Travel-time tomography imaging the Ecuadorian subduction, north of the Mw 7.8 Pedernales earthquake

The Ecuadorian subduction regularly hosts large earthquakes. Among them, the Mw 8.8 1906 earthquake is the 7th biggest known event. Following the recent 2016 Mw 7.8 Pedernales earthquake, a large deployment of onshore/offshore seismological stations, in addition to the permanent seismological/geodetical network, revealed a complex slip behavior including the presence&#160; of&#160; seismic and aseismic slip.During the geophysical experiment HIPER, in march 2020, 47 Ocean Bottom Seismometers (OBS), were densely deployed along a 93-km-long trench-perpendicular profile, recording airgun shots (4990 cu.inch.) performed by R/V Atalante to obtain a high-resolution P-wave velocity image. The profile was located north of the 2016 Pedernales rupture zone passing through an area experiencing aseismic slip and a region of contrasted geodetic interseismic coupling. &#160;&#160;&#160;We used the traveltime tomography code &#171; tomo2d &#187; (Korenaga et al., 2000) to invert first arrivals and reflected phases recorded by our OBS.&#160; A joint 2D-seismic-reflection profile was acquired (abstract by L. Schenini) and provides details on the oceanic basement topography and on Vp velocities in shallow sedimentary layers.Regarding the structural complexity in the region, we decided to start the inversion&#160; using an a priori 2D velocity model. Several geophysical experiments have already been conducted offshore-onshore Ecuador (SISTEUR, 2000 ; SALIERI, 2001 and ESMERALDAS, 2005). Compilation of velocity models from tomographic images were used to build two a priori 1D Vp velocity models for both the Nazca oceanic crust and the forearc seismic structure. A 2D a priori Vp velocity model was built by merging the results of the two localized inversions using a selection of OBS on each side of the trench.We obtain the crustal structure of the upper and subducting plates down to 20 km depth. Beneath the trench, a ~30-km-wide low-Vp anomaly is observed at lithospheric scale. This velocity is 10% lower than the typical Vp values observed for hydrated Pacific-type oceanic crust near the trench (Grevemeyer et al., 2018).&#160;Recorded PmP phases will allow us to further constrain the crustal thickness. While we observe PmP phases in areas of low-Vp, the Moho reflectivity weakens and even disappears from the coincident MCS line. This intriguing observation could highlight processes, such as the presence of fluids or serpentinization, that need to be identified and better understood.

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Seismic Structure of Local Crustal Earthquakes beneath the Zipingpu Reservoir of Longmenshan Fault Zone

International Journal of Geophysics ◽

10.1155/2011/407673 ◽

2011 ◽

Vol 2011 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Haiou Li ◽

Xiwei Xu ◽

Wentao Ma ◽

Ronghua Xie ◽

Jingli Yuan ◽

...

Keyword(s):

Fault Zone ◽

Three Dimensional ◽

P Wave ◽

The Tibetan Plateau ◽

Seismic Structure ◽

Infiltration Depth ◽

Low Velocity ◽

Longmenshan Fault Zone ◽

Velocity Models ◽

Longmenshan Fault

Three-dimensional P wave velocity models under the Zipingpu reservoir in Longmenshan fault zone are obtained with a resolution of 2 km in the horizontal direction and 1 km in depth. We used a total of 8589 P wave arrival times from 1014 local earthquakes recorded by both the Zipingpu reservoir network and temporary stations deployed in the area. The 3-D velocity images at shallow depth show the low-velocity regions have strong correlation with the surface trace of the Zipingpu reservoir. According to the extension of those low-velocity regions, the infiltration depth directly from the Zipingpu reservoir itself is limited to 3.5 km depth, while the infiltration depth downwards along the Beichuan-Yingxiu fault in the study area is about 5.5 km depth. Results show the low-velocity region in the east part of the study area is related to the Proterozoic sedimentary rocks. The Guanxian-Anxian fault is well delineated by obvious velocity contrast and may mark the border between the Tibetan Plateau in the west and the Sichuan basin in the east.

