Articles | Volume 11, issue 4
https://doi.org/10.5194/se-11-1527-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-11-1527-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Aug 2020
Research article |
| 24 Aug 2020
| 24 Aug 2020
Deep learning for fast simulation of seismic waves in complex media
Department of Computer Science, University of Oxford, Oxford, UK
Tarje Nissen-Meyer
Department of Earth Sciences, University of Oxford, Oxford, UK
Andrew Markham
Department of Computer Science, University of Oxford, Oxford, UK
Related authors
No articles found.
Laura Ermert, Jonas Igel, Korbinian Sager, Eléonore Stutzmann, Tarje Nissen-Meyer, and Andreas Fichtner
Solid Earth, 11, 1597–1615, https://doi.org/10.5194/se-11-1597-2020, https://doi.org/10.5194/se-11-1597-2020, 2020
Short summary
Short summary
We present an open-source tool to model ambient seismic auto- and cross-correlations with spatially varying source spectra. The modeling is based on pre-computed databases of seismic wave propagation, which can be obtained from public data providers. The aim of this tool is to facilitate the modeling of ambient noise correlations, which are an important seismologic observable, with realistic wave propagation physics. We present a description and benchmark along with example use cases.
Related subject area
Subject area: Crustal structure and composition | Editorial team: Seismics, seismology, paleoseismology, geoelectrics, and electromagnetics | Discipline: Seismology
The impact of seismic noise produced by wind turbines on seismic borehole measurements
Probing environmental and tectonic changes underneath Mexico City with the urban seismic field
Quantifying gender gaps in seismology authorship
Mapping the basement of the Cerdanya Basin (eastern Pyrenees) using seismic ambient noise
Referent seismic crustal model of the Dinarides
Constraints on fracture distribution in the Los Humeros geothermal field from beamforming of ambient seismic noise
Radial anisotropy and S-wave velocity depict the internal to external zone transition within the Variscan orogen (NW Iberia)
Distributed acoustic sensing as a tool for subsurface mapping and seismic event monitoring: a proof of concept
Seismic monitoring of the STIMTEC hydraulic stimulation experiment in anisotropic metamorphic gneiss
One-dimensional velocity structure modeling of the Earth's crust in the northwestern Dinarides
A functional tool to explore the reliability of micro-earthquake focal mechanism solutions for seismotectonic purposes
Changepoint detection in seismic double-difference data: application of a trans-dimensional algorithm to data-space exploration
3D crustal structure of the Ligurian Basin revealed by surface wave tomography using ocean bottom seismometer data
Elastic anisotropies of deformed upper crustal rocks in the Alps
A revised image of the instrumental seismicity in the Lodi area (Po Plain, Italy)
Seismic radiation from wind turbines: observations and analytical modeling of frequency-dependent amplitude decays
Relocation of earthquakes in the southern and eastern Alps (Austria, Italy) recorded by the dense, temporary SWATH-D network using a Markov chain Monte Carlo inversion
Seismic noise variability as an indicator of urban mobility during the COVID-19 pandemic in the Santiago metropolitan region, Chile
Transversely isotropic lower crust of Variscan central Europe imaged by ambient noise tomography of the Bohemian Massif
Evaluating seismic beamforming capabilities of distributed acoustic sensing arrays
Crustal structure of southeast Australia from teleseismic receiver functions
Seismic monitoring of the Auckland Volcanic Field during New Zealand's COVID-19 lockdown
Using horizontal-to-vertical spectral ratios to construct shear-wave velocity profiles
Crustal structures beneath the Eastern and Southern Alps from ambient noise tomography
Introducing noisi: a Python tool for ambient noise cross-correlation modeling and noise source inversion
Fault reactivation by gas injection at an underground gas storage off the east coast of Spain
Lithospheric image of the Central Iberian Zone (Iberian Massif) using global-phase seismic interferometry
Modeling active fault systems and seismic events by using a fiber bundle model – example case: the Northridge aftershock sequence
Visual analytics of aftershock point cloud data in complex fault systems
Passive processing of active nodal seismic data: estimation of VP∕VS ratios to characterize structure and hydrology of an alpine valley infill
Monitoring of induced distributed double-couple sources using Marchenko-based virtual receivers
ER3D: a structural and geophysical 3-D model of central Emilia-Romagna (northern Italy) for numerical simulation of earthquake ground motion
Migration of reflector orientation attributes in deep seismic profiles: evidence for decoupling of the Yilgarn Craton lower crust
The cross-dip correction as a tool to improve imaging of crooked-line seismic data: a case study from the post-glacial Burträsk fault, Sweden
Green's theorem in seismic imaging across the scales
Near-surface structure of the North Anatolian Fault zone from Rayleigh and Love wave tomography using ambient seismic noise
Power spectra of random heterogeneities in the solid earth
A multi-technology analysis of the 2017 North Korean nuclear test
Obtaining reliable source locations with time reverse imaging: limits to array design, velocity models and signal-to-noise ratios
Fabian Limberger, Georg Rümpker, Michael Lindenfeld, and Hagen Deckert
Solid Earth, 14, 859–869, https://doi.org/10.5194/se-14-859-2023, https://doi.org/10.5194/se-14-859-2023, 2023
Short summary
Short summary
Wind turbines that are located close to a seismometer produce ground tremors that can increase the noise level at the seismic station. Using numerical models, we analyse the effectivity of borehole installations to reduce this impact. We study effects of geophysical parameters on the borehole effectivity and validate our modelling approach with data from real boreholes. Boreholes are effective in reducing the impact of wind turbines; however, this depends on the wavelength of the seismic wave.
