Upper crustal structure at the KTB drilling site from ambient noise tomography

2020 ◽  
Author(s):  
Ehsan Qorbani ◽  
Irene Bianchi ◽  
Petr Kolínský ◽  
Dimitri Zigone ◽  
Götz Bokelmann
Keyword(s):  
Ambient Noise ◽  
Velocity Model ◽  
Ambient Noise Tomography ◽  
Data Set ◽  
Crustal Rocks ◽  
Velocity Models ◽  
Path Coverage ◽  
Passive Seismic ◽  
Cross Correlations ◽  
Drilling Site

<p>In this study, we show results from ambient noise tomography at the KTB drilling site, Germany. The Continental Deep Drilling Project, or ‘Kontinentales Tiefbohrprogramm der Bundesrepublik Deutschland’ (KTB) is at the northwestern edge of the Bohemian Massif and is located on the Variscan belt of Europe. During the KTB project crustal rocks have been drilled down to 9 km depth and several active seismic studies have been performed in the surrounding. The KTB area therefore presents an ideal test area for testing and verifying the potential resolution of passive seismic techniques. The aim of this study is to present a new shear-wave velocity model of the area while comparing the results to the previous velocity models and hints for anisotropy depicted by former passive and active seismological studies. We use a unique data set composed of two years of continuous data recorded at nine 3-component temporary stations installed from July 2012 to July 2014 located on top and vicinity of the drilling site. Moreover, we included a number of permanent stations in the region in order to improve the path coverage and density. Cross correlations of ambient noise are computed between the station pairs using all possible combination of three-component data. Dispersion curves of surface waves are extracted and are then inverted to obtain group velocity maps. We present here a new velocity model of the upper crust of the area, which shows velocity variations at short scales that correlate well with geology in the region.</p>

Upper crustal structure at the KTB drilling site from ambient noise tomography

2021 ◽  
Author(s):  
Ehsan Qorbani ◽  
Irene Bianchi ◽  
Petr Kolínský ◽  
Dimitri Zigone ◽  
Goetz Bokelmann
Keyword(s):  
Ambient Noise ◽  
Velocity Model ◽  
Ambient Noise Tomography ◽  
Data Set ◽  
Crustal Rocks ◽  
Velocity Models ◽  
Path Coverage ◽  
Passive Seismic ◽  
Drilling Site ◽  
Continental Deep Drilling

<p>In this study, we show results from ambient noise tomography at the KTB drilling site, Germany. The Continental Deep Drilling Project, or ‘Kontinentales Tiefbohrprogramm der Bundesrepublik Deutschland’ (KTB) is at the northwestern edge of the Bohemian Massif and is located on the Variscan belt of Europe. During the KTB project crustal rocks have been drilled down to 9 km depth and several active seismic studies have been performed in the surrounding. The KTB area therefore presents an ideal test area for testing and verifying the potential resolution of passive seismic techniques. The aim of this study is to present a new shear-wave velocity model of the area while comparing the results to the previous velocity models and hints for anisotropy depicted by former passive and active seismological studies. We use a unique data set composed of two years of continuous data recorded at nine 3-component temporary stations installed from July 2012 to July 2014 located on top and vicinity of the drilling site. Moreover, we included a number of permanent stations in the region in order to improve the path coverage and density. We present here a new velocity model of the upper crust of the area, which shows velocity variations at short scales that correlate well with geology in the region.</p>

scholarly journals Crustal structures beneath the Eastern and Southern Alps from ambient noise tomography

2020 ◽  
Author(s):  
Ehsan Qorbani ◽  
Dimitri Zigone ◽  
Mark R. Handy ◽  
Götz Bokelmann ◽  
Keyword(s):  
High Resolution ◽  
Crustal Structure ◽  
Ambient Noise ◽  
Eastern Alps ◽  
Southern Alps ◽  
Ambient Noise Tomography ◽  
Tauern Window ◽  
Velocity Models ◽  
Cross Correlations ◽  
Velocity Anomalies