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Multi-parameter model building for the Qiuyue structure using four-component ocean-bottom seismometer data

Geophysics ◽

10.1190/geo2020-0537.1 ◽

2021 ◽

pp. 1-52

Author(s):

Yuzhu Liu ◽

Xinquan Huang ◽

Jizhong Yang ◽

Xueyi Liu ◽

Bin Li ◽

...

Keyword(s):

Wave Velocity ◽

Waveform Inversion ◽

Velocity Model ◽

P Wave ◽

Velocity Analysis ◽

Full Waveform Inversion ◽

Ocean Bottom ◽

Ocean Bottom Seismometer ◽

Full Waveform ◽

P Wave Velocity

Thin sand-mud-coal interbedded layers and multiples caused by shallow water pose great challenges to conventional 3D multi-channel seismic techniques used to detect the deeply buried reservoirs in the Qiuyue field. In 2017, a dense ocean-bottom seismometer (OBS) acquisition program acquired a four-component dataset in East China Sea. To delineate the deep reservoir structures in the Qiuyue field, we applied a full-waveform inversion (FWI) workflow to this dense four-component OBS dataset. After preprocessing, including receiver geometry correction, moveout correction, component rotation, and energy transformation from 3D to 2D, a preconditioned first-arrival traveltime tomography based on an improved scattering integral algorithm is applied to construct an initial P-wave velocity model. To eliminate the influence of the wavelet estimation process, a convolutional-wavefield-based objective function for the preprocessed hydrophone component is used during acoustic FWI. By inverting the waveforms associated with early arrivals, a relatively high-resolution underground P-wave velocity model is obtained, with updates at 2.0 km and 4.7 km depth. Initial S-wave velocity and density models are then constructed based on their prior relationships to the P-wave velocity, accompanied by a reciprocal source-independent elastic full-waveform inversion to refine both velocity models. Compared to a traditional workflow, guided by stacking velocity analysis or migration velocity analysis, and using only the pressure component or other single-component, the workflow presented in this study represents a good approach for inverting the four-component OBS dataset to characterize sub-seafloor velocity structures.

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Two robust imaging methodologies for challenging environments: Wave-equation dispersion inversion of surface waves and guided waves and supervirtual interferometry + tomography for far-offset refractions

Interpretation ◽

10.1190/int-2017-0229.1 ◽

2018 ◽

Vol 6 (4) ◽

pp. SM27-SM37 ◽

Cited By ~ 3

Author(s):

Jing Li ◽

Kai Lu ◽

Sherif Hanafy ◽

Gerard Schuster

Keyword(s):

Wave Equation ◽

Velocity Distribution ◽

Surface Waves ◽

Guided Waves ◽

Velocity Model ◽

Dispersion Curves ◽

P Wave ◽

Head Wave ◽

Near Surface ◽

Velocity Models

Two robust imaging technologies are reviewed that provide subsurface geologic information in challenging environments. The first one is wave-equation dispersion (WD) inversion of surface waves and guided waves (GW) for the shear-velocity (S-wave) and compressional-velocity (P-wave) models, respectively. The other method is traveltime inversion for the velocity model, in which supervirtual refraction interferometry (SVI) is used to enhance the signal-to-noise ratio of far-offset refractions. We have determined the benefits and liabilities of both methods with synthetic seismograms and field data. The benefits of WD are that (1) there is no layered-medium assumption, as there is in conventional inversion of dispersion curves. This means that 2D or 3D velocity models can be accurately estimated from data recorded by seismic surveys over rugged topography, and (2) WD mostly avoids getting stuck in local minima. The liability is that WD for surface waves is almost as expensive as full-waveform inversion (FWI) and, for Rayleigh waves, only recovers the S-velocity distribution to a depth no deeper than approximately 1/2 to 1/3 wavelength of the lowest-frequency surface wave. The limitation for GW is that, for now, it can estimate the P-velocity model by inverting the dispersion curves from GW propagating in near-surface low-velocity zones. Also, WD often requires user intervention to pick reliable dispersion curves. For SVI, the offset of usable refractions can be more than doubled, so that traveltime tomography can be used to estimate a much deeper model of the P-velocity distribution. This can provide a more effective starting velocity model for FWI. The liability is that SVI assumes head-wave first arrivals, not those from strong diving waves.

