scholarly journals Relocation of the December 20, 2007 Bala earthquake (Mw 5.4) and it's aftershocks by using a new one-dimensional seismic velocity model for the Central Anatolia

2018 ◽  
Author(s):  
BEGÜM ÇIVGIN ◽  
BÜLENT KAYPAK
Keyword(s):  
Seismic Velocity ◽  
Velocity Model ◽  
Central Anatolia ◽  
One Dimensional

scholarly journals One dimensional seismic velocity structure beneath Western Nepal

2002 ◽  
Vol 27 ◽  
Author(s):  
S. Rajaure
Keyword(s):  
Arrival Time ◽  
Velocity Structure ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Compressional Wave ◽  
S Wave ◽  
Time Data ◽  
Seismic Velocity Structure ◽  
One Dimensional ◽  
Wave Velocities

An attempt has been made to study the velocity structure of western Nepal. Arrival time data of local earthquakes occurring in that region were used to derive the model. A three layered velocity model both for the P- as well as S-wave velocity has been estimated. The compressional wave velocities in the first, second and the third layers have been estimated to be 5.53 km/sec. 6.29 km/sec and 8.13 km/sec respectively. Similarly the corresponding S-wave velocities are 3.18, 3.62, 4.66 km/sec respectively. The model for the Western Nepal and that for the Centre-East Nepal are almost same. The crust and the mantle beneath west and center-east are homogeneous.

Super-Resolution of Seismic Velocity Model Guided by Seismic Data

2021 ◽  
pp. 1-12
Author(s):  
Yinshuo Li ◽  
Jianyong Song ◽  
Wenkai Lu ◽  
Patrice Monkam ◽  
Yile Ao
Keyword(s):  
Seismic Data ◽  
Seismic Velocity ◽  
Super Resolution ◽  
Velocity Model

scholarly journals Travel Time Tomography to Delineate 3-D Regional Seismic Velocity Structure in the Banyumas Basin, Central Java, Indonesia, Using Dense Borehole Seismographic Stations

2021 ◽  
Vol 9 ◽  
Author(s):  
Hidayat Hidayat ◽  
Andri Dian Nugraha ◽  
Awali Priyono ◽  
Marjiyono Marjiyono ◽  
Januar H. Setiawan ◽  
...  
Keyword(s):  
Velocity Structure ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Crustal Layer ◽  
Central Java ◽  
Resolution Element ◽  
Passive Seismic ◽  
Dextral Strike Slip Fault ◽  
Southern Central ◽  
Well Data

The Banyumas Basin is a tertiary sedimentary basin located in southern Central Java, Indonesia. Due to the presence of volcanic deposits, 2-D seismic reflection methods cannot provide a good estimation of the sediment thickness and the subsurface geology structure in this area. In this study, the passive seismic tomography (PST) method was applied to image the 3-D subsurface Vp, Vs, and Vp/Vs ratio. We used 70 seismograph borehole stations with a recording duration of 177 days. A total of 354 events with 9, 370 P and 9, 368 S phases were used as input for tomographic inversion. The checkshot data of a 4, 400-meter deep exploration well (Jati-1) located within the seismic network were used to constrain the shallow crustal layer of the initial 1-D velocity model. The model resolution of the tomographic inversions was assessed using the checkerboard resolution test (CRT), the diagonal resolution element (DRE), and the derivative weight sum (DWS). Using the obtained Vp, Vs, and Vp/Vs ratio, we were able to sharpen details of the geological structures within the basin from previous geological studies, and a fault could be well-imaged at a depth of 4 km. We interpreted this as the main dextral strike-slip fault that controls the pull apart process of the Banyumas Basin. The thickness of the sediment layers, as well as its layering, were also could be well determined. We found prominent features of the velocity contrast that aligned very well with the boundary between the Halang and Rambatan formations as observed in the Jati-1 well data. Furthermore, an anticline structure, which is a potential structural trap for the petroleum system in the Banyumas Basin, was also well imaged. This was made possible due to the dense borehole seismographic stations which were deployed in the study area.

scholarly journals Antarctic Upper Mantle Rheology

2021 ◽  
pp. M56-2020-19
Author(s):  
E. R. Ivins ◽  
W. van der Wal ◽  
D. A. Wiens ◽  
A. J. Lloyd ◽  
L. Caron
Keyword(s):  
Wave Velocity ◽  
Upper Mantle ◽  
Effective Viscosity ◽  
Seismic Velocity ◽  
Imaging Techniques ◽  
Three Dimensional ◽  
Velocity Model ◽  
Mantle Flow ◽  
S Wave ◽  
Velocity Changes

AbstractThe Antarctic mantle and lithosphere are known to have large lateral contrasts in seismic velocity and tectonic history. These contrasts suggest differences in the response time scale of mantle flow across the continent, similar to those documented between the northeastern and southwestern upper mantle of North America. Glacial isostatic adjustment and geodynamical modeling rely on independent estimates of lateral variability in effective viscosity. Recent improvements in imaging techniques and the distribution of seismic stations now allow resolution of both lateral and vertical variability of seismic velocity, making detailed inferences about lateral viscosity variations possible. Geodetic and paleo sea-level investigations of Antarctica provide quantitative ways of independently assessing the three-dimensional mantle viscosity structure. While observational and causal connections between inferred lateral viscosity variability and seismic velocity changes are qualitatively reconciled, significant improvements in the quantitative relations between effective viscosity anomalies and those imaged by P- and S-wave tomography have remained elusive. Here we describe several methods for estimating effective viscosity from S-wave velocity. We then present and compare maps of the viscosity variability beneath Antarctica based on the recent S-wave velocity model ANT-20 using three different approaches.