Abstract. We study the crustal structure under the Eastern and Southern Alps using ambient noise tomography. We use cross-correlations of ambient seismic noise between pairs of 71 permanent stations and 19 stations of the EASI profile to derive new high-resolution 3-D shear-velocity models for the crust. Continuous records from 2014 and 2015 are cross-correlated to estimate Green's functions of Rayleigh and Love waves propagating between the station pairs. Group velocities extracted from the cross-correlations are inverted to obtain isotropic 3-D Rayleigh and Love-wave shear-wave velocity models. Our high resolution models image several velocity anomalies and contrasts and reveal details of the crustal structure. Velocity variations at short periods correlate very closely with the lithologies of tectonic units at the surface and projected to depth. Low-velocity zones, associated with the Po and Molasse sedimentary basins, are imaged well to the south and north of the Alps, respectively. We find large high-velocity zones associated with the crystalline basement that forms the core of the Tauern Window. Small-scale velocity anomalies are also aligned with geological units such as the Ötztal and the Gurktal nappes of the Austroalpine nappes. Clear velocity contrasts in the Tauern Window along vertical cross-sections of the velocity model show the depth extent of the tectonic units and their bounding faults. A mid-crustal velocity contrast is interpreted as a manifestation of intracrustal decoupling in the Eastern Alps and decoupling between the Southern and Eastern Alps.

Ambient noise tomography of Gran Canaria island (Canary Islands)

2021 ◽  
Author(s):  
Iván Cabrera Pérez ◽  
Jean Soubestre ◽  
Luca D'Auria ◽  
Germán Cervigón-Tomico ◽  
David Martínez van Dorth ◽  
...  
Keyword(s):  
Group Velocity ◽  
Canary Islands ◽  
Ambient Noise ◽  
Velocity Model ◽  
Dispersion Curves ◽  
Ambient Noise Tomography ◽  
Gran Canaria ◽  
Geothermal Reservoirs ◽  
Velocity Models ◽  
Non Linear

<p>The island of Gran Canaria is located in the Canarian Archipelago, with an area of 1560 km<sup>2 </sup>and a maximum altitude of 1956 m.a.s.l., being the third island of the archipelago in terms of extension and altitude. The island has two very well differentiated geological domains: the southwest domain or Paleo-Canarias, which is the geologically oldest part, and the northeast domain or Neo-Canarias, where are located the vents of the most recent Holocene eruptions. This volcanic island hosted Holocene eruptions. Therefore, apart from being affected by volcanic risk, it potentially hosts geothermal resources that could be exploited to increase the percentage of renewable energy in the Canary Islands.</p><p>The main objective of this work is to use Ambient Noise Tomography (ANT) for retrieving a high-resolution seismic velocity model of the first few kilometres of the crust, to improve local earthquake location and detect anomalies potentially related to active geothermal reservoirs. Currently, the 1-D velocity model of the island does not allow a correct determination of the hypocenters, being unable to take into account the substantial horizontal velocity contrasts correctly.</p><p>To realize the ANT, we deployed 28 temporary broadband seismic stations in two phases. Each campaign lasted at least one month. We also exploited data recorded by the permanent seismic network Red Sísmica Canaria (C7) operated by INVOLCAN. After applying standard data processing to retrieve Green’s functions from ambient noise cross-correlations, we retrieved the dispersion curves using the FTAN (Frequency Time ANalysis) technique. The inversion of dispersion curves to obtain group velocity maps was realized using a novel non-linear multiscale tomographic approach (MAnGOSTA, Multiscale Ambient NOiSe TomogrAphy). The forward modelling of surface waves traveltimes was implemented using a shortest-path algorithm that allows the topography to be taken into account. The MANgOSTA method consists of successive non-linear inversion steps on progressively finer grids. This technique allows retrieving 2-D group velocity models in the presence of substantial velocity contrasts with up to 100% of the relative variation. Then, we performed a depth inversion of the Rayleigh wave dispersion curves using a transdimensional Bayesian formulation. The final result is a 3-D model of P- and S-wave velocities of the island. The preliminary results show the presence of a low-velocity zone in the eastern part of the island that coincides spatially with anomalies observed in previous geophysical and geochemical studies and which could be related to actual or fossil geothermal reservoirs. Furthermore, the model shows the presence of high-velocity anomalies that are associated with the mafic core of the island.</p>

scholarly journals Crustal structures beneath the Eastern and Southern Alps from ambient noise tomography