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Migration moveout analysis and depth focusing

Geophysics ◽

10.1190/1.1443354 ◽

1993 ◽

Vol 58 (1) ◽

pp. 91-100 ◽

Cited By ~ 52

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.

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Hypocenter Determination of Volcano-Tectonic (VT) Earthquake around Agung Volcano in Period October-December 2017 Using a Non-Linear Location Method: A Preliminary Result

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/873/1/012098 ◽

2021 ◽

Vol 873 (1) ◽

pp. 012098

Author(s):

P P Rahsetyo ◽

D P Sahara ◽

A D Nugraha ◽

D K Syahbana ◽

Zulfakriza ◽

...

Keyword(s):

Volcanic Eruptions ◽

Velocity Model ◽

P Wave ◽

Significant Activity ◽

S Wave ◽

Location Method ◽

Hypocenter Determination ◽

Velocity Models ◽

Central Java ◽

Non Linear

Abstract Agung is one of active volcanoes in Indonesia, located on island of Bali. Since 1963, Agung has not had significant activity, until in September 2017 the volcano was active again which was marked by increased seismic activity and eruptions in November 2017. Therefore, to analyze the dynamics and processes of active volcanic eruptions requires an understanding of the structure of the volcano, especially the position of the magma reservoir and its path. The depiction of the structure of this volcano can be analyzed by determining the location of the earthquake due to volcanic activity, especially Volcano-Tectonic (VT) earthquake. In this study, we determined the location of the hypocenter around the Agung using the non-linear location method. VT earthquakes have similar characteristics to tectonic earthquakes so this method can be used to determine the initial hypocenter. The data used in this study came from 8 PVMBG seismographs from October to December 2017. We manually picking arrival time of P- and S-waves from the 3948 VT events found. Pair of P and S wave phases with 18741 P-wave phases and 17237 S-wave phases, plotted in a wadati diagram resulting in a vp/vs ratio of 1.7117. We use 1D velocity models derived from Koulakov with the assumption that the geology of the study area is not much different from the volcanoes in Central Java. The resulting hypocenter distribution shows a very random location and has uncertain X, Y, and Z directions from a range of 0 to 91 km. This study limits this uncertainty to 5 km resulting in a more reliable earthquakes distribution of 3050 events. The results indicate 2 clustered events, a swarm of VT events that occur every month at a depth of 8 to 15 km and there are 2 paths that lead to the top of Agung and SW of that swarm. These preliminary results will be used to update 1D velocity model and relocate the events beneath Agung region for further studies.

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P-wave tomographic model from local bulletin data for improved seismic location in and around Israel

10.5194/egusphere-egu2020-13371 ◽

2020 ◽

Author(s):

Lewis Schardong ◽

Yochai Ben-Horin ◽

Alon Ziv ◽

Hillel Wust-Bloch ◽

Yael Radzyner

Keyword(s):

