be fundamental for more reliable risk assessment in water quality protection.
Jakob Juul Larsen, Stine Søgaard Pedersen, Nikolaj Foged, and Esben Auken
Geosci. Instrum. Method. Data Syst., 10, 81–90, https://doi.org/10.5194/gi-10-81-2021, https://doi.org/10.5194/gi-10-81-2021, 2021
Short summary
Short summary
The transient electromagnetic method (TEM) is widely used for mapping subsurface resistivity structures, but data are inevitably contaminated by noise from various sources including radio signals in the very low frequency (VLF) 3–30 kHz band. We present an approach where VLF noise is effectively suppressed with a new post-processing scheme where boxcar gates are combined into semi-tapered gates. The result is a 20 % increase in the depth of investigation for the presented test survey.
Lichao Liu, Denys Grombacher, Esben Auken, and Jakob Juul Larsen
Geosci. Instrum. Method. Data Syst., 8, 1–11, https://doi.org/10.5194/gi-8-1-2019, https://doi.org/10.5194/gi-8-1-2019, 2019
Short summary
Short summary
This paper introcudes the design workflow and test approaches of a surface-NMR receiver. But the method and technqiues, for instance, signal loop, acqusition board, GPS synchronization, and Wi-Fi network, could also be employed in other geophysical instruments.
Adrian A. S. Barfod, Troels N. Vilhelmsen, Flemming Jørgensen, Anders V. Christiansen, Anne-Sophie Høyer, Julien Straubhaar, and Ingelise Møller
Hydrol. Earth Syst. Sci., 22, 5485–5508, https://doi.org/10.5194/hess-22-5485-2018, https://doi.org/10.5194/hess-22-5485-2018, 2018
Short summary
Short summary
The focus of this study is on the uncertainty related to using multiple-point statistics (MPS) for stochastic modeling of the upper 200 m of the subsurface. The main research goal is to showcase how MPS methods can be used on real-world hydrogeophysical data and show how the uncertainty related to changing the underlying MPS setup propagates into the finalized 3-D subsurface models.
Adrian A. S. Barfod, Ingelise Møller, Anders V. Christiansen, Anne-Sophie Høyer, Júlio Hoffimann, Julien Straubhaar, and Jef Caers
Hydrol. Earth Syst. Sci., 22, 3351–3373, https://doi.org/10.5194/hess-22-3351-2018, https://doi.org/10.5194/hess-22-3351-2018, 2018
Short summary
Short summary
Three-dimensional geological models are important to securing and managing groundwater. Such models describe the geological architecture, which is used for modeling the flow of groundwater. Common geological modeling approaches result in one model, which does not quantify the architectural uncertainty of the geology.
We present a comparison of three different state-of-the-art stochastic multiple-point statistical methods for quantifying the geological uncertainty using real-world datasets.
Ahmad Ali Behroozmand, Pietro Teatini, Jesper Bjergsted Pedersen, Esben Auken, Omar Tosatto, and Anders Vest Christiansen
Hydrol. Earth Syst. Sci., 21, 1527–1545, https://doi.org/10.5194/hess-21-1527-2017, https://doi.org/10.5194/hess-21-1527-2017, 2017
Short summary
Short summary
Within the framework of the EU project IMPROWARE, our goal was to investigate a Mediterranean coastal aquifer in Egypt and develop scenarios for artificial aquifer remediation and recharge. The results of an extensive hydrogeophysical investigation were successfully used as an input in regional and local hydrological models to understand the hydrological evolution of the area. The research outcomes clearly highlight the effectiveness of using advanced geophysical and modeling methodologies.
