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
| 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
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Mathias Vang, Jakob Juul Larsen, Anders Vest Christiansen, and Denys Grombacher
EGUsphere, https://doi.org/10.5194/egusphere-2025-406, https://doi.org/10.5194/egusphere-2025-406, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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To manage groundwater effectively, it's important to understand subsurface water systems. Geophysical methods can characterize subsurface layers, but relying on just one method can be misleading. This study combines two methods – Transient electromagnetics and surface nuclear magnetic resonance – in a K-means clustering scheme to better resolve freshwater and saltwater zones. Two case studies showed how a combined approach improves characterization of these hydrogeological important layers.
Smith Kashiram Khare, Paul McLachlan, Pradip Kumar Maurya, and Jakob Juul Larsen
Geosci. Instrum. Method. Data Syst., 13, 27–41, https://doi.org/10.5194/gi-13-27-2024, https://doi.org/10.5194/gi-13-27-2024, 2024
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Data recorded with transient electromagnetics are typically gated to improve the signal-to-noise ratio. Gating corresponds to low-pass filtering of data with the shape of the gate giving the frequency response. We show that standard gate shapes can lead to significant correlation between gates caused by distortion from VLF radios. A multi-objective cost function is used to select optimum gate shapes with less influence from radio noise. Performance is demonstrated using synthetic and field data.
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
Preprint withdrawn
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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
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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
Revised manuscript accepted for GI
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Tim Enzlberger Jensen, Bjørnar Dale, Andreas Stokholm, René Forsberg, Alexandre Bresson, Nassim Zahzam, Alexis Bonnin, and Yannick Bidel
Earth Syst. Sci. Data, 17, 1667–1684, https://doi.org/10.5194/essd-17-1667-2025, https://doi.org/10.5194/essd-17-1667-2025, 2025
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Christoph Dahle, Eva Boergens, Ingo Sasgen, Thorben Döhne, Sven Reißland, Henryk Dobslaw, Volker Klemann, Michael Murböck, Rolf König, Robert Dill, Mike Sips, Ulrike Sylla, Andreas Groh, Martin Horwath, and Frank Flechtner
Earth Syst. Sci. Data, 17, 611–631, https://doi.org/10.5194/essd-17-611-2025, https://doi.org/10.5194/essd-17-611-2025, 2025
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Hui Gao, Xinming Wu, Xiaoming Sun, Mingcai Hou, Hang Gao, Guangyu Wang, and Hanlin Sheng
Earth Syst. Sci. Data, 17, 595–609, https://doi.org/10.5194/essd-17-595-2025, https://doi.org/10.5194/essd-17-595-2025, 2025
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We propose three strategies for field seismic data curation, knowledge-guided synthesization, and generative adversarial network (GAN)-based generation to construct a massive-scale, feature-rich, and high-realism benchmark dataset of seismic facies and evaluate its effectiveness in training a deep-learning model for automatic seismic facies classification.
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-413, https://doi.org/10.5194/essd-2024-413, 2024
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Estimates of earthquake ground motions rely on evaluating soil and rock profiles, with shear wave velocity (Vs) as a key factor. Uncertainty in Vs impacts seismic hazard predictions. Stochastic procedures model this uncertainty, but must be calibrated using detailed geological data and Vs databases. This paper provides a new Vs profile database for Piedmont (NW Italy), integrating geological modeling and geophysical data, supporting similar studies in other regions.
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-467, https://doi.org/10.5194/essd-2024-467, 2024
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-448, https://doi.org/10.5194/essd-2024-448, 2024
Revised manuscript accepted for ESSD
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This work describes a dataset of 5 years of earthquakes with magnitude range 2.0–5.5 from January 2019 along the East Anatolian Fault, Türkiye. All events were located using the Non-Linear Location algorithm, providing reliable horizontal locations and depths. The distributed product includes Fourier Amplitude Spectra, Peak Ground Acceleration and Peak Ground Velocity; we strongly believe that the creation of high-quality, open-source datasets is crucial for any seismological investigation.