Laura A. Ermert, Enrique Cabral-Cano, Estelle Chaussard, Darío Solano-Rojas, Luis Quintanar, Diana Morales Padilla, Enrique A. Fernández-Torres, and Marine A. Denolle
Solid Earth, 14, 529–549, https://doi.org/10.5194/se-14-529-2023, https://doi.org/10.5194/se-14-529-2023, 2023
Short summary
Short summary
Mexico City is built on a unique ground containing the clay-rich sediments of the ancient lake Texcoco. Continuous imperceptible shaking of these deposits by city traffic and other sources allows us to monitor changes in the subsurface seismic wave speed. Wave speed varies seasonally, likely due to temperature and rain effects; it temporarily drops after large earthquakes then starts to recover. Throughout the studied period, it increased on average, which may be related to soil compaction.
Laura Anna Ermert, Maria Koroni, and Naiara Korta Martiartu
Solid Earth, 14, 485–498, https://doi.org/10.5194/se-14-485-2023, https://doi.org/10.5194/se-14-485-2023, 2023
Short summary
Short summary
We investigate women's representation in seismology to raise awareness of existing gender disparities.
By analysing the authorship of peer-reviewed articles, we identify lower representation of women among single authors, high-impact authors, and highly productive authors. Seismology continues to be a male-dominated field, and trends suggest that parity is decades away. These gaps are an obstacle to women’s career advancement and, if neglected, may perpetuate the leaky-pipeline problem.
Jordi Díaz, Sergi Ventosa, Martin Schimmel, Mario Ruiz, Albert Macau, Anna Gabàs, David Martí, Özgenç Akin, and Jaume Vergés
Solid Earth, 14, 499–514, https://doi.org/10.5194/se-14-499-2023, https://doi.org/10.5194/se-14-499-2023, 2023
Short summary
Short summary
We assess the capability of multiple methods based on the interpretation of seismic noise to map the basement of the Cerdanya Basin, located in the eastern Pyrenees. Basement depth estimations retrieved from the different approaches are consistent, with maximum depths reaching 700 m close to the Têt fault bounding the basin to the east. Our results prove that seismic noise analysis using high-density networks is an excellent tool to improve the geological characterization of sedimentary basins.
Katarina Zailac, Bojan Matoš, Igor Vlahović, and Josip Stipčević
EGUsphere, https://doi.org/10.5194/egusphere-2023-183, https://doi.org/10.5194/egusphere-2023-183, 2023
Short summary
Short summary
Presently there is no complete crustal model of the Dinarides. Using the compilations of previous studies, we have created vertically, and laterally varying crustal models defined on a regular grid for the wider area of the Dinarides, also covering parts of Adriatic Sea and SW part of the Pannonian Basin. In addition to the seismic velocities and density, we also defined three interfaces – sedimentary deposits bottom, Carbonate rock thickness and crustal thickness.
Heather Kennedy, Katrin Löer, and Amy Gilligan
Solid Earth, 13, 1843–1858, https://doi.org/10.5194/se-13-1843-2022, https://doi.org/10.5194/se-13-1843-2022, 2022
Short summary
Short summary
The energy transition is an important topic for benefiting the future; thus renewable energy is required to reach net-zero carbon emission goals. Geothermal energy, heat from the ground, can be used in this transition. Therefore, geothermal fields need to be characterized as much as possible to allow for increased productivity within these fields. This study involves and looks at potential fractures within a geothermal field at depth to help increase the overall understanding of this field.
Jorge Acevedo, Gabriela Fernández-Viejo, Sergio Llana-Fúnez, Carlos López-Fernández, Javier Olona, and Diego Pérez-Millán
Solid Earth, 13, 659–679, https://doi.org/10.5194/se-13-659-2022, https://doi.org/10.5194/se-13-659-2022, 2022
Short summary
Short summary
The NW Iberian Peninsula provides one of the most complete Variscan sections in Europe, showing the transition between a sedimentary domain with folds and thrust and a metamorphic domain with igneous intrusions. By processing the seismic ambient noise recorded by several seismograph networks in this area, new 3-D S-wave velocity and radial anisotropy models were created. These models reveal the limit between the two domains, delineating the core of the large western European Variscan Belt.
Nicola Piana Agostinetti, Alberto Villa, and Gilberto Saccorotti
Solid Earth, 13, 449–468, https://doi.org/10.5194/se-13-449-2022, https://doi.org/10.5194/se-13-449-2022, 2022
Short summary
Short summary
Sensing the Earth is a fundamental operation for the future where georesources, like geothermal energy and CO2 underground storage, will become important tools for addressing societal challenges. The development of networks of optical fibre cables gives the possibility of a sensing grid with an unprecedented spatial coverage. Here, we investigate the potential of using portions of a optical fibre cable as a standard seismometer for exploring the subsurface and monitoring georesources.
Carolin M. Boese, Grzegorz Kwiatek, Thomas Fischer, Katrin Plenkers, Juliane Starke, Felix Blümle, Christoph Janssen, and Georg Dresen
Solid Earth, 13, 323–346, https://doi.org/10.5194/se-13-323-2022, https://doi.org/10.5194/se-13-323-2022, 2022
Short summary
Short summary
Hydraulic stimulation experiments in underground facilities allow for placing monitoring equipment close to and surrounding the stimulated rock under realistic and complex conditions at depth. We evaluate how accurately the direction-dependent velocity must be known for high-resolution seismic monitoring during stimulation. Induced transient deformation in rocks only 2.5–5 m apart may differ significantly in magnitude and style, and monitoring requires sensitive sensors adapted to the frequency.