P. A. Marker, N. Foged, X. He, A. V. Christiansen, J. C. Refsgaard, E. Auken, and P. Bauer-Gottwein
Hydrol. Earth Syst. Sci., 19, 3875–3890, https://doi.org/10.5194/hess-19-3875-2015, https://doi.org/10.5194/hess-19-3875-2015, 2015
N. Foged, P. A. Marker, A. V. Christansen, P. Bauer-Gottwein, F. Jørgensen, A.-S. Høyer, and E. Auken
Hydrol. Earth Syst. Sci., 18, 4349–4362, https://doi.org/10.5194/hess-18-4349-2014, https://doi.org/10.5194/hess-18-4349-2014, 2014
Related subject area
Domain: ESSD – Land | Subject: Geophysics and geodesy
Global physics-based database of injection-induced seismicity
The Weisweiler passive seismological network: optimised for state-of-the-art location and imaging methods
A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by Advanced Very High Resolution Radiometer observations from 1981 to 2021
Moho depths beneath the European Alps: a homogeneously processed map and receiver functions database
The ULR-repro3 GPS data reanalysis and its estimates of vertical land motion at tide gauges for sea level science
In situ stress database of the greater Ruhr region (Germany) derived from hydrofracturing tests and borehole logs
The European Preinstrumental Earthquake Catalogue EPICA, the 1000–1899 catalogue for the European Seismic Hazard Model 2020
Rescue and quality control of historical geomagnetic measurement at Sheshan observatory, China
A newly integrated ground temperature dataset of permafrost along the China–Russia crude oil pipeline route in Northeast China
In situ observations of the Swiss periglacial environment using GNSS instruments
Permafrost changes in the northwestern Da Xing'anling Mountains, Northeast China, in the past decade
British Antarctic Survey's aerogeophysical data: releasing 25 years of airborne gravity, magnetic, and radar datasets over Antarctica
Iman R. Kivi, Auregan Boyet, Haiqing Wu, Linus Walter, Sara Hanson-Hedgecock, Francesco Parisio, and Victor Vilarrasa
Earth Syst. Sci. Data, 15, 3163–3182, https://doi.org/10.5194/essd-15-3163-2023, https://doi.org/10.5194/essd-15-3163-2023, 2023
Short summary
Short summary
Induced seismicity has posed significant challenges to secure deployment of geo-energy projects. Through a review of published documents, we present a worldwide, multi-physical database of injection-induced seismicity. The database contains information about in situ rock, tectonic and geologic characteristics, operational parameters, and seismicity for various subsurface energy-related activities. The data allow for an improved understanding and management of injection-induced seismicity.
Claudia Finger, Marco P. Roth, Marco Dietl, Aileen Gotowik, Nina Engels, Rebecca M. Harrington, Brigitte Knapmeyer-Endrun, Klaus Reicherter, Thomas Oswald, Thomas Reinsch, and Erik H. Saenger
Earth Syst. Sci. Data, 15, 2655–2666, https://doi.org/10.5194/essd-15-2655-2023, https://doi.org/10.5194/essd-15-2655-2023, 2023
Short summary
Short summary
Passive seismic analyses are a key technology for geothermal projects. The Lower Rhine Embayment, at the western border of North Rhine-Westphalia in Germany, is a geologically complex region with high potential for geothermal exploitation. Here, we report on a passive seismic dataset recorded with 48 seismic stations and a total extent of 20 km. We demonstrate that the network design allows for the application of state-of-the-art seismological methods.
Jia-Hao Li, Zhao-Liang Li, Xiangyang Liu, and Si-Bo Duan
Earth Syst. Sci. Data, 15, 2189–2212, https://doi.org/10.5194/essd-15-2189-2023, https://doi.org/10.5194/essd-15-2189-2023, 2023
Short summary
Short summary
The Advanced Very High Resolution Radiometer (AVHRR) is the only sensor that has the advantages of frequent revisits (twice per day), relatively high spatial resolution (4 km at the nadir), global coverage, and easy access prior to 2000. This study developed a global historical twice-daily LST product for 1981–2021 based on AVHRR GAC data. The product is suitable for detecting and analyzing climate changes over the past 4 decades.
Konstantinos Michailos, György Hetényi, Matteo Scarponi, Josip Stipčević, Irene Bianchi, Luciana Bonatto, Wojciech Czuba, Massimo Di Bona, Aladino Govoni, Katrin Hannemann, Tomasz Janik, Dániel Kalmár, Rainer Kind, Frederik Link, Francesco Pio Lucente, Stephen Monna, Caterina Montuori, Stefan Mroczek, Anne Paul, Claudia Piromallo, Jaroslava Plomerová, Julia Rewers, Simone Salimbeni, Frederik Tilmann, Piotr Środa, Jérôme Vergne, and the AlpArray-PACASE Working Group
Earth Syst. Sci. Data, 15, 2117–2138, https://doi.org/10.5194/essd-15-2117-2023, https://doi.org/10.5194/essd-15-2117-2023, 2023
Short summary
Short summary
We examine the spatial variability of the crustal thickness beneath the broader European Alpine region by using teleseismic earthquake information (receiver functions) on a large amount of seismic waveform data. We compile a new Moho depth map of the broader European Alps and make our results freely available. We anticipate that our results can potentially provide helpful hints for interdisciplinary imaging and numerical modeling studies.