Eric Buchta, Mirko Scheinert, Matt A. King, Terry Wilson, Achraf Koulali, Peter J. Clarke, Demián Gómez, Eric Kendrick, Christoph Knöfel, and Peter Busch
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-355, https://doi.org/10.5194/essd-2024-355, 2024
Revised manuscript accepted for ESSD
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For nearly three decades, geodetic GPS measurements in Antarctica have tracked bedrock displacement, vital for understanding geodynamic processes like plate motion and glacial isostatic adjustment (GIA). However, the potential of GPS data has been limited by its partially fragmented availability and unreliable metadata. A new dataset, spanning 1995–2021, offers consistently processed coordinate time series for 286 GPS sites, promising to enhance future geodynamic research.
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-406, https://doi.org/10.5194/essd-2024-406, 2024
Revised manuscript accepted for ESSD
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-162, https://doi.org/10.5194/essd-2024-162, 2024
Revised manuscript accepted for ESSD
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This paper describes data and preliminary analyses made by the INGV emergency task force EMERSITO, devoted to site effects and seismic microzonation studies, following the November 9th, 2022, MW 5.5 earthquake (Adriatic sea, Italy). Considering the earthquake affected area, EMERSITO deployed a temporary seismic network of 11 stations (net code 6N) sampling different geological units in the urban area of Ancona, the regional capital of Marche, and operated from November 2022 to February 2023.
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-131, https://doi.org/10.5194/essd-2024-131, 2024
Revised manuscript accepted for ESSD
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Seismic paleochannel interpretation is essential for hydrocarbon exploration and paleoclimate studies but remains labor-intensive. Deep learning (DL) is promising to automate it but hindered by the lack of labeled data. We propose a workflow to simulate various channels and realistic seismic volumes, yielding the largest 3D seismic dataset with diverse channel labels. Its effectiveness is proven by field applications. The dataset, codes and DL models are released to advance further research.
Hao Zhou, Lijun Zheng, Yaozong Li, Xiang Guo, Zebing Zhou, and Zhicai Luo
Earth Syst. Sci. Data, 16, 3261–3281, https://doi.org/10.5194/essd-16-3261-2024, https://doi.org/10.5194/essd-16-3261-2024, 2024
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The satellite gravimetry mission Gravity Recovery and Climate Experiment (GRACE) and its follower GRACE-FO play a vital role in monitoring mass transportation on Earth. Based on the latest observation data derived from GRACE and GRACE-FO and an updated data processing chain, a new monthly temporal gravity field series, HUST-Grace2024, was determined.
Francesca Pace, Andrea Vergnano, Alberto Godio, Gerardo Romano, Luigi Capozzoli, Ilaria Baneschi, Marco Doveri, and Alessandro Santilano
Earth Syst. Sci. Data, 16, 3171–3192, https://doi.org/10.5194/essd-16-3171-2024, https://doi.org/10.5194/essd-16-3171-2024, 2024
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We present the geophysical data set acquired close to Ny-Ålesund (Svalbard islands) for the characterization of glacial and hydrological processes and features. The data have been organized in a repository that includes both raw and processed (filtered) data and some representative results of 2D models of the subsurface. This data set can foster multidisciplinary scientific collaborations among many disciplines: hydrology, glaciology, climatology, geology, geomorphology, etc.
Dino Bindi, Riccardo Zaccarelli, Angelo Strollo, Domenico Di Giacomo, Andres Heinloo, Peter Evans, Fabrice Cotton, and Frederik Tilmann
Earth Syst. Sci. Data, 16, 1733–1745, https://doi.org/10.5194/essd-16-1733-2024, https://doi.org/10.5194/essd-16-1733-2024, 2024
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The size of an earthquake is often described by a single number called the magnitude. Among the possible magnitude scales, the seismic moment (Mw) and the radiated energy (Me) scales are based on physical parameters describing the rupture process. Since these two magnitude scales provide complementary information that can be used for seismic hazard assessment and for seismic risk mitigation, we complement the Mw catalog disseminated by the GEOFON Data Centre with Me values.