Gregor Rajh, Josip Stipčević, Mladen Živčić, Marijan Herak, Andrej Gosar, and the AlpArray Working Group
Solid Earth, 13, 177–203, https://doi.org/10.5194/se-13-177-2022, https://doi.org/10.5194/se-13-177-2022, 2022
Short summary
Short summary
We investigated the 1-D velocity structure of the Earth's crust in the NW Dinarides with inversion of arrival times from earthquakes. The obtained velocity models give a better insight into the crustal structure and show velocity variations among different parts of the study area. In addition to general structural implications and a potential for improving further work, the results of our study can also be used for routine earthquake location and for detecting errors in seismological bulletins.
Guido Maria Adinolfi, Raffaella De Matteis, Rita de Nardis, and Aldo Zollo
Solid Earth, 13, 65–83, https://doi.org/10.5194/se-13-65-2022, https://doi.org/10.5194/se-13-65-2022, 2022
Short summary
Short summary
We propose a methodology useful to evaluate (1) the reliability of a focal mechanism solution inferred by the inversion of seismological data and (2) the performance of a seismic network, operated to monitor natural or induced seismicity, to assess focal mechanism solutions. As a test case, we studied the focal mechanism reliability by using synthetic data computed for ISNet, a local seismic network monitoring the Irpinia fault system (southern Italy).
Nicola Piana Agostinetti and Giulia Sgattoni
Solid Earth, 12, 2717–2733, https://doi.org/10.5194/se-12-2717-2021, https://doi.org/10.5194/se-12-2717-2021, 2021
Short summary
Short summary
One of the present-day challenges for geoscientists is tackling the big data revolution. An ever-growing amount of data needs to be processed and data are subjectively handled before using them to make inferences on the Earth’s interior. But imposing subjective decisions on the data might have strong influences on the final outputs. Here we present a totally novel and automatic application for screening the data and for defining data volumes that are consistent with physical hypotheses.
Felix N. Wolf, Dietrich Lange, Anke Dannowski, Martin Thorwart, Wayne Crawford, Lars Wiesenberg, Ingo Grevemeyer, Heidrun Kopp, and the AlpArray Working Group
Solid Earth, 12, 2597–2613, https://doi.org/10.5194/se-12-2597-2021, https://doi.org/10.5194/se-12-2597-2021, 2021
Short summary
Short summary
The Ligurian Sea opened ~30–15 Ma during SE migration of the Calabrian subduction zone. Using ambient seismic noise from stations on land and at the ocean bottom, we calculated a 3D shear-velocity model of the Ligurian Basin. In keeping with existing 2D studies, we find a shallow crust–mantle transition at the SW basin centre that deepens towards the northeast, Corsica, and the Liguro-Provençal coast. We observe a separation of SW and NE basins. We do not observe high crustal vP/vS ratios.
Ruth Keppler, Roman Vasin, Michael Stipp, Tomás Lokajícek, Matej Petruzálek, and Nikolaus Froitzheim
Solid Earth, 12, 2303–2326, https://doi.org/10.5194/se-12-2303-2021, https://doi.org/10.5194/se-12-2303-2021, 2021
Short summary
Short summary
Rocks in mountain belts have been deformed during continental collision causing a certain alignment of the minerals referred to as crystallographic preferred orientation (CPO). Minerals have anisotropic properties: the velocity of seismic waves travelling through them is direction dependent. This leads to anisotropy of the rocks. We measured the CPO of common rocks within the Alps. With this data and known anisotropic properties of the minerals we calculated the seismic anisotropy of the rocks.
Laura Peruzza, Alessandra Schibuola, Maria Adelaide Romano, Marco Garbin, Mariangela Guidarelli, Denis Sandron, and Enrico Priolo
Solid Earth, 12, 2021–2039, https://doi.org/10.5194/se-12-2021-2021, https://doi.org/10.5194/se-12-2021-2021, 2021
Short summary
Short summary
In weakly seismic or poorly monitored areas, the uncritical use of earthquake catalogues can be misleading. This is the case for a central sector in the Po Valley, where the Northern Apennines and Southern Alps collide. We collect and reprocess the available instrumental data of about 300 earthquakes from 1951 to 2019. The seismicity is weak, deeper than expected, and far from some existing human activities carried out underground. The potential tectonic causative sources are still unknown.
Fabian Limberger, Michael Lindenfeld, Hagen Deckert, and Georg Rümpker
Solid Earth, 12, 1851–1864, https://doi.org/10.5194/se-12-1851-2021, https://doi.org/10.5194/se-12-1851-2021, 2021
Short summary
Short summary
Frequency-dependent amplitude decays of seismic signals induced by wind turbines are determined from (up to) 6 months of continuous recordings measured along an 8 km profile located at a wind farm in Bavaria, Germany. The radiation pattern and amplitude decay of the induced signals are accounted for by an analytical approach that includes path and source effects. This approach is generalized to predict the characteristic seismic radiation patterns of arbitrary wind farm configurations.