Médéric Gravelle, Guy Wöppelmann, Kevin Gobron, Zuheir Altamimi, Mikaël Guichard, Thomas Herring, and Paul Rebischung
Earth Syst. Sci. Data, 15, 497–509, https://doi.org/10.5194/essd-15-497-2023, https://doi.org/10.5194/essd-15-497-2023, 2023
Short summary
Short summary
We produced a reanalysis of GNSS data near tide gauges worldwide within the International GNSS Service. It implements advances in data modelling and corrections, extending the record length by about 7 years. A 28 % reduction in station velocity uncertainties is achieved over the previous solution. These estimates of vertical land motion at the coast supplement data from satellite altimetry or tide gauges for an improved understanding of sea level changes and their impacts along coastal areas.
Michal Kruszewski, Gerd Klee, Thomas Niederhuber, and Oliver Heidbach
Earth Syst. Sci. Data, 14, 5367–5385, https://doi.org/10.5194/essd-14-5367-2022, https://doi.org/10.5194/essd-14-5367-2022, 2022
Short summary
Short summary
The authors assemble an in situ stress magnitude and orientation database based on 429 hydrofracturing tests that were carried out in six coal mines and two coal bed methane boreholes between 1986 and 1995 within the greater Ruhr region (Germany). Our study summarises the results of the extensive in situ stress test campaign and assigns quality to each data record using the established quality ranking schemes of the World Stress Map project.
Andrea Rovida, Andrea Antonucci, and Mario Locati
Earth Syst. Sci. Data, 14, 5213–5231, https://doi.org/10.5194/essd-14-5213-2022, https://doi.org/10.5194/essd-14-5213-2022, 2022
Short summary
Short summary
EPICA is the 1000–1899 catalogue compiled for the European Seismic Hazard Model 2020 and contains 5703 earthquakes with Mw ≥ 4.0. It relies on the data of the European Archive of Historical Earthquake Data (AHEAD), both macroseismic intensities from historical seismological studies and parameters from regional catalogues. For each earthquake, the most representative datasets were selected and processed in order to derive harmonised parameters, both from intensity data and parametric catalogues.
Suqin Zhang, Changhua Fu, Jianjun Wang, Guohao Zhu, Chuanhua Chen, Shaopeng He, Pengkun Guo, and Guoping Chang
Earth Syst. Sci. Data, 14, 5195–5212, https://doi.org/10.5194/essd-14-5195-2022, https://doi.org/10.5194/essd-14-5195-2022, 2022
Short summary
Short summary
The Sheshan observatory has nearly 150 years of observation history, and its observation data have important scientific value. However, with time, these precious historical data face the risk of damage and loss. We have carried out a series of rescues on the historical data of the Sheshan observatory. New historical datasets were released, including the quality-controlled absolute hourly mean values of three components (D, H, and Z) from 1933 to 2019.
Guoyu Li, Wei Ma, Fei Wang, Huijun Jin, Alexander Fedorov, Dun Chen, Gang Wu, Yapeng Cao, Yu Zhou, Yanhu Mu, Yuncheng Mao, Jun Zhang, Kai Gao, Xiaoying Jin, Ruixia He, Xinyu Li, and Yan Li
Earth Syst. Sci. Data, 14, 5093–5110, https://doi.org/10.5194/essd-14-5093-2022, https://doi.org/10.5194/essd-14-5093-2022, 2022
Short summary
Short summary
A permafrost monitoring network was established along the China–Russia crude oil pipeline (CRCOP) route at the eastern flank of the northern Da Xing'anling Mountains in Northeast China. The resulting datasets fill the gaps in the spatial coverage of mid-latitude mountain permafrost databases. Results show that permafrost warming has been extensively observed along the CRCOP route, and local disturbances triggered by the CRCOPs have resulted in significant permafrost thawing.