Neda Darbeheshti, Martin Lasser, Ulrich Meyer, Daniel Arnold, and Adrian Jäggi
Earth Syst. Sci. Data, 16, 1589–1599, https://doi.org/10.5194/essd-16-1589-2024, https://doi.org/10.5194/essd-16-1589-2024, 2024
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This paper discusses strategies to improve the GRACE gravity field monthly solutions computed at the Astronomical Institute of the University of Bern. We updated the input observations and background models, as well as improving processing strategies in terms of instrument data screening and instrument parameterization.
Athina Peidou, Donald F. Argus, Felix W. Landerer, David N. Wiese, and Matthias Ellmer
Earth Syst. Sci. Data, 16, 1317–1332, https://doi.org/10.5194/essd-16-1317-2024, https://doi.org/10.5194/essd-16-1317-2024, 2024
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This study recommends a framework for preparing and processing vertical land displacements derived from GPS positioning for future integration with Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) measurements. We derive GPS estimates that only reflect surface mass signals and evaluate them against GRACE (and GRACE-FO). We also quantify uncertainty of GPS vertical land displacement estimates using various uncertainty quantification methods.
Lavinia Tunini, Andrea Magrin, Giuliana Rossi, and David Zuliani
Earth Syst. Sci. Data, 16, 1083–1106, https://doi.org/10.5194/essd-16-1083-2024, https://doi.org/10.5194/essd-16-1083-2024, 2024
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This study presents 20-year time series of more than 350 GNSS stations located in NE Italy and surroundings, together with the outgoing velocities. An overview of the input data, station information, data processing and solution quality is provided. The documented dataset constitutes a crucial and complete source of information about the deformation of an active but slowly converging margin over the last 2 decades, also contributing to the regional seismic hazard assessment of NE Italy.
Wenbin Tang, Ji Zhou, Jin Ma, Ziwei Wang, Lirong Ding, Xiaodong Zhang, and Xu Zhang
Earth Syst. Sci. Data, 16, 387–419, https://doi.org/10.5194/essd-16-387-2024, https://doi.org/10.5194/essd-16-387-2024, 2024
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This paper reported a daily 1 km all-weather land surface temperature (LST) dataset for Chinese land mass and surrounding areas – TRIMS LST. The results of a comprehensive evaluation show that TRIMS LST has the following special features: the longest time coverage in its class, high image quality, and good accuracy. TRIMS LST has already been released to the scientific community, and a series of its applications have been reported by the literature.
Arno Zang, Peter Niemz, Sebastian von Specht, Günter Zimmermann, Claus Milkereit, Katrin Plenkers, and Gerd Klee
Earth Syst. Sci. Data, 16, 295–310, https://doi.org/10.5194/essd-16-295-2024, https://doi.org/10.5194/essd-16-295-2024, 2024
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We present experimental data collected in 2015 at Äspö Hard Rock Laboratory. We created six cracks in a rock mass by injecting water into a borehole. The cracks were monitored using special sensors to study how the water affected the rock. The goal of the experiment was to figure out how to create a system for generating heat from the rock that is better than what has been done before. The data collected from this experiment are important for future research into generating energy from rocks.
Quetzalcoatl Rodríguez-Pérez and F. Ramón Zúñiga
Earth Syst. Sci. Data, 15, 4781–4801, https://doi.org/10.5194/essd-15-4781-2023, https://doi.org/10.5194/essd-15-4781-2023, 2023
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We present a comprehensive catalog of focal mechanisms for earthquakes in Mexico and neighboring areas spanning February 1928 to July 2022. The catalog comprises a wide range of earthquake magnitudes and depths and includes data from diverse geological environments. We collected and revised focal mechanism data from various sources and methods. The catalog is a valuable resource for future studies on earthquake source mechanisms, tectonics, and seismic hazard in the region.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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...
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