Solid Earth ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 1947-1968 ◽  
Author(s):  
Ehsan Qorbani ◽  
Dimitri Zigone ◽  
Mark R. Handy ◽  
Götz Bokelmann ◽  
Keyword(s):  
Crustal Structure ◽  
Ambient Noise ◽  
Southern Alps ◽  
Small Scale ◽  
Ambient Noise Tomography ◽  
Tauern Window ◽  
Velocity Models ◽  
Austroalpine Nappes ◽  
Cross Correlations ◽  
Velocity Anomalies

Abstract. We study the crustal structure under the Eastern and Southern Alps using ambient noise tomography. We use cross-correlations of ambient seismic noise between pairs of 71 permanent stations and 19 stations of the Eastern Alpine Seismic Investigation (EASI) profile to derive new 3D shear velocity models for the crust. Continuous records from 2014 and 2015 are cross-correlated to estimate Green's functions of Rayleigh and Love waves propagating between the station pairs. Group velocities extracted from the cross-correlations are inverted to obtain isotropic 3D Rayleigh- and Love-wave shear-wave velocity models. Our models image several velocity anomalies and contrasts and reveal details of the crustal structure. Velocity variations at short periods correlate very closely with the lithologies of tectonic units at the surface and projected to depth. Low-velocity zones, associated with the Po and Molasse sedimentary basins, are imaged well to the south and north of the Alps, respectively. We find large high-velocity zones associated with the crystalline basement that forms the core of the Tauern Window. Small-scale velocity anomalies are also aligned with geological units of the Austroalpine nappes. Clear velocity contrasts in the Tauern Window along vertical cross sections of the velocity model show the depth extent of the tectonic units and their bounding faults. A mid-crustal velocity contrast is interpreted as a manifestation of intracrustal decoupling in the Eastern Alps that accommodated eastward escape of the Alcapa block.

3D Anisotropic velocity model of the Los Humeros geothermal field, Mexico, using seismic ambient noise tomography.

2020 ◽  
Author(s):  
Ivan Granados Chavarria ◽  
Marco Calò ◽  
Ángel Figueroa Soto ◽  
Philippe Jousset
Keyword(s):  
Surface Waves ◽  
Geothermal Energy ◽  
Ambient Noise ◽  
Geothermal System ◽  
Ambient Noise Tomography ◽  
Seismic Ambient Noise ◽  
Velocity Models ◽  
Seismic Image ◽  
Short Period ◽  
Cross Correlations

<p>In the framework of the international collaboration between Mexico and Europe for the development of geothermal energy (GEMex consortium), a seismic network of 45 seismic stations (25 broad-band and 20 short-period) was installed around the super-hot geothermal system of Los Humeros (Mexico) for more than one year. Los Humeros power plant is nested inside a quaternary caldera located in the eastern part of the Trans-Mexican Volcanic Belt that crosses the whole country from the Pacific coast to the Gulf of Mexico.</p><p>Among the several targets of the data collected by this network, an important task is to produce a seismic image of the caldera and of the geothermal reservoir. Here we present the 3D anisotropic shear wave velocity models retrieved by the seismic ambient noise tomography.</p><p>Thanks to the severe pre-processing of the whole seismic database we were able to obtain reliable and highly resolved models.</p><p>To carry out the model we applied a rigorous data quality assessment consisting in: 1) correction of the orientation of the sensors using the polarization of surface waves associated with tele-seismic and regional earthquakes, 2) assessment of the synchronization of the stations and correction of the times using daily cross-correlations functions, 3) finally to asses the quality of the stacked cross-correlations, knowed as Green’s functions (GF), we analyzed the noise sources directivity, inter-station distance and level of emergence of surface waves depending on the type of sensor used.</p><p>The processing allowed to pick clearly about 600 dispersion curves per velocity type (group and phase of R and L waves), using the NDCP code (Noisy Dispersion Curve Picking), that allows to display and select dispersion patterns both in time and frequency domain, for both causal and anti-causal part of the GF.</p><p>2D tomography maps were calculated from 0.5 to 9 s for each type of velocity. Depth inversion for the whole velocities types was carried out using surf96, allowing reconstructing the 3D anisotropic structure of the caldera for the first time.</p><p>The resulting models provides a larger view of the caldera and its anisotropic patterns down to 10 km depth. In these models, we were able to define the depth of the caldera rim, some important features of the internal part of the caldera and a low velocity body that could be associated with the hot sources feeding the reservoir. Our model are in strong agreement with those retrieved applying other geophysical methodologies (e.g. magnetotelluric, passive travel-time tomography, gravimetric, etc.).</p><p>This work is performed in the framework of the Mexican European consortium GeMex (Cooperation in Geothermal energy research Europe-Mexico, PT5.2 N: 267084 funded by CONACyT-SENER : S0019, 2015-04, and Horizon 2020, grant agreement No. 727550).</p>

scholarly journals Passive seismic event estimation using multiscattering waveform inversion

