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 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.

Rayleigh Wave Analysis in the Presence of High Impedance Boundaries

2020 ◽  
Author(s):  
Gabriel Gribler
Keyword(s):  
Surface Wave ◽  
Shear Wave ◽  
High Velocity ◽  
Inversion Method ◽  
Surface Wave Dispersion ◽  
Wave Data ◽  
Wave Velocities ◽  
Shear Wave Velocities ◽  
High Impedance ◽  
Surface Wave Data

Surface wave data is commonly used to estimate shear wave velocity of the subsurface. Most standard approaches for analyzing surface wave data fail under conditions when high-impedance boundaries, or sharp contrasts, exists within the range of sensitivities. I present two primary scenarios, one with a high velocity bedrock layer in the upper 20 meters overlain by low velocity unconsolidated sediment, and a thin high velocity road layer on top of unconsolidated sediments. For the shallow bedrock case, I present new multicomponent methods to more accurately and reliably extract surface wave dispersion information from active source waveforms. I also present a new data inversion method that utilizes additional information from multicomponent wavefields, allowing for more accurate estimates of shear wave velocities in these environments. For the thin, high velocity surface layer, I highlight the potential pitfalls of ignoring this layer when inverting for the underlying shear wave velocities, and I propose a solution that yields more accurate velocity estimates. All of these approaches are explained and presented using modeled data, then extended to highlight the improvements over standard approaches using real data.

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 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

Effects of thin soft layers on body waves

1969 ◽  
Vol 59 (5) ◽  
pp. 2071-2078
Author(s):  
Tom Landers ◽  
Jon F. Claerbout
Keyword(s):  
Surface Wave ◽  
Upper Mantle ◽  
Body Wave ◽  
Wave Dispersion ◽  
Soft Material ◽  
Body Waves ◽  
Surface Wave Dispersion ◽  
Wave Data ◽  
Use Of Time ◽  
Surface Wave Data

abstract The inability of simple layered models to fit both Rayleigh wave and Love wave data has led to the proposal of an upper mantle interleaved with thin soft horizontal layers. Since surface-wave dispersion is not sensitive to the distribution of soft material but only to the fraction of soft material a variety of models is possible. The solution to this indeterminancy is found through body-wave analysis. It is shown that body waves are dispersed according to the thinness and softness of the layers. Three models, each of which satisfy all surface-wave data, are examined. Transmission seismograms calculated for these models show one to be impossible, one improbable and the other possible. Synthesis of the seismograms is accomplished through the use of time domain theory as the complicated frequency response of the models makes a frequency oriented Haskell-Thompson approach impractical.

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

Joint inversion of two-dimensional magnetotelluric and surface wave dispersion data with cross-gradient constraints

2020 ◽  
Vol 221 (2) ◽  
pp. 938-950
Author(s):  
Pingping Wu ◽  
Handong Tan ◽  
Changhong Lin ◽  
Miao Peng ◽  
Huan Ma ◽  
...  
Keyword(s):  
Surface Wave ◽  
Velocity Structure ◽  
Joint Inversion ◽  
Wave Dispersion ◽  
Uniqueness Problem ◽  
Inversion Method ◽  
Inversion Algorithm ◽  
Surface Wave Dispersion ◽  
Gradient Constraints ◽  
Dispersion Data

SUMMARY Multiphysics imaging for data inversion is of growing importance in many branches of science and engineering. Cross-gradient constraint has been considered as a feasible way to reduce the non-uniqueness problem inherent in inversion process by finding geometrically consistent images from multigeophysical data. Based on OCCAM inversion algorithm, a direct inversion method of 2-D profile velocity structure with surface wave dispersion data is proposed. Then we jointly invert the profiles of magnetotelluric and surface wave dispersion data with cross-gradient constraints. Three synthetic models, including block homogeneous or heterogeneous models with consistent or inconsistent discontinuities in velocity and resistivity, are presented to gauge the performance of the joint inversion scheme. We find that owning to the complementary advantages of the two geophysical data sets, the models recovered with structure coupling constraints exhibit higher resolution in the classification of complex geologic units and settle some imaging problems caused by the separate inversion methods. Finally, a realistic velocity model from the NE Tibetan Plateau and its corresponding resistivity model calculated by empirical law are used to test the effectiveness of the joint inversion scheme in the real geological environment.

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
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