Eastern Mediterranean ◽

A Priori ◽

Warning System ◽

Velocity Model ◽

P Wave ◽

Seismic Velocities ◽

Fast Marching ◽

Earthquake Early Warning System ◽

Starting Model ◽

Correction Terms

For the past 40 years, the Geophysical Institute of Israel has been in charge of the recording, monitoring and relocating of local earthquakes. Due to the variety of data analysts and data sources, as well as several network upgrades, the resulting bulletin data has to be completed and homogenised, and station metadata needs to be tracked down, and sometimes corrected. For those reasons, as well as because of the lack of consensus on an accurate model for seismic velocities in the area, published source locations are often poorly constrained. We present a homogenised Israeli bulletin, including natural and man-made explosion data. We extract sets of seismic sources with location accuracy greater than 5 km (GT5), as well as GT0 explosions.We select a set of events with the highest network coverage, comprising (1) natural earthquakes, (2) man-made quarry or mine blasts, (3) GT5 earthquakes or explosions, and (4) GT0 explosions. We relocate them altogether using the BayesLoc package, a Bayesian, hierarchical, multi-event locator which produces, after source relocation, event-, station- and phase-specific correction terms. We put different a priori constraints on the different categories of seismic events, allowing poorly constrained origin parameters to improve thanks to the more accurate GT locations. BayesLoc also produces traveltime correction terms that can be used to correct systematic errors in the dataset, as well as error estimates.Eventually, we invert this homogenised local traveltime dataset in order to invert for a P-wave crustal velocity model of Israel and its surroundings. To do so, we use the Fast Marching Tomography package, which allows the representation of a wide variety of input structures (starting model and geometry of layer boundaries) and can take many different types of input data. We show preliminary inversion tests and results that are in good agreement with past local studies.This crustal model of Israel is ultimately to be used as a starting model in a larger tomographic study of the Eastern Mediterranean and Middle East region, where the Regional Seismic Travel Time approach is to be expanded, in order to improve the CTBT&#8217;s capabilities in monitoring the regional seismicity. Eventually, such a velocity model could also be used to relocate the whole earthquake catalogue more accurately, and improve the Earthquake Early Warning System currently in development in Israel.

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Elastic reflection waveform inversion based on the decomposition of sensitivity kernels

Geophysics ◽

10.1190/geo2018-0220.1 ◽

2019 ◽

Vol 84 (2) ◽

pp. R235-R250 ◽

Cited By ~ 1

Author(s):

Zhiming Ren ◽

Zhenchun Li ◽

Bingluo Gu

Keyword(s):

Wave Velocity ◽

Waveform Inversion ◽

Velocity Model ◽

P Wave ◽

Background Model ◽

Reverse Time ◽

S Wave ◽

Velocity Models ◽

Time Migration ◽

Starting Model

Full-waveform inversion (FWI) has the potential to obtain an accurate velocity model. Nevertheless, it depends strongly on the low-frequency data and the initial model. When the starting model is far from the real model, FWI tends to converge to a local minimum. Based on a scale separation of the model (into the background model and reflectivity model), reflection waveform inversion (RWI) can separate out the tomography term in the conventional FWI kernel and invert for the long-wavelength components of the velocity model by smearing the reflected wave residuals along the transmission (or “rabbit-ear”) paths. We have developed a new elastic RWI method to build the P- and S-wave velocity macromodels. Our method exploits a traveltime-based misfit function to highlight the contribution of tomography terms in the sensitivity kernels and a sensitivity kernel decomposition scheme based on the P- and S-wave separation to suppress the high-wavenumber artifacts caused by the crosstalk of different wave modes. Numerical examples reveal that the gradients of the background models become sufficiently smooth owing to the decomposition of sensitivity kernels and the traveltime-based misfit function. We implement our elastic RWI in an alternating way. At each loop, the reflectivity model is generated by elastic least-squares reverse time migration, and then the background model is updated using the separated traveltime kernels. Our RWI method has been successfully applied in synthetic and real reflection seismic data. Inversion results demonstrate that the proposed method can retrieve preferable low-wavenumber components of the P- and S-wave velocity models, which are reliable to serve as a starting model for conventional elastic FWI. Also, our method with a two-stage inversion workflow, first updating the P-wave velocity using the PP kernels and then updating the S-wave velocity using the PS kernels, is feasible and robust even when P- and S-wave velocities have different structures.

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Shallow Shear-Wave Velocity Structure in Oklahoma Based on the Joint Inversion of Ambient Noise Dispersion and Teleseismic P-Wave Receiver Functions

Bulletin of the Seismological Society of America ◽

10.1785/0120200246 ◽

2020 ◽

Author(s):

Jiayan Tan ◽

Charles A. Langston ◽

Sidao Ni

Keyword(s):

