scholarly journals Minimum 1D Velocity Model and Local Magnitude Scale for Myanmar

2019 ◽  
Author(s):  
Hasbi Ash Shiddiqi ◽  
Pa Pa Tun ◽  
Lars Ottemöller
Keyword(s):  
Travel Time ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Magnitude Scale ◽  
Local Magnitude ◽  
Waveform Data ◽  
Velocity Inversion ◽  
Hypocentral Distance ◽  
Velocity Models ◽  
1D Velocity Model

ABSTRACT Earthquake monitoring in Myanmar has improved in recent years because of an increased number of seismic stations. This provides a good quality dataset to derive a minimum 1D velocity model and local magnitude (ML) scale for the Myanmar region, which will improve the earthquake location and magnitude estimates