Alessandro Cicoira, Samuel Weber, Andreas Biri, Ben Buchli, Reynald Delaloye, Reto Da Forno, Isabelle Gärtner-Roer, Stephan Gruber, Tonio Gsell, Andreas Hasler, Roman Lim, Philippe Limpach, Raphael Mayoraz, Matthias Meyer, Jeannette Noetzli, Marcia Phillips, Eric Pointner, Hugo Raetzo, Cristian Scapozza, Tazio Strozzi, Lothar Thiele, Andreas Vieli, Daniel Vonder Mühll, Vanessa Wirz, and Jan Beutel
Earth Syst. Sci. Data, 14, 5061–5091, https://doi.org/10.5194/essd-14-5061-2022, https://doi.org/10.5194/essd-14-5061-2022, 2022
Short summary
Short summary
This paper documents a monitoring network of 54 positions, located on different periglacial landforms in the Swiss Alps: rock glaciers, landslides, and steep rock walls. The data serve basic research but also decision-making and mitigation of natural hazards. It is the largest dataset of its kind, comprising over 209 000 daily positions and additional weather data.
Xiaoli Chang, Huijun Jin, Ruixia He, Yanlin Zhang, Xiaoying Li, Xiaoying Jin, and Guoyu Li
Earth Syst. Sci. Data, 14, 3947–3959, https://doi.org/10.5194/essd-14-3947-2022, https://doi.org/10.5194/essd-14-3947-2022, 2022
Short summary
Short summary
Based on 10-year observations of ground temperatures in seven deep boreholes in Gen’he, Mangui, and Yituli’he, a wide range of mean annual ground temperatures at the depth of 20 m (−2.83 to −0.49 ℃) and that of annual maximum thawing depth (about 1.1 to 7.0 m) have been revealed. This study demonstrates that most trajectories of permafrost changes in Northeast China are ground warming and permafrost degradation, except that the shallow permafrost is cooling in Yituli’he.
Alice C. Frémand, Julien A. Bodart, Tom A. Jordan, Fausto Ferraccioli, Carl Robinson, Hugh F. J. Corr, Helen J. Peat, Robert G. Bingham, and David G. Vaughan
Earth Syst. Sci. Data, 14, 3379–3410, https://doi.org/10.5194/essd-14-3379-2022, https://doi.org/10.5194/essd-14-3379-2022, 2022
Short summary
Short summary
This paper presents the release of large swaths of airborne geophysical data (including gravity, magnetics, and radar) acquired between 1994 and 2020 over Antarctica by the British Antarctic Survey. These include a total of 64 datasets from 24 different surveys, amounting to >30 % of coverage over the Antarctic Ice Sheet. This paper discusses how these data were acquired and processed and presents the methods used to standardize and publish the data in an interactive and reproducible manner.
Cited articles
Asif, M. R.: rizwanasif/DL-RMD: DL-RMD (DL-RMD), Zenodo [code], https://doi.org/10.5281/zenodo.7740243, 2023.
Asif, M. R., Qi, C., Wang, T., Fareed, M. S., and Khan, S.: License plate
detection for multi-national vehicles–a generalized approach, Multimed.
Tools Appl., 78, 35585–35606, 2019.
Asif, M. R., Bording, T. S., Barfod, A. S., Grombacher, D. J., Maurya, P.
K., Christiansen, A. V., Auken, E., and Larsen, J. J.: Effect of data
pre-processing on the performance of neural networks for 1-D transient
electromagnetic forward modelling, IEEE Access, 9, 34635–34646, 2021a.
Asif, M. R., Bording, T. S., Maurya, P. K., Zhang, B., Fiandaca, G.,
Grombacher, D. J., Christiansen, A. V., Auken, E., and Larsen, J. J.: A
Neural Network-Based Hybrid Framework for Least-Squares Inversion of
Transient Electromagnetic Data, IEEE T. Geosci. Remote, 60, 4503610, https://doi.org/10.1109/TGRS.2021.3076121, 2021b.
Asif, M. R., Foged, N., Bording, T., Larsen, J. J., and Christiansen, A. V.:
DL-RMD: A geophysically constrained electromagnetic resistivity model
database for deep learning applications, Zenodo [data set], https://doi.org/10.5281/zenodo.7260886, 2022a.