Azam Jozi Najafabadi, Christian Haberland, Trond Ryberg, Vincent F. Verwater, Eline Le Breton, Mark R. Handy, Michael Weber, and the AlpArray and AlpArray SWATH-D working groups
Solid Earth, 12, 1087–1109, https://doi.org/10.5194/se-12-1087-2021, https://doi.org/10.5194/se-12-1087-2021, 2021
Short summary
Short summary
This study achieved high-precision hypocenters of 335 earthquakes (1–4.2 ML) and 1D velocity models of the Southern and Eastern Alps. The general pattern of seismicity reflects head-on convergence of the Adriatic Indenter with the Alpine orogenic crust. The relatively deeper seismicity in the eastern Southern Alps and Giudicarie Belt indicates southward propagation of the Southern Alpine deformation front. The derived hypocenters form excellent data for further seismological studies, e.g., LET.
Javier Ojeda and Sergio Ruiz
Solid Earth, 12, 1075–1085, https://doi.org/10.5194/se-12-1075-2021, https://doi.org/10.5194/se-12-1075-2021, 2021
Short summary
Short summary
In Santiago, Chile, the lockdown imposed due to COVID-19 was recorded by seismological instruments. This analysis shows temporal changes in the surface vibrations controlled by lockdown phases, mobility, and epidemiological factors. Our findings suggest that
dynamic lockdownand the early deconfinement in April 2020 caused an increase in mobility and therefore virus transmission. We propose that seismic networks could be used to monitor urban mobility as a new proxy in public policies.
Jiří Kvapil, Jaroslava Plomerová, Hana Kampfová Exnerová, Vladislav Babuška, György Hetényi, and AlpArray Working Group
Solid Earth, 12, 1051–1074, https://doi.org/10.5194/se-12-1051-2021, https://doi.org/10.5194/se-12-1051-2021, 2021
Short summary
Short summary
This paper presents a high-resolution 3-D shear wave velocity (vS) model of the Bohemian Massif crust imaged from high-density data and enhanced depth sensitivity of tomographic inversion. The dominant features of the model are relatively higher vS in the upper crust than in its surrounding, a distinct intra-crustal interface, and a velocity decrease in the lower part of the crust. The low vS in the lower part of the crust is explained by the anisotropic fabric of the lower crust.
Martijn P. A. van den Ende and Jean-Paul Ampuero
Solid Earth, 12, 915–934, https://doi.org/10.5194/se-12-915-2021, https://doi.org/10.5194/se-12-915-2021, 2021
Short summary
Short summary
Distributed acoustic sensing (DAS) is an emerging technology that measures stretching of an optical-fibre cable. This technology can be used to record the ground shaking of earthquakes, which offers a cost-efficient alternative to conventional seismometers. Since DAS is relatively new, we need to verify that existing seismological methods can be applied to this new data type. In this study, we reveal several issues by comparing DAS with conventional seismometer data for earthquake localisation.
Mohammed Bello, David G. Cornwell, Nicholas Rawlinson, Anya M. Reading, and Othaniel K. Likkason
Solid Earth, 12, 463–481, https://doi.org/10.5194/se-12-463-2021, https://doi.org/10.5194/se-12-463-2021, 2021
Short summary
Short summary
In this study, ground motion caused by distant earthquakes recorded in southeast Australia is used to image the structure of the crust and underlying mantle. This part of the Australian continent was assembled over the last 500 million years, but it remains poorly understood. By studying variations in crustal properties and thickness, we find evidence for the presence of an old microcontinent that is embedded in the younger terrane and forms a connection between Victoria and Tasmania.
Kasper van Wijk, Calum J. Chamberlain, Thomas Lecocq, and Koen Van Noten
Solid Earth, 12, 363–373, https://doi.org/10.5194/se-12-363-2021, https://doi.org/10.5194/se-12-363-2021, 2021
Short summary
Short summary
The Auckland Volcanic Field is monitored by a seismic network. The lockdown measures to combat COVID-19 in New Zealand provided an opportunity to evaluate the performance of seismic stations in the network and to search for small(er) local earthquakes, potentially hidden in the noise during "normal" times. Cross-correlation of template events resulted in detection of 30 new events not detected by GeoNet, but there is no evidence of an increase in detections during the quiet period of lockdown.
Janneke van Ginkel, Elmer Ruigrok, and Rien Herber
Solid Earth, 11, 2015–2030, https://doi.org/10.5194/se-11-2015-2020, https://doi.org/10.5194/se-11-2015-2020, 2020
Short summary
Short summary
Knowledge of subsurface velocities is key to understand how earthquake waves travel through the Earth. We present a method to construct velocity profiles for the upper sediment layer on top of the Groningen field, the Netherlands. Here, the soft-sediment layer causes resonance of seismic waves, and this resonance is used to compute velocities from. Recordings from large earthquakes and the background noise signals are used to derive reliable velocities for the deep sedimentary layer.
Ehsan Qorbani, Dimitri Zigone, Mark R. Handy, Götz Bokelmann, and AlpArray-EASI working group
Solid Earth, 11, 1947–1968, https://doi.org/10.5194/se-11-1947-2020, https://doi.org/10.5194/se-11-1947-2020, 2020
Short summary
Short summary
The crustal structure of the Eastern and Southern Alps is complex. Although several seismological studies have targeted the crust, the velocity structure under this area is still not fully understood. Here we study the crustal velocity structure using seismic ambient noise tomography. Our high-resolution models image several velocity anomalies and contrasts and reveal details of the crustal structure. We discuss our new models of the crust with respect to the geologic and tectonic features.