High-resolution seismic velocity analysis by sign-based weighted semblance

Geophysics ◽  
2021 ◽  
pp. 1-35
Author(s):  
M. Javad Khoshnavaz
Keyword(s):  
Seismic Velocity ◽  
Resolution Enhancement ◽  
Synthetic Data ◽  
Correlation Coefficients ◽  
Velocity Model ◽  
Seismic Attenuation ◽  
Velocity Analysis ◽  
Velocity Models ◽  
Amplitude Versus Offset ◽  
Amplitude Versus

Building an accurate velocity model plays a vital role in routine seismic imaging workflows. Normal-moveout-based seismic velocity analysis is a popular method to make the velocity models. However, traditional velocity analysis methodologies are not generally capable of handling amplitude variations across moveout curves, specifically polarity reversals caused by amplitude-versus-offset anomalies. I present a normal-moveout-based velocity analysis approach that circumvents this shortcoming by modifying the conventional semblance function to include polarity and amplitude correction terms computed using correlation coefficients of seismic traces in the velocity analysis scanning window with a reference trace. Thus, the proposed workflow is suitable for any class of amplitude-versus-offset effects. The approach is demonstrated to four synthetic data examples of different conditions and a field data consisting a common-midpoint gather. Lateral resolution enhancement using the proposed workflow is evaluated by comparison between the results from the workflow and the results obtained by the application of conventional semblance and three semblance-based velocity analysis algorithms developed to circumvent the challenges associated with amplitude variations across moveout curves, caused by seismic attenuation and class II amplitude-versus-offset anomalies. According to the obtained results, the proposed workflow is superior to all the presented workflows in handling such anomalies.

scholarly journals Seismic Velocity Structure in the Area of the 2007, Mw 8.0, Pisco-Peru Earthquake: Implications for the Mechanics of Subduction in the Vicinity of the Nazca Ridge

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
I. Bernal ◽  
H. Tavera
Keyword(s):  
Velocity Structure ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Double Difference ◽  
Seismic Velocity Structure ◽  
Uplift Rates ◽  
Northern Edge ◽  
Nazca Ridge ◽  
Geomorphological Features ◽  
Postseismic Slip

In this study, we present a velocity model for the area of the 2007 Pisco-Peru earthquake ( Mw = 8.0 ) obtained using a double-difference tomography algorithm that considers aftershocks acquired for 6 months. The studied area is particularly interesting because it lies on the northern edge of the Nazca Ridge, in which the subduction of a large bathymetric structure is the origin of geomorphological features of the central coast of Peru. Relocated seismicity is used to infer the geometry of the subduction slab on the northern flank of the Nazca Ridge. The results prove that the geometry is continuous but convex because of the subduction of the ridge, thereby explaining the high uplift rates observed in this area. Our inferred distribution of seismicity agrees with both the coseismic and postseismic slip distributions.

scholarly journals Seismic velocity estimation: A deep recurrent neural-network approach

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. U21-U29
Author(s):  
Gabriel Fabien-Ouellet ◽  
Rahul Sarkar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Model Building ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Interval Velocity ◽  
Velocity Model Building ◽  
3D Velocity Model

Applying deep learning to 3D velocity model building remains a challenge due to the sheer volume of data required to train large-scale artificial neural networks. Moreover, little is known about what types of network architectures are appropriate for such a complex task. To ease the development of a deep-learning approach for seismic velocity estimation, we have evaluated a simplified surrogate problem — the estimation of the root-mean-square (rms) and interval velocity in time from common-midpoint gathers — for 1D layered velocity models. We have developed a deep neural network, whose design was inspired by the information flow found in semblance analysis. The network replaces semblance estimation by a representation built with a deep convolutional neural network, and then it performs velocity estimation automatically with recurrent neural networks. The network is trained with synthetic data to identify primary reflection events, rms velocity, and interval velocity. For a synthetic test set containing 1D layered models, we find that rms and interval velocity are accurately estimated, with an error of less than [Formula: see text] for the rms velocity. We apply the neural network to a real 2D marine survey and obtain accurate rms velocity predictions leading to a coherent stacked section, in addition to an estimation of the interval velocity that reproduces the main structures in the stacked section. Our results provide strong evidence that neural networks can estimate velocity from seismic data and that good performance can be achieved on real data even if the training is based on synthetics. The findings for the 1D problem suggest that deep convolutional encoders and recurrent neural networks are promising components of more complex networks that can perform 2D and 3D velocity model building.

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