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. KS59-KS69 ◽  
Author(s):  
Chao Song ◽  
Zedong Wu ◽  
Tariq Alkhalifah
Keyword(s):  
Seismic Data ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Source Image ◽  
Time Inversion ◽  
Multiple Sources ◽  
Origin Time ◽  
Secondary Sources ◽  
Velocity Models ◽  
Passive Seismic

Passive seismic monitoring has become an effective method to understand underground processes. Time-reversal-based methods are often used to locate passive seismic events directly. However, these kinds of methods are strongly dependent on the accuracy of the velocity model. Full-waveform inversion (FWI) has been used on passive seismic data to invert the velocity model and source image, simultaneously. However, waveform inversion of passive seismic data uses mainly the transmission energy, which results in poor illumination and low resolution. We developed a waveform inversion using multiscattered energy for passive seismic to extract more information from the data than conventional FWI. Using transmission wavepath information from single- and double-scattering, computed from a predicted scatterer field acting as secondary sources, our method provides better illumination of the velocity model than conventional FWI. Using a new objective function, we optimized the source image and velocity model, including multiscattered energy, simultaneously. Because we conducted our method in the frequency domain with a complex source function including spatial and wavelet information, we mitigate the uncertainties of the source wavelet and source origin time. Inversion results from the Marmousi model indicate that by taking advantage of multiscattered energy and starting from a reasonably acceptable frequency (a single source at 3 Hz and multiple sources at 5 Hz), our method yields better inverted velocity models and source images compared with conventional FWI.

Nonlinear velocity inversion by a two‐step Monte Carlo method.

Geophysics ◽  
1994 ◽  
Vol 59 (4) ◽  
pp. 577-590 ◽  
Author(s):  
Side Jin ◽  
Raul Madariaga
Keyword(s):  
Monte Carlo ◽  
Velocity Model ◽  
Small Scale ◽  
Inversion Method ◽  
Nonlinear Inversion ◽  
Ray Theory ◽  
Data Set ◽  
Seismic Reflection Data ◽  
Velocity Models ◽  
Reference Velocity

Seismic reflection data contain information on small‐scale impedance variations and a smooth reference velocity model. Given a reference velocity model, the reflectors can be obtained by linearized migration‐inversion. If the reference velocity is incorrect, the reflectors obtained by inverting different subsets of the data will be incoherent. We propose to use the coherency of these images to invert for the background velocity distribution. We have developed a two‐step iterative inversion method in which we separate the retrieval of small‐scale variations of the seismic velocity from the longer‐period reference velocity model. Given an initial background velocity model, we use a waveform misfit‐functional for the inversion of small‐scale velocity variations. For this linear step we use the linearized migration‐inversion method based on ray theory that we have recently developed with Lambaré and Virieux. The reference velocity model is then updated by a Monte Carlo inversion method. For the nonlinear inversion of the velocity background, we introduce an objective functional that measures the coherency of the short wavelength components obtained by inverting different common shot gathers at the same locations. The nonlinear functional is calculated directly in migrated data space to avoid expensive numerical forward modeling by finite differences or ray theory. Our method is somewhat similar to an iterative migration velocity analysis, but we do an automatic search for relatively large‐scale 1-D reference velocity models. We apply the nonlinear inversion method to a marine data set from the North Sea and also show that nonlinear inversion can be applied to realistic scale data sets to obtain a laterally heterogeneous velocity model with a reasonable amount of computer time.