Asif, M. R., Foged, N., Maurya, P. K., Grombacher, D. J., Christiansen, A.
V., Auken, E., and Larsen, J. J.: Integrating neural networks in
least-squares inversion of airborne time-domain electromagnetic data,
Geophysics, 87, E177–E187, https://doi.org/10.1190/geo2021-0335.1, 2022b.
Auken, E., Christiansen, A. V., Kirkegaard, C., Fiandaca, G., Schamper, C.,
Behroozmand, A. A., Binley, A., Nielsen, E., Effersø, F., and
Christensen, N. B.: An overview of a highly versatile forward and stable
inverse algorithm for airborne, ground-based and borehole electromagnetic
and electric data, Explor. Geophys., 46, 223–235, https://doi.org/10.1071/EG13097, 2015 (data available at: https://hgg.au.dk/software/aarhusinv, last access: 16 March 2023).
Auken, E., Foged, N., Larsen, J. J., Lassen, K. V. T., Maurya, P. K., Dath,
S. M., and Eiskjær, T. T.: tTEM – A towed transient electromagnetic
system for detailed 3D imaging of the top 70 m of the subsurface,
Geophysics, 84, E13–E22, 2018.
Baan, M. V. D. and Jutten, C.: Neural networks in geophysical applications,
Geophysics, 65, 1032–1047, 2000.
Bai, P., Vignoli, G., Viezzoli, A., Nevalainen, J., and Vacca, G.: (Quasi-)Real-Time Inversion of Airborne Time-Domain Electromagnetic Data via
Artificial Neural Network, Remote Sensing, 12, 3440, https://doi.org/10.3390/rs12203440, 2020.
Balch, S., Boyko, W., and Paterson, N.: The AeroTEM airborne electromagnetic
system, The Leading Edge, 22, 562–566, 2003.
Bang, M., Oh, S., Noh, K., Seol, S. J., and Byun, J.: Imaging subsurface orebodies with airborne electromagnetic data using a recurrent neural network, Geophysics, 86, E407–E419, https://doi.org/10.1190/geo2020-0871.1, 2021.
Bergen, K. J., Johnson, P. A., Maarten, V., and Beroza, G. C.: Machine
learning for data-driven discovery in solid Earth geoscience, Science, 363,
eaau0323, https://doi.org/10.1126/science.aau0323, 2019.
Birken, R. A. and Poulton, M. M.: Neural Network Interpretation of High
Frequency Electromagnetic Ellipticity Data Part II: Analyzing 3D Responses,
Journal of Environmental Engineering Geophysics, 4, 149–165, 1999.
Birken, R. A., Poulton, M. M., and Lee, K. H.: Neural Network Interpretation
of High Frequency Electromagnetic Ellipticity Data Part I: Understanding the
Half-Space and Layered Earth Response, Journal of Environmental Engineering
Geophysics, 4, 93–103, 1999.
Bording, T. S., Asif, M. R., Barfod, A. S., Larsen, J. J., Zhang, B.,
Grombacher, D. J., Christiansen, A. V., Engebretsen, K. W., Pedersen, J. B.,
Maurya, P. K., and Auken, E.: Machine learning based fast forward modelling
of ground-based time-domain electromagnetic data, J. Appl.
Geophys., 187, 104290, https://doi.org/10.1016/j.jappgeo.2021.104290, 2021.
Christiansen, A. V. and Auken, E.: Layered 2-D inversion of profile data,
evaluated using stochastic models, ASEG Extended Abstracts, 1–8, https://doi.org/10.1071/ASEG2003_3DEMab005, 2003.
Christiansen, A. V. and Auken, E.: A global measure for depth of
investigation, Geophysics, 77, WB171–WB177, 2012.
Christiansen, A. V., Auken, E., and Sørensen, K.: The transient
electromagnetic method, in: Groundwater geophysics, edited by: Kirsch, R., Springer, ISBN 978-3-540-29383-5, https://doi.org/10.1007/3-540-29387-6_6, 2006.