Laura Ermert, Jonas Igel, Korbinian Sager, Eléonore Stutzmann, Tarje Nissen-Meyer, and Andreas Fichtner
Solid Earth, 11, 1597–1615, https://doi.org/10.5194/se-11-1597-2020, https://doi.org/10.5194/se-11-1597-2020, 2020
Short summary
Short summary
We present an open-source tool to model ambient seismic auto- and cross-correlations with spatially varying source spectra. The modeling is based on pre-computed databases of seismic wave propagation, which can be obtained from public data providers. The aim of this tool is to facilitate the modeling of ambient noise correlations, which are an important seismologic observable, with realistic wave propagation physics. We present a description and benchmark along with example use cases.
Antonio Villaseñor, Robert B. Herrmann, Beatriz Gaite, and Arantza Ugalde
Solid Earth, 11, 63–74, https://doi.org/10.5194/se-11-63-2020, https://doi.org/10.5194/se-11-63-2020, 2020
Short summary
Short summary
We present new earthquake focal depths and fault orientations for earthquakes that occurred in 2013 in the vicinity of an underground gas storage off the east coast of Spain. Our focal depths are in the range of 5–10 km, notably deeper than the depth of the gas injection (2 km). The obtained fault orientations also differ from the predominant faults at shallow depths. This suggests that the faults reactivated are deeper, previously unmapped faults occurring beneath the sedimentary layers.
Juvenal Andrés, Deyan Draganov, Martin Schimmel, Puy Ayarza, Imma Palomeras, Mario Ruiz, and Ramon Carbonell
Solid Earth, 10, 1937–1950, https://doi.org/10.5194/se-10-1937-2019, https://doi.org/10.5194/se-10-1937-2019, 2019
Marisol Monterrubio-Velasco, F. Ramón Zúñiga, José Carlos Carrasco-Jiménez, Víctor Márquez-Ramírez, and Josep de la Puente
Solid Earth, 10, 1519–1540, https://doi.org/10.5194/se-10-1519-2019, https://doi.org/10.5194/se-10-1519-2019, 2019
Short summary
Short summary
Earthquake aftershocks display spatiotemporal correlations arising from their self-organized critical behavior. Stochastical models such as the fiber bundle (FBM) permit the use of an analog of the physical model that produces a statistical behavior with many similarities to real series. In this work, a new model based on FBM that includes geometrical faults systems is proposed. Our analysis focuses on aftershock statistics, and as a study case we modeled the Northridge sequence.
Chisheng Wang, Junzhuo Ke, Jincheng Jiang, Min Lu, Wenqun Xiu, Peng Liu, and Qingquan Li
Solid Earth, 10, 1397–1407, https://doi.org/10.5194/se-10-1397-2019, https://doi.org/10.5194/se-10-1397-2019, 2019
Short summary
Short summary
The point cloud of located aftershocks contains the information which can directly reveal the fault geometry and temporal evolution of an earthquake sequence. However, there is a lack of studies using state-of-the-art visual analytics methods to explore the data to discover hidden information about the earthquake fault. We present a novel interactive approach to illustrate 3-D aftershock point clouds, which can help the seismologist to better understand the complex fault system.
Michael Behm, Feng Cheng, Anna Patterson, and Gerilyn S. Soreghan
Solid Earth, 10, 1337–1354, https://doi.org/10.5194/se-10-1337-2019, https://doi.org/10.5194/se-10-1337-2019, 2019
Short summary
Short summary
New acquisition styles for active seismic source exploration provide a wealth of additional quasi-passive data. We show how these data can be used to gain complementary information about the subsurface. Specifically, we process an active-source dataset from an alpine valley in western Colorado with both active and passive inversion schemes. The results provide new insights on subsurface hydrology based on the ratio of P-wave and S-wave velocity structures.
Joeri Brackenhoff, Jan Thorbecke, and Kees Wapenaar
Solid Earth, 10, 1301–1319, https://doi.org/10.5194/se-10-1301-2019, https://doi.org/10.5194/se-10-1301-2019, 2019
Short summary
Short summary
Earthquakes in the subsurface are hard to monitor due to their complicated signals. We aim to make the monitoring of the subsurface possible by redatuming the sources and the receivers from the surface of the Earth to the subsurface to monitor earthquakes originating from small faults in the subsurface. By using several sources together, we create complex earthquake signals for large-scale faults sources.
Peter Klin, Giovanna Laurenzano, Maria Adelaide Romano, Enrico Priolo, and Luca Martelli
Solid Earth, 10, 931–949, https://doi.org/10.5194/se-10-931-2019, https://doi.org/10.5194/se-10-931-2019, 2019
Short summary
Short summary
Using geological and geophysical data, we set up a 3-D digital description of the underground structure in the central part of the Po alluvial plain. By means of computer-simulated propagation of seismic waves, we were able to identify the structural features that caused the unexpected elongation and amplification of the earthquake ground motion that was observed in the area during the 2012 seismic crisis. The study permits a deeper understanding of the seismic hazard in alluvial basins.
Andrew J. Calvert and Michael P. Doublier
Solid Earth, 10, 637–645, https://doi.org/10.5194/se-10-637-2019, https://doi.org/10.5194/se-10-637-2019, 2019
Short summary
Short summary
Deep (> 40 km) seismic reflection surveys are acquired on land along crooked roads. Using the varying azimuth between source and receiver, the true 3-D orientation of crustal structures can be determined. Applying this method to a survey over the ancient Australian Yilgarn Craton reveals that most reflectors in the lower crust exhibit a systematic dip perpendicular to those in the overlying crust, consistent with lateral flow of a weak lower crust in the hotter early Earth 2.7 billion years ago.