Deep learning-driven velocity model building workflow

2019 ◽  
Vol 38 (11) ◽  
pp. 872a1-872a9 ◽  
Author(s):  
Mauricio Araya-Polo ◽  
Stuart Farris ◽  
Manuel Florez
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Model Building ◽  
Ground Truth ◽  
Velocity Model ◽  
Training Data ◽  
Quality Data ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Velocity Models

Exploration seismic data are heavily manipulated before human interpreters are able to extract meaningful information regarding subsurface structures. This manipulation adds modeling and human biases and is limited by methodological shortcomings. Alternatively, using seismic data directly is becoming possible thanks to deep learning (DL) techniques. A DL-based workflow is introduced that uses analog velocity models and realistic raw seismic waveforms as input and produces subsurface velocity models as output. When insufficient data are used for training, DL algorithms tend to overfit or fail. Gathering large amounts of labeled and standardized seismic data sets is not straightforward. This shortage of quality data is addressed by building a generative adversarial network (GAN) to augment the original training data set, which is then used by DL-driven seismic tomography as input. The DL tomographic operator predicts velocity models with high statistical and structural accuracy after being trained with GAN-generated velocity models. Beyond the field of exploration geophysics, the use of machine learning in earth science is challenged by the lack of labeled data or properly interpreted ground truth, since we seldom know what truly exists beneath the earth's surface. The unsupervised approach (using GANs to generate labeled data)illustrates a way to mitigate this problem and opens geology, geophysics, and planetary sciences to more DL applications.

Migration moveout analysis and depth focusing

Geophysics ◽  
1993 ◽  
Vol 58 (1) ◽  
pp. 91-100 ◽  
Author(s):  
Claude F. Lafond ◽  
Alan R. Levander
Keyword(s):  
Heterogeneous Media ◽  
A Priori ◽  
Complex Structure ◽  
Tomographic Reconstruction ◽  
Synthetic Data ◽  
Real Data ◽  
Velocity Model ◽  
Velocity Analysis ◽  
Data Set ◽  
Velocity Models

Prestack depth migration still suffers from the problems associated with building appropriate velocity models. The two main after‐migration, before‐stack velocity analysis techniques currently used, depth focusing and residual moveout correction, have found good use in many applications but have also shown their limitations in the case of very complex structures. To address this issue, we have extended the residual moveout analysis technique to the general case of heterogeneous velocity fields and steep dips, while keeping the algorithm robust enough to be of practical use on real data. Our method is not based on analytic expressions for the moveouts and requires no a priori knowledge of the model, but instead uses geometrical ray tracing in heterogeneous media, layer‐stripping migration, and local wavefront analysis to compute residual velocity corrections. These corrections are back projected into the velocity model along raypaths in a way that is similar to tomographic reconstruction. While this approach is more general than existing migration velocity analysis implementations, it is also much more computer intensive and is best used locally around a particularly complex structure. We demonstrate the technique using synthetic data from a model with strong velocity gradients and then apply it to a marine data set to improve the positioning of a major fault.

Inversion of seismic refraction and reflection data for building long-wavelength velocity models

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. R81-R93 ◽  
Author(s):  
Haiyang Wang ◽  
Satish C. Singh ◽  
Francois Audebert ◽  
Henri Calandra
Keyword(s):  
Wave Equation ◽  
Short Wavelength ◽  
Velocity Model ◽  
Density Model ◽  
Data Set ◽  
Reflected Waves ◽  
Long Wavelength ◽  
Velocity Models ◽  
Computation Efficiency ◽  
Refracted Waves

Long-wavelength velocity model building is a nonlinear process. It has traditionally been achieved without appealing to wave-equation-based approaches for combined refracted and reflected waves. We developed a cascaded wave-equation tomography method in the data domain, taking advantage of the information contained in the reflected and refracted waves. The objective function was the traveltime residual that maximized the crosscorrelation function between real and synthetic data. To alleviate the nonlinearity of the inversion problem, refracted waves were initially used to provide vertical constraints on the velocity model, and reflected waves were then included to provide lateral constraints. The use of reflected waves required scale separation. We separated the long- and short-wavelength subsurface structures into velocity and density models, respectively. The velocity model update was restricted to long wavelengths during the wave-equation tomography, whereas the density model was used to absorb all the short-wavelength impedance contrasts. To improve the computation efficiency, the density model was converted into the zero-offset traveltime domain, where it was invariant to changes of the long-wavelength velocity model. After the wave-equation tomography has derived an optimized long-wavelength velocity model, full-waveform inversion was used to invert all the data to retrieve the short-wavelength velocity structures. We developed our method in two synthetic tests and then applied it to a marine field data set. We evaluated the results of the use of refracted and reflected waves, which was critical for accurately building the long-wavelength velocity model. We showed that our wave-equation tomography strategy was robust for the real data application.

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