scholarly journals Seismic Structure of the Changbai Intraplate Volcano in NE China From Joint Inversion of Ambient Noise and Receiver Functions

2019 ◽  
Vol 124 (5) ◽  
pp. 4984-5002 ◽  
Author(s):  
Hongxiang Zhu ◽  
You Tian ◽  
Dapeng Zhao ◽  
Honghao Li ◽  
Cai Liu
Keyword(s):  
Ambient Noise ◽  
Joint Inversion ◽  
Receiver Functions ◽  
Seismic Structure ◽  
Ne China

scholarly journals Seismic structure of the Changbai intraplate volcano from joint inversion of ambient noise and receiver functions

2019 ◽  
Vol 93 (S1) ◽  
pp. 262-262
Author(s):  
Hongxiang Zhu ◽  
You Tian ◽  
Dapeng Zhao ◽  
Honghao Li ◽  
Cai Liu
Keyword(s):  
Ambient Noise ◽  
Joint Inversion ◽  
Receiver Functions ◽  
Seismic Structure

scholarly journals The Seismic Potential in the Seismic Gap Between the Wenchuan and Lushan Earthquakes Revealed by the Joint Inversion of Receiver Functions and Ambient Noise Data

Tectonics ◽  
2018 ◽  
Vol 37 (11) ◽  
pp. 4226-4238 ◽  
Author(s):  
Zhiqiang Liu ◽  
Chuntao Liang ◽  
Qian Hua ◽  
Ying Li ◽  
Yihai Yang ◽  
...  
Keyword(s):  
Ambient Noise ◽  
Joint Inversion ◽  
Receiver Functions ◽  
Seismic Gap ◽  
Seismic Potential ◽  
Noise Data

Seismic structure across central Myanmar from joint inversion of receiver functions and Rayleigh wave dispersion

2021 ◽  
pp. 229068
Author(s):  
Yiming Bai ◽  
Yumei He ◽  
Xiaohui Yuan ◽  
Frederik Tilmann ◽  
Yinshuang Ai ◽  
...  
Keyword(s):  
Rayleigh Wave ◽  
Joint Inversion ◽  
Wave Dispersion ◽  
Receiver Functions ◽  
Seismic Structure ◽  
Rayleigh Wave Dispersion

scholarly journals Structure of the Ecuadorian forearc from the joint inversion of receiver functions and ambient noise surface waves

2020 ◽  
Vol 222 (3) ◽  
pp. 1671-1685 ◽  
Author(s):  
Clinton D Koch ◽  
Colton Lynner ◽  
Jonathan Delph ◽  
Susan L Beck ◽  
Anne Meltzer ◽  
...  
Keyword(s):  
Oceanic Crust ◽  
Crustal Structure ◽  
Ambient Noise ◽  
Joint Inversion ◽  
Receiver Functions ◽  
Surface Wave Dispersion ◽  
Data Set ◽  
Megathrust Earthquakes ◽  
High Magnitude ◽  
Accreted Terranes

SUMMARY The Ecuadorian forearc is a complex region of accreted terranes with a history of large megathrust earthquakes. Most recently, a Mw 7.8 megathrust earthquake ruptured the plate boundary offshore of Pedernales, Ecuador on 16 April 2016. Following this event, an international collaboration arranged by the Instituto Geofisico at the Escuela Politécnica Nacional mobilized a rapid deployment of 65 seismic instruments along the Ecuadorian forearc. We combine this new seismic data set with 14 permanent stations from the Ecuadorian national network to better understand how variations in crustal structure relate to regional seismic hazards along the margin. Here, we present receiver function adaptive common conversion point stacks and a shear velocity model derived from the joint inversion of receiver functions and surface wave dispersion data obtained through ambient noise cross-correlations for the upper 50 km of the forearc. Beneath the forearc crust, we observe an eastward dipping slow velocity anomaly we interpret as subducting oceanic crust, which shallows near the projected centre of the subducting Carnegie Ridge. We also observe a strong shallow positive conversion in the Ecuadorian forearc near the Borbon Basin indicating a major discontinuity at a depth of ∼7 km. This conversion is not ubiquitous and may be the top of the accreted terranes. We also observe significant north–south changes in shear wave velocity. The velocity changes indicate variations in the accreted terranes and may indicate an increased amount of hydration beneath the Manabí Basin. This change in structure also correlates geographically with the southern rupture limit of multiple high magnitude megathrust earthquakes. The earthquake record along the Ecuadorian trench shows that no event with a Mw >7.4 has ruptured south of ∼0.5°S in southern Ecuador or northern Peru. Our observations, along with previous studies, suggest that variations in the forearc crustal structure and subducting oceanic crust may influance the occurrence and spatial distribution of high magnitude seismicity in the region.

