Joint Inversion of VES and Surface Wave Data

2002 ◽  
Author(s):  
Cesare Comina ◽  
Sebastiano Foti ◽  
Luigi Sambuelli ◽  
Laura V. Socco ◽  
Claudio Strobbia
Keyword(s):  
Surface Wave ◽  
Joint Inversion ◽  
Wave Data ◽  
Surface Wave Data

scholarly journals A Reduced Order Approach for Probabilistic Inversions of 3DMagnetotelluric Data II: Joint inversion of MT and Surface-Wave Data

2021 ◽  
Author(s):  
María Constanza Manassero ◽  
Juan Carlos Afonso ◽  
Fabio Iván Zyserman ◽  
Sergio Zlotnik ◽  
Ilya Fomin
Keyword(s):  
Surface Wave ◽  
Joint Inversion ◽  
Reduced Order ◽  
Wave Data ◽  
Surface Wave Data

Joint Inversion Of Ves And Surface Wave Data

2002 ◽  
Author(s):  
Cesare Comina ◽  
Sebastiano Foti ◽  
Luigi Sambuelli ◽  
Laura V. Socco ◽  
Claudio Strobbia
Keyword(s):  
Surface Wave ◽  
Joint Inversion ◽  
Wave Data ◽  
Surface Wave Data

scholarly journals A Reduced Order Approach for Probabilistic Inversions of 3DMagnetotelluric Data II: Joint inversion of MT and Surface-Wave Data

2021 ◽  
Author(s):  
María Constanza Manassero ◽  
Juan Carlos Carlos Afonso ◽  
Fabio Iván Zyserman ◽  
Sergio Zlotnik ◽  
Ilya Fomin
Keyword(s):  
Surface Wave ◽  
Joint Inversion ◽  
Reduced Order ◽  
Wave Data ◽  
Surface Wave Data

Joint inversion of gravity and surface wave data constrained by magnetotelluric: Application to deep geothermal exploration of crustal fault zone in felsic basement

Geothermics ◽  
2019 ◽  
Vol 80 ◽  
pp. 56-68 ◽  
Author(s):  
Jean-Michel Ars ◽  
Pascal Tarits ◽  
Sophie Hautot ◽  
Mathieu Bellanger ◽  
Olivier Coutant ◽  
...  
Keyword(s):  
Surface Wave ◽  
Fault Zone ◽  
Joint Inversion ◽  
Geothermal Exploration ◽  
Wave Data ◽  
Surface Wave Data

scholarly journals Shear velocity model for the Kyrgyz Tien Shan from joint inversion of receiver function and surface wave data

2014 ◽  
Vol 199 (1) ◽  
pp. 480-498 ◽  
Author(s):  
Amy Gilligan ◽  
Steven W. Roecker ◽  
Keith F. Priestley ◽  
Ceri Nunn
Keyword(s):  
Surface Wave ◽  
Receiver Function ◽  
Joint Inversion ◽  
Shear Velocity ◽  
Velocity Model ◽  
Tien Shan ◽  
Wave Data ◽  
Surface Wave Data

scholarly journals A Reduced Order Approach for Probabilistic Inversions of 3D Magnetotelluric Data II: Joint inversion of 3D MT and Surface-Wave Data

2021 ◽  
Author(s):  
Maria Constanza Manassero ◽  
Juan Afonso ◽  
Fabio Zyserman ◽  
Sergio Zlotnik ◽  
Ilya Fomin
Keyword(s):  
Surface Wave ◽  
Large Scale ◽  
Joint Inversion ◽  
Mantle Metasomatism ◽  
Joint Analysis ◽  
Inversion Method ◽  
Surface Wave Dispersion ◽  
Magnetotelluric Data ◽  
Wave Data ◽  
Surface Wave Data

Joint probabilistic inversions of magnetotelluric (MT) and seismic data has great potential for imaging the thermochemical structure of the lithosphere as well as mapping fluid/melt pathways and regions of mantle metasomatism. In this contribution we present a novel probabilistic (Bayesian) joint inversion scheme for 3D MT and surface-wave dispersion data particularly designed for large-scale lithospheric studies. The approach makes use of a recently developed strategy for fast solutions of the 3D MT forward problem (Manassero et al.,2020) and combines it with adaptive Markov chain Monte Carlo (MCMC) algorithms and parallel-in-parallel strategies to achieve extremely efficient simulations. To demonstrate the feasibility, benefits and performance of our joint inversion method to image the temperature and conductivity structures of the lithosphere, we apply it to two numerical examples of increasing complexity. The inversion approach presented here is timely and will be useful in the joint analysis of MT and surface wave data that are being collected in many parts of the world. This approach also opens up new avenues for the study of translithospheric and transcrustal magmatic systems, the detection of metasomatised mantle and the incorporation of MT into multi-observable inversions for the physical state of the Earth's interior.

scholarly journals A Reduced Order Approach for Probabilistic Inversions of 3D Magnetotelluric Data II: Joint inversion of MT and Surface-Wave Data

2021 ◽  
Author(s):  
Maria Constanza Manassero ◽  
Juan Afonso ◽  
Fabio Zyserman ◽  
Sergio Zlotnik ◽  
Ilya Fomin
Keyword(s):  
Surface Wave ◽  
Joint Inversion ◽  
Magnetotelluric Data ◽  
Reduced Order ◽  
Wave Data ◽  
Surface Wave Data

scholarly journals Imaging the subsurface using induced seismicity and ambient noise: 3-D tomographic Monte Carlo joint inversion of earthquake body wave traveltimes and surface wave dispersion

2020 ◽  
Vol 222 (3) ◽  
pp. 1639-1655
Author(s):  
Xin Zhang ◽  
Corinna Roy ◽  
Andrew Curtis ◽  
Andy Nowacki ◽  
Brian Baptie
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Velocity Structure ◽  
Joint Inversion ◽  
Body Wave ◽  
Source Parameters ◽  
Wave Dispersion ◽  
Inversion Method ◽  
Surface Wave Dispersion ◽  
Wave Data

SUMMARY Seismic body wave traveltime tomography and surface wave dispersion tomography have been used widely to characterize earthquakes and to study the subsurface structure of the Earth. Since these types of problem are often significantly non-linear and have non-unique solutions, Markov chain Monte Carlo methods have been used to find probabilistic solutions. Body and surface wave data are usually inverted separately to produce independent velocity models. However, body wave tomography is generally sensitive to structure around the subvolume in which earthquakes occur and produces limited resolution in the shallower Earth, whereas surface wave tomography is often sensitive to shallower structure. To better estimate subsurface properties, we therefore jointly invert for the seismic velocity structure and earthquake locations using body and surface wave data simultaneously. We apply the new joint inversion method to a mining site in the United Kingdom at which induced seismicity occurred and was recorded on a small local network of stations, and where ambient noise recordings are available from the same stations. The ambient noise is processed to obtain inter-receiver surface wave dispersion measurements which are inverted jointly with body wave arrival times from local earthquakes. The results show that by using both types of data, the earthquake source parameters and the velocity structure can be better constrained than in independent inversions. To further understand and interpret the results, we conduct synthetic tests to compare the results from body wave inversion and joint inversion. The results show that trade-offs between source parameters and velocities appear to bias results if only body wave data are used, but this issue is largely resolved by using the joint inversion method. Thus the use of ambient seismic noise and our fully non-linear inversion provides a valuable, improved method to image the subsurface velocity and seismicity.

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