Colombo, D., Li, W., Rovetta, D., Sandoval-Curiel, E., and Turkoglu, E.:
Physics-driven deep learning joint inversion, in: SEG Technical Program
Expanded Abstracts 2020, Society of Exploration Geophysicists, 1775–1779, https://doi.org/10.1190/segam2020-3424997.1, 2020a.
Colombo, D., Li, W., Sandoval-Curiel, E., and McNeice, G. W.:
Electromagnetic reservoir monitoring with machine-learning inversion and
fluid flow simulators, in: Fifth International Conference on Engineering
Geophysics (ICEG), Al Ain, UAE, 21–24 October 2019, 167–170, https://doi.org/10.1190/iceg2019-043.1, 2020b.
Colombo, D., Turkoglu, E., Li, W., and Rovetta, D.: Coupled physics-deep
learning inversion, Comput. Geosci., 157, 104917, https://doi.org/10.1016/j.cageo.2021.104917, 2021a.
Colombo, D., Turkoglu, E., Li, W., Sandoval-Curiel, E., and Rovetta, D.:
Physics-driven deep learning inversion with application to transient
electromagnetics, Geophysics, 86, E209–E224, https://doi.org/10.1190/geo2020-0760.1, 2021b.
Conway, D., Alexander, B., King, M., Heinson, G., and Kee, Y.: Inverting
magnetotelluric responses in a three-dimensional earth using fast forward
approximations based on artificial neural networks, Comput.
Geosci., 127, 44–52, 2019.
Dong, C., Loy, C. C., and Tang, X.: Accelerating the super-resolution
convolutional neural network, in: European conference on computer vision, Amsterdam, the Netherlands, 11–14 October 2016,
391–407, https://doi.org/10.1007/978-3-319-46475-6_25, 2016.
Dramsch, J. S.: 70 years of machine learning in geoscience in review,
Adv. Geophys., 61, 1–55, https://doi.org/10.1016/bs.agph.2020.08.002, 2020.
Feng, X.-T. and Seto, M.: Neural network dynamic modelling of rock
microfracturing sequences under triaxial compressive stress conditions,
Tectonophysics, 292, 293–309, 1998.
Guo, R., Li, M., Fang, G., Yang, F., Xu, S., and Abubakar, A.: Application
of supervised descent method to transient electromagnetic data inversion,
Geophysics, 84, E225–E237, 2019.
Hopfield, J. J.: Neural networks and physical systems with emergent
collective computational abilities, P. Natl. Acad.
Sci. USA, 79, 2554–2558, 1982.
Jørgensen, F., Sandersen, P. B., Auken, E., Lykke-Andersen, H., and
Sørensen, K.: Contributions to the geological mapping of Mors, Denmark – a
study based on a large-scale TEM survey, B. Geol. Soc.
Denmark, 52, 53–75, 2005.
Karshakov, E. V., Podmogov, Y. G., Kertsman, V. M., and Moilanen, J.:
Combined Frequency Domain and Time Domain Airborne Data for Environmental
and Engineering Challenges, J. Environ. Eng. Geoph.,
22, 1–11, https://doi.org/10.2113/JEEG22.1.1, 2017.
Khatibi, S. and Aghajanpour, A.: Machine Learning: A Useful Tool in
Geomechanical Studies, a Case Study from an Offshore Gas Field, Energies,
13, 3528, https://doi.org/10.3390/en13143528, 2020.
Kirsch, R.: Groundwater geophysics: a tool for hydrogeology, Springer, ISBN 978-3-540-29383-5, https://doi.org/10.1007/3-540-29387-6, 2006.
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: ImageNet classification with deep convolutional neural networks, Adv. Neur. Inf. Proc. Sys., 25, 1097–1105, https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf (last access: 16 March 2023), 2012.
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: Imagenet classification
with deep convolutional neural networks, Communications of the ACM, 60,
84–90, 2017.
Kwan, K., Reford, S., Abdoul-Wahab, D. M., Pitcher, D. H., Bournas, N.,
Prikhodko, A., Plastow, G., and Legault, J. M.: Supervised neural network
targeting and classification analysis of airborne EM, magnetic and gamma-ray
spectrometry data for mineral exploration, ASEG Extended Abstracts, 2015,
1–5, https://doi.org/10.1071/ASEG2015ab306, 2015.