Ruth A. Beckel and Christopher Juhlin
Solid Earth, 10, 581–598, https://doi.org/10.5194/se-10-581-2019, https://doi.org/10.5194/se-10-581-2019, 2019
Short summary
Short summary
Scandinavia is crossed by extensive fault scarps that have likely been caused by huge earthquakes when the ice sheets of the last glacial melted. Due to the inaccessibility of the terrain, reflection seismic data have to be collected along crooked lines, which reduces the imaging quality unless special corrections are applied. We developed a new correction method that is very tolerant to noise and used it to improve the reflection image of such a fault and refine its geological interpretation.
Kees Wapenaar, Joeri Brackenhoff, and Jan Thorbecke
Solid Earth, 10, 517–536, https://doi.org/10.5194/se-10-517-2019, https://doi.org/10.5194/se-10-517-2019, 2019
Short summary
Short summary
The earthquake seismology and seismic exploration communities have developed a variety of seismic imaging methods for passive- and active-source data. Despite the seemingly different approaches and underlying principles, many of these methods are based in some way or another on the same mathematical theorem. Starting with this theorem, we discuss a variety of classical and recent seismic imaging methods in a systematic way and explain their similarities and differences.
George Taylor, Sebastian Rost, Gregory A. Houseman, and Gregor Hillers
Solid Earth, 10, 363–378, https://doi.org/10.5194/se-10-363-2019, https://doi.org/10.5194/se-10-363-2019, 2019
Short summary
Short summary
We constructed a seismic velocity model of the North Anatolian Fault in Turkey. We found that the fault is located within a region of reduced seismic velocity and skirts the edges of a geological unit that displays high seismic velocity, indicating that this unit could be stronger than the surrounding material. Furthermore, we found that seismic waves travel fastest in the NE–SW direction, which is the direction of maximum extension for this part of Turkey and indicates mineral alignment.
Haruo Sato
Solid Earth, 10, 275–292, https://doi.org/10.5194/se-10-275-2019, https://doi.org/10.5194/se-10-275-2019, 2019
Short summary
Short summary
Recent seismological observations clarified that the velocity structure of the crust and upper mantle is randomly heterogeneous. I compile reported power spectral density functions of random velocity fluctuations based on various types of measurements. Their spectral envelope is approximated by the third power of wavenumber. It is interesting to study what kinds of geophysical processes created such a power-law spectral envelope at different scales and in different geological environments.
Peter Gaebler, Lars Ceranna, Nima Nooshiri, Andreas Barth, Simone Cesca, Michaela Frei, Ilona Grünberg, Gernot Hartmann, Karl Koch, Christoph Pilger, J. Ole Ross, and Torsten Dahm
Solid Earth, 10, 59–78, https://doi.org/10.5194/se-10-59-2019, https://doi.org/10.5194/se-10-59-2019, 2019
Short summary
Short summary
On 3 September 2017 official channels of the Democratic People’s Republic of
Korea announced the successful test of a nuclear device. This study provides a
multi-technology analysis of the 2017 North Korean event and its aftermath using a wide array of geophysical methods (seismology, infrasound, remote sensing, radionuclide monitoring, and atmospheric transport modeling). Our results clearly indicate that the September 2017 North Korean event was in fact a nuclear test.
Claudia Werner and Erik H. Saenger
Solid Earth, 9, 1487–1505, https://doi.org/10.5194/se-9-1487-2018, https://doi.org/10.5194/se-9-1487-2018, 2018
Short summary
Short summary
Time reverse imaging is a method for locating quasi-simultaneous or low-amplitude earthquakes. Numerous three-dimensional synthetic simulations were performed to discover the influence of station distributions, complex velocity models and high noise rates on the reliability of localisations. The guidelines obtained enable the estimation of the localisation success rates of an existing station set-up and provide the basis for designing new arrays.
Cited articles
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K.,
Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O.,
Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.:
TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, https://www.tensorflow.org, last access: 9 August 2020, 2015. a
Ahmed, E., Saint, A., Shabayek, A. E. R., Cherenkova, K., Das, R., Gusev, G.,
Aouada, D., and Ottersten, B.: A survey on Deep Learning Advances on
Different 3D Data Representations, arXiv [preprint], https://arxiv.org/abs/1808.01462, 2018. a
Araya-Polo, M., Jennings, J., Adler, A., and Dahlke, T.: Deep-learning
tomography, The Leading Edge, 37, 58–66, 2018. a
Bergen, K. J., Johnson, P. A., De Hoop, M. 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. a
Bohlen, T.: Parallel 3-D viscoelastic finite difference seismic modelling,
Comput. Geosci., 28, 887–899, 2002. a
Boore, D. M.: Simulation of ground motion using the stochastic method, Pure
Appl. Geophys., 160, 635–676, 2003. a
Bozdağ, E., Peter, D., Lefebvre, M., Komatitsch, D., Tromp, J., Hill, J.,
Podhorszki, N., and Pugmire, D.: Global adjoint tomography: first-generation
model, Geophys. J. Int., 207, 1739–1766, 2016. a
Chopra, S. and Marfurt, K. J.: Seismic Attributes for Prospect Identification
and Reservoir Characterization, Society of Exploration Geophysicists and
European Association of Geoscientists and Engineers, 2007. a
Cui, Y., Olsen, K. B., Jordan, T. H., Lee, K., Zhou, J., Small, P., Roten, D.,
Ely, G., Panda, D. K., Chourasia, A., Levesque, J., Day, S. M., and
Maechling, P.: Scalable Earthquake Simulation on Petascale Supercomputers,
in: 2010 ACM/IEEE International Conference for High Performance Computing,
Networking, Storage and Analysis, New Orleans, LA, USA, 13–19 November 2010, 1–20, 2010. a
Devilee, R. J. R., Curtis, A., and Roy-Chowdhury, K.: An efficient,
probabilistic neural network approach to solving inverse problems: Inverting
surface wave velocities for Eurasian crustal thickness, J.