Surface Wave ◽

Shear Wave ◽

Wave Velocity ◽

Shear Wave Velocity ◽

Ambient Noise ◽

Sedimentary Basins ◽

Velocity Model ◽

Receiver Functions ◽

P Wave ◽

Velocity Models

ABSTRACT Ambient noise cross-correlations, used to obtain fundamental-mode Rayleigh-wave group velocity estimates, and teleseismic P-wave receiver functions are jointly modeled to obtain a 3D shear-wave velocity model for the crust and upper mantle of Oklahoma. Broadband data from 82 stations of EarthScope Transportable Array, the U.S. National Seismic Network, and the Oklahoma Geological Survey are used. The period range for surface-wave ambient noise Green’s functions is from 4.5 to 30.5 s constraining shear-wave velocity to a depth of 50 km. We also compute high-frequency receiver functions at these stations from 214 teleseismic earthquakes to constrain individual 1D velocity models inferred from the surface-wave tomography. Receiver functions reveal Ps conversions from the Moho, intracrustal interfaces, and shallow sedimentary basins. Shallow low-velocity zones in the model correlate with the large sedimentary basins of Oklahoma. The velocity model significantly improves the agreement of synthetic and observed seismograms from the 6 November 2011 Mw 5.7 Prague, Oklahoma earthquake suggesting that it can be used to improve earthquake location and moment tensor inversion of local and regional earthquakes.

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Time-reverse imaging with limited S-wave velocity model information

Geophysics ◽

10.1190/geo2010-0303.1 ◽

2011 ◽

Vol 76 (5) ◽

pp. MA33-MA40 ◽

Cited By ~ 3

Author(s):

Brian Steiner ◽

Erik H. Saenger ◽

Stefan M. Schmalholz

Keyword(s):

Energy Density ◽

Wave Velocity ◽

Wave Energy ◽

Velocity Model ◽

Body Waves ◽

P Wave ◽

S Wave ◽

Velocity Models ◽

Wave Energy Density ◽

Wavefield Extrapolation

Time-reverse imaging is a wave propagation algorithm for locating sources. Signals recorded by synchronized receivers are reversed in time and propagated back to the source location by elastic wavefield extrapolation. Elastic wavefield extrapolation requires a P-wave as well as an S-wave velocity model. The velocity models available from standard reflection seismic methods are usually restricted to only P-waves. In this study, we use synthetically produced time signals to investigate the accuracy of seismic source localization by means of time-reverse imaging with the correct P-wave and a perturbed S-wave velocity model. The studies reveal that perturbed S-wave velocity models strongly influence the intensity and position of the focus. Imaging the results with the individual maximum energy density for both body wave types instead of mixed modes allows individual analysis of the two body waves. P-wave energy density images render stable focuses in case of a correct P-wave and incorrect S-wave velocity model. Thus, P-wave energy density seems to be a more suitable imaging condition in case of a high degree of uncertainty in the S-wave velocity model.

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Generation and processing of pseudo‐shear‐wave data: Theory and case study

Geophysics ◽

10.1190/1.1635033 ◽

2003 ◽

Vol 68 (6) ◽

pp. 1807-1816 ◽

Cited By ~ 25

Author(s):

Vladimir Grechka ◽

Pawan Dewangan

Keyword(s):

Shear Wave ◽

Pure Shear ◽

Transversely Isotropic ◽

Velocity Model ◽

Velocity Analysis ◽

Ocean Bottom ◽

S Wave ◽

Velocity Models ◽

Conversion Point

Processing of converted (PS) waves currently adopted by the exploration industry is essentially based on resorting the PS data into common‐conversion‐point gathers and using them for velocity analysis. Here, we explore an alternative procedure. Our key idea is to generate the so‐called pseudo‐shear (ΨS) seismograms from the recorded PP and PS traces and run conventional velocity analysis on the reconstructed ΨS data. This results in an effective S‐wave velocity model because our method creates data that possess kinematics of pure shear‐wave primaries. We never deal with such complexities of converted waves as moveout asymmetry, reflection point dispersal, and polarity reversal; therefore, these generally troublesome features become irrelevant. We describe the details of our methodology and examine its behavior both analytically and numerically. We apply the developed processing flow to a four‐component ocean‐bottom cable line acquired in the Gulf of Mexico. Since the obtained stacking velocities of P‐ and ΨS‐waves indicate the presence of effective anisotropy, we proceed with estimating a family of kinematically equivalent vertical transversely isotropic (VTI) velocity models of the subsurface.

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