Leggatt, P. B., Klinkert, P. S., and Hage, T. B.: The Spectrem airborne
electromagnetic system – Further developments, Geophysics, 65, 1976–1982,
2000.
Li, J., Liu, Y., Yin, C., Ren, X., and Su, Y.: Fast imaging of time-domain
airborne EM data using deep learning technology, Geophysics, 85, E163–E170, https://doi.org/10.1190/geo2019-0015.1, 2020.
Lin, F., Chen, K., Wang, X., Cao, H., Chen, D., and Chen, F.: Denoising stacked autoencoders for transient electromagnetic signal denoising, Nonlin. Processes Geophys., 26, 13–23, https://doi.org/10.5194/npg-26-13-2019, 2019.
Liu, W., Lü, Q., Yang, L., Lin, P., and Wang, Z.: Application of
Sample-Compressed Neural Network and Adaptive-Clustering Algorithm for
Magnetotelluric Inverse Modeling, IEEE Geoscience Remote Sensing Letters,
18, 1540–1544, https://doi.org/10.1109/LGRS.2020.3005796, 2020.
Maurer, H., Holliger, K., and Boerner, D. E.: Stochastic regularization:
Smoothness or similarity?, Geophys. Res. Lett., 25, 2889–2892,
1998.
Moghadas, D.: One-dimensional deep learning inversion of electromagnetic
induction data using convolutional neural network, Geophys. J.
Int., 222, 247–259, 2020.
Moghadas, D., Behroozmand, A. A., and Christiansen, A. V.: Soil electrical
conductivity imaging using a neural network-based forward solver: Applied to
large-scale Bayesian electromagnetic inversion, J. Appl.
Geophys., 176, 104012, https://doi.org/10.1016/j.jappgeo.2020.104012, 2020.
Møller, I., Jacobsen, B. H., and Christensen, N. B.: Rapid inversion of
2-D geoelectrical data by multichannel deconvolution, Geophysics, 66,
800–808, 2001.
Noh, K., Yoon, D., and Byun, J.: Imaging subsurface resistivity structure
from airborne electromagnetic induction data using deep neural network,
Explor. Geophys., 51, 214–220, 2020.
Pang, X., Zhou, Y., Wang, P., Lin, W., and Chang, V.: An innovative neural
network approach for stock market prediction, J. Supercomput.,
76, 2098–2118, 2020.
Poulton, M. M., Sternberg, B. K., and Glass, C. E.: Location of subsurface
targets in geophysical data using neural networks, Geophysics, 57,
1534–1544, 1992.
Puzyrev, V.: Deep learning electromagnetic inversion with convolutional
neural networks, Geophys. J. Int., 218, 817–832, 2019.
Puzyrev, V. and Swidinsky, A.: Inversion of 1D frequency-and time-domain
electromagnetic data with convolutional neural networks, Comput.
Geosci., 149, 104681, https://doi.org/10.1016/j.cageo.2020.104681, 2021.
Qin, S., Wang, Y., Tai, H.-M., Wang, H., Liao, X., and Fu, Z.: TEM apparent
resistivity imaging for grounding grid detection using artificial neural
network, IET Generation, Transmission, Distribution, 13, 3932–3940, https://doi.org/10.1049/iet-gtd.2018.6450, 2019.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A.: You only look once:
Unified, real-time object detection, in: Proceedings of the IEEE conference on
computer vision and pattern recognition, Las Vegas, NV, USA, 27–30 June 2016, 779–788, https://doi.org/10.1109/CVPR.2016.91, 2016.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., and
Carvalhais, N.: Deep learning and process understanding for data-driven
Earth system science, Nature, 566, 195–204, 2019.
Ren, S., He, K., Girshick, R., and Sun, J.: Faster r-cnn: Towards real-time
object detection with region proposal networks, Adv. Neur.
In., 1, 91–99, 2015.
Röth, G. and Tarantola, A.: Neural networks and inversion of seismic
data, J. Geophys. Res.-Sol. Ea., 99, 6753–6768, 1994.
Smith, R., Fountain, D., and Allard, M.: The MEGATEM fixed-wing transient EM
system applied to mineral exploration: a discovery case history, First
Break, 21, https://doi.org/10.3997/1365-2397.21.7.25570, 2003.
Smith, R. S.: Airborne electromagnetic methods: Applications to minerals,
water and hydrocarbon exploration, Canadian Society of Explortation
Geophysics, 35, 7–10, https://csegrecorder.com/articles/view/airborne-electromagnetic-methods-app-to-minerals-water-hydrocarbon-expl (last access: 16 March 2023), 2010.
Sørensen, K. I. and Auken, E.: SkyTEM-A new high-resolution helicopter
transient electromagnetic system, Explor. Geophys., 35, 191–199, 2004.
Tacher, L., Pomian-Srzednicki, I., and Parriaux, A.: Geological
uncertainties associated with 3-D subsurface models, Comput. Geosci., 32, 212–221, 2006.
Viezzoli, A., Christiansen, A. V., Auken, E., and Sørensen, K.: Quasi-3D
modeling of airborne TEM data by spatially constrained inversion,
Geophysics, 73, F105–F113, 2008.
Vignoli, G., Fiandaca, G., Christiansen, A. V., Kirkegaard, C., and Auken,
E.: Sharp spatially constrained inversion with applications to transient
electromagnetic data, Geophys. Prospect., 63, 243–255, 2015.
Wang, W., Zhang, M., Chen, G., Jagadish, H., Ooi, B. C., and Tan, K.-L.:
Database meets deep learning: Challenges and opportunities, ACM SIGMOD
Record, 45, 17–22, 2016.
Wu, S., Huang, Q., and Zhao, L.: Convolutional neural network inversion of
airborne transient electromagnetic data, Geophys. Prospect., 69,
1761–1772, 2021a.
Wu, S., Huang, Q., and Zhao, L.: De-noising of transient electromagnetic
data based on the long short-term memory-autoencoder, Geophys. J.
Int., 224, 669–681, 2021b.
Xue, G., Li, H., He, Y., Xue, J., and Wu, X.: Development of the Inversion
Method for Transient Electromagnetic Data, IEEE Access, 8, 146172–146181, https://doi.org/10.1109/ACCESS.2020.3013626,
2020.
Zhang, R., Liu, Y., and Sun, H.: Physics-guided convolutional neural network
(PhyCNN) for data-driven seismic response modeling, Eng. Struct.,
215, 110704, https://doi.org/10.1016/j.engstruct.2020.110704, 2020.
Zhang, Y. and Paulson, K.: Regularized hopfield neural networks and its
application to one-dimensional inverse problem of magnetotelluric
observations, Inverse Probl. Eng., 5, 33–53, 1997.
Zhang, Y., Pezeshki, M., Brakel, P., Zhang, S., Bengio, C. L. Y., and
Courville, A.: Towards end-to-end speech recognition with deep convolutional
neural networks, arXiv [preprint], https://doi.org/10.48550/arXiv.1701.02720, 10 January 2017.
Zhang, Z., Qi, C., and Asif, M. R.: Investigation on Projection Space Pairs
in Neighbor Embedding Algorithms, 2018 14th IEEE International Conference on Signal Processing (ICSP), Beijing, China, 12–16 August 2018, 125–128, https://doi.org/10.1109/ICSP.2018.8652441, 2018.
Zhdanov, M. S.: Inverse theory and applications in geophysics, 2nd edn., Elsevier, ISBN 978-0-444-62674-5,
https://doi.org/10.1016/C2012-0-03334-0,
2015.
Zhu, K.-G., Ma, M.-Y., Che, H.-W., Yang, E.-W., Ji, Y.-J., Yu, S.-B., and
Lin, J.: PC-based artificial neural network inversion for airborne
time-domain electromagnetic data, J. Appl. Geophys., 9, 1–8, https://doi.org/10.1007/s11770-012-0307-7,
2012.
Short summary
To apply a deep learning (DL) algorithm to electromagnetic (EM) methods, subsurface resistivity models and/or the corresponding EM responses are often required. To date, there are no standardized EM datasets, which hinders the progress and evolution of DL methods due to data inconsistency. Therefore, we present a large-scale physics-driven model database of geologically plausible and EM-resolvable subsurface models to incorporate consistency and reliability into DL applications for EM methods.
To apply a deep learning (DL) algorithm to electromagnetic (EM) methods, subsurface resistivity...