Geophys. Res.-Sol. Ea., 104, 28841–28857, 1999. a
Dowla, F. U., Taylor, S. R., and Anderson, R. W.: Seismic discrimination with
artificial neural networks: Preliminary results with regional spectral data,
B. Seismol. Soc. Am., 80, 1346–1373, 1990. a
Earp, S. and Curtis, A.: Probabilistic neural network-based 2D travel-time
tomography, Neural Comput. Appl., 1–19, 2020. a
Fichtner, A.: Full Seismic Waveform Modelling and Inversion, Springer, 2010. a
Gal, Y.: Uncertainty in Deep Learning, PhD thesis, University of Cambridge, 2016. a
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang,
X., Wang, G., Cai, J., and Chen, T.: Recent advances in convolutional neural
networks, Pattern Recogn., 77, 354–377, 2018. a
Gutenberg, B.: The amplitudes of waves to be expected in seismic prospecting,
Geophysics, 1, 252–256, 1936. a
Hosseini, K., Sigloch, K., Tsekhmistrenko, M., Zaheri, A., Nissen-Meyer, T.,
and Igel, H.: Global mantle structure from multifrequency tomography using
P, PP and P-diffracted waves, Geophys. J. Int., 220,
96–141, 2019. a
Igel, H.: Computational seismology: a practical introduction, Oxford
University Press, 2017. a
Ioffe, S. and Szegedy, C.: Batch normalization: Accelerating deep network
training by reducing internal covariate shift, in: 32nd International
Conference on Machine Learning, ICML 2015, 7–9 July 2015, Lille, France, 1, 448–456, 2015. a
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, arXiv [preprint], https://arxiv.org/abs/1412.6980, 2014. a
Komatitsch, D. and Martin, R.: An unsplit convolutional perfectly matched
layer improved at grazing incidence for the seismic wave equation,
Geophysics, 72, SM155–SM167, 2007. a
Komatitsch, D. and Tromp, J.: Introduction to the spectral element method for
three-dimensional seismic wave propagation, Geophys. J.
Int., 139, 806–822, 1999. a
Kong, Q., Trugman, D. T., Ross, Z. E., Bianco, M. J., Meade, B. J., and
Gerstoft, P.: Machine learning in seismology: Turning data into insights,
Seismol. Res. Lett., 90, 3–14, 2019. a
Krischer, L. and Fichtner, A.: Generating Seismograms with Deep Neural
Networks, AGU Fall Meeting Abstracts, 11–15 December 2017, New Orleans, Louisiana, USA, 2017. a
Krischer, L., Hutko, A. R., van Driel, M., Stähler, S., Bahavar, M.,
Trabant, C., and Nissen‐Meyer, T.: On-Demand Custom Broadband Synthetic
Seismograms, Seismol. Res. Lett., 88, 1127–1140, 2017. a
Long, G., Zhao, Y., and Zou, J.: A temporal fourth-order scheme for the
first-order acoustic wave equations, Geophys. J. Int., 194,
1473–1485, 2013. a
Lumley, D. E.: Time-lapse seismic reservoir monitoring, Geophysics, 66,
50–53, 2001. a
Margrave, G. F. and Lamoureux, M. P.: Numerical Methods of Exploration
Seismology, Cambridge University Press, 2018. a
Martin, G. S., Wiley, R., and Marfurt, K. J.: Marmousi2: An elastic upgrade
for Marmousi, Leading Edge, 25, 156–166, 2006. a
Moczo, P., Robertsson, J. O., and Eisner, L.: The Finite-Difference
Time-Domain Method for Modeling of Seismic Wave Propagation, Adv.
Geophys., 48, 421–516, 2007. a
Moseley, B.: Code repository for deep learning for fast simulation of seismic waves in complex media, available at: https://github.com/benmoseley/seismic-simulation-complex-media, last access: 9 August 2020. a
Moseley, B., Markham, A., and Nissen-Meyer, T.: Fast approximate simulation of
seismic waves with deep learning, arXiv [preprint], https://arxiv.org/abs/1807.06873, 2018. a
Murat, M. E. and Rudman, A. J.: Automated first arrival picking: a neural
network approach, Geophys. Prospect., 40, 587–604, 1992. a
Nair, V. and Hinton, G.: Rectified Linear Units Improve Restricted Boltzmann
Machines Vinod Nair, in: Proceedings of ICML, 21–24 June 2010, Haifa, Israel, 27, 807–814, 2010. a
Nath, S. K., Chakraborty, S., Singh, S. K., and Ganguly, N.: Velocity
inversion in cross-hole seismic tomography by counter-propagation neural
network, genetic algorithm and evolutionary programming techniques,
Geophys. J. Int., 138, 108–124, 1999. a
Newman, P.: Divergence effects in a layered earth, Geophysics, 38, 481–488, 1973. a
Ni, S., Tan, E., Gurnis, M., and Helmberger, D.: Sharp sides to the African
superplume, Science, 296, 1850–1852, 2002. a
Perol, T., Gharbi, M., and Denolle, M.: Convolutional neural network for
earthquake detection and location, Science Advances, 4, e1700578, 2018. a
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. a
Pytorch: available at: https://www.pytorch.org (last access: 9 August 2020), 2016. a
Raissi, M., Perdikaris, P., and Karniadakis, G. E.: Physics-informed neural
networks: A deep learning framework for solving forward and inverse problems
involving nonlinear partial differential equations, J. Comput.
Phys., 378, 686–707, 2019. a
Richardson, A.: Seismic Full-Waveform Inversion Using Deep Learning Tools and
Techniques, arXiv [preprint], https://arxiv.org/abs/1801.07232, 2018. a
Rietmann, M., Messmer, P., Nissen-Meyer, T., Peter, D., Basini, P., Komatitsch,
D., Schenk, O., Tromp, J., Boschi, L., and Giardini, D.: Forward and adjoint
simulations of seismic wave propagation on emerging large-scale GPU
architectures, International Conference for High Performance Computing,
Networking, Storage and Analysis, SC, November 2012, Salt Lake City, UT, 1–11, 2012. a
Russell, B. H.: Introduction to Seismic Inversion Methods, Society of
Exploration Geophysicists, 1988. a
Schuster, G. T.: Seismic Inversion, Society of Exploration Geophysicists,
2017. a
Sun, H. and Demanet, L.: Low frequency extrapolation with deep learning, 2018
SEG International Exposition and Annual Meeting, 14–19 October 2018, Anaheim, CA, USA, 2011–2015,
2018. a
Tarantola, A.: Inverse problem theory: methods for data fitting and model
parameter estimation, Elsevier, 1987. a
Thorne, M. S., Pachhai, S., Leng, K., Wicks, J. K., and Nissen-Meyer, T.: New
Candidate Ultralow-Velocity Zone Locations from Highly Anomalous SPdKS
Waveforms, Minerals, 10, 211, 2020. a
Valentine, A. P. and Trampert, J.: Data space reduction, quality assessment
and searching of seismograms: autoencoder networks for waveform data,
Geophys. J. Int., 189, 1183–1202, 2012. a
Van Der Baan, M., and Jutten, C.: Neural networks in geophysical
applications, Geophysics, 65, 1032–1047, 2000. a
van Driel, M., and Nissen-Meyer, T.: Optimized viscoelastic wave propagation
for weakly dissipative media, Geophys. J. Int., 199,
1078–1093, 2014a. a
van Driel, M., and Nissen-Meyer, T.: Seismic wave propagation in fully
anisotropic axisymmetric media, Geophys. J. Int., 199,
880–893, 2014b. a
van Driel, M., Ceylan, S., Clinton, J. F., Giardini, D., Alemany, H., Allam,
A., Ambrois, D., Balestra, J., Banerdt, B., Becker, D., Böse, M.,
Boxberg, M. S., Brinkman, N., Casademont, T., Chèze, J., Daubar, I.,
Deschamps, A., Dethof, F., Ditz, M., Drilleau, M., Essing, D., Euchner, F.,
Fernando, B., Garcia, R., Garth, T., Godwin, H., Golombek, M. P., Grunert,
K., Hadziioannou, C., Haindl, C., Hammer, C., Hochfeld, I., Hosseini, K., Hu,
H., Kedar, S., Kenda, B., Khan, A., Kilchling, T., Knapmeyer-Endrun, B.,
Lamert, A., Li, J., Lognonné, P., Mader, S., Marten, L., Mehrkens, F.,
Mercerat, D., Mimoun, D., Möller, T., Murdoch, N., Neumann, P.,
Neurath, R., Paffrath, M., Panning, M. P., Peix, F., Perrin, L., Rolland, L.,
Schimmel, M., Schröer, C., Spiga, A., Stähler, S. C., Steinmann,
R., Stutzmann, E., Szenicer, A., Trumpik, N., Tsekhmistrenko, M., Twardzik,
C., Weber, R., Werdenbach-Jarklowski, P., Zhang, S., and Zheng, Y.:
Preparing for InSight: Evaluation of the blind test for martian seismicity,
Seismol. Res. Lett., 90, 1518–1534, 2019. a
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N.,
Kaiser, L., and Polosukhin, I.: Attention Is All You Need, arXiv [preprint], https://arxiv.org/abs/1706.03762,
2017.
a
Vinje, V., Iversen, E., and Gjoystdal, H.: Traveltime and amplitude estimation
using wavefront construction, Geophysics, 58, 1157–1166, 1993. a
Virieux, J. and Operto, S.: An overview of full-waveform inversion in
exploration geophysics, Geophysics, 74, 6, 2009. a
Wu, Y. and Lin, Y.: InversionNet: A Real-Time and Accurate Full Waveform
Inversion with CNNs and continuous CRFs, arXiv [preprint], https://arxiv.org/abs/1811.07875, 2018. a
Xie, X.-B., Jin, S., and Wu, R.-S.: Wave-equation-based seismic illumination
analysis, Geophysics, 71, S169–S177, 2006. a
Yang, F. and Ma, J.: Deep-learning inversion: A next-generation seismic
velocity model building method, Geophysics, 84, R583–R599, 2019. a
Zhang, Z. and Lin, Y.: Data-driven Seismic Waveform Inversion: A Study on the
Robustness and Generalization,arXiv [preprint], https://arxiv.org/abs/1809.10262, 2018. a
Short summary
Simulations of seismic waves are very important; they allow us to understand how earthquakes spread and how the interior of the Earth is structured. However, whilst powerful, existing simulation methods usually require a large amount of computational power and time to run. In this research, we use modern machine learning techniques to accelerate these calculations inside complex models of the Earth.
Simulations of seismic waves are very important; they allow us to understand how earthquakes...