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.

scholarly journals Velocity structure and radial anisotropy of the lithosphere in southern Madagascar from surface wave dispersion

2020 ◽  
Vol 224 (3) ◽  
pp. 1930-1944 ◽  
Author(s):  
E J Rindraharisaona ◽  
F Tilmann ◽  
X Yuan ◽  
J Dreiling ◽  
J Giese ◽  
...  
Keyword(s):  
Receiver Functions ◽  
Dispersion Curves ◽  
Eastern Coast ◽  
Small Scale ◽  
Seismic Structure ◽  
Lithospheric Thickness ◽  
Velocity Models ◽  
Phase Velocities ◽  
Maximum Value ◽  
Radial Anisotropy

SUMMARY We investigate the upper mantle seismic structure beneath southern Madagascar and infer the imprint of geodynamic events since Madagascar’s break-up from Africa and India and earlier rifting episodes. Rayleigh and Love wave phase velocities along a profile across southern Madagascar were determined by application of the two-station method to teleseismic earthquake data. For shorter periods (<20 s), these data were supplemented by previously published dispersion curves determined from ambient noise correlation. First, tomographic models of the phase velocities were determined. In a second step, 1-D models of SV and SH wave velocities were inverted based on the dispersion curves extracted from the tomographic models. As the lithospheric mantle is represented by high velocities we identify the lithosphere–asthenosphere boundary by the strongest negative velocity gradient. Finally, the radial anisotropy (RA) is derived from the difference between the SV and SH velocity models. An additional constraint on the lithospheric thickness is provided by the presence of a negative conversion seen in S receiver functions, which results in comparable estimates under most of Madagascar. We infer a lithospheric thickness of 110−150 km beneath southern Madagascar, significantly thinner than beneath the mobile belts in East Africa (150−200 km), where the crust is of comparable age and which were located close to Madagascar in Gondwanaland. The lithospheric thickness is correlated with the geological domains. The thinnest lithosphere (∼110 km) is found beneath the Morondava basin. The pre-breakup Karoo failed rifting, the rifting and breakup of Gondwanaland have likely thinned the lithosphere there. The thickness of the lithosphere in the Proterozoic terranes (Androyen and Anosyen domains) ranges from 125 to 140 km, which is still ∼30 km thinner than in the Mozambique belt in Tanzania. The lithosphere is the thickest beneath Ikalamavony domain (Proterozoic) and the west part of the Antananarivo domain (Archean) with a thickness of ∼150 km. Below the eastern part of Archean domain the lithosphere thickness reduces to ∼130 km. The lithosphere below the entire profile is characterized by positive RA. The strongest RA is observed in the uppermost mantle beneath the Morondava basin (maximum value of ∼9 per cent), which is understandable from the strong stretching that the basin was exposed to during the Karoo and subsequent rifting episode. Anisotropy is still significantly positive below the Proterozoic (maximum value of ∼5 per cent) and Archean (maximum value of ∼6 per cent) domains, which may result from lithospheric extension during the Mesozoic and/or thereafter. In the asthenosphere, a positive RA is observed beneath the eastern part Morondava sedimentary basin and the Proterozoic domain, indicating a horizontal asthenospheric flow pattern. Negative RA is found beneath the Archean in the east, suggesting a small-scale asthenospheric upwelling, consistent with previous studies. Alternatively, the relatively high shear wave velocity in the asthenosphere in this region indicate that the negative RA could be associated to the Réunion mantle plume, at least beneath the volcanic formation, along the eastern coast.

scholarly journals Reliable workflow for inversion of seismic receiver function and surface wave dispersion data: a “13 BB Star” case study

2019 ◽  
Vol 24 (1) ◽  
pp. 101-120
Author(s):  
Kajetan Chrapkiewicz ◽  
Monika Wilde-Piórko ◽  
Marcin Polkowski ◽  
Marek Grad
Keyword(s):  
Surface Wave ◽  
Inverse Problems ◽  
Receiver Function ◽  
Joint Inversion ◽  
Wave Dispersion ◽  
Receiver Functions ◽  
P Wave ◽  
Surface Wave Dispersion ◽  
Phase Velocity Dispersion ◽  
Wide Range

AbstractNon-linear inverse problems arising in seismology are usually addressed either by linearization or by Monte Carlo methods. Neither approach is flawless. The former needs an accurate starting model; the latter is computationally intensive. Both require careful tuning of inversion parameters. An additional challenge is posed by joint inversion of data of different sensitivities and noise levels such as receiver functions and surface wave dispersion curves. We propose a generic workflow that combines advantages of both methods by endowing the linearized approach with an ensemble of homogeneous starting models. It successfully addresses several fundamental issues inherent in a wide range of inverse problems, such as trapping by local minima, exploitation of a priori knowledge, choice of a model depth, proper weighting of data sets characterized by different uncertainties, and credibility of final models. Some of them are tackled with the aid of novel 1D checkerboard tests—an intuitive and feasible addition to the resolution matrix. We applied our workflow to study the south-western margin of the East European Craton. Rayleigh wave phase velocity dispersion and P-wave receiver function data were gathered in the passive seismic experiment “13 BB Star” (2013–2016) in the area of the crust recognized by previous borehole and refraction surveys. Final models of S-wave velocity down to 300 km depth beneath the array are characterized by proximity in the parameter space and very good data fit. The maximum value in the mantle is higher by 0.1–0.2 km/s than reported for other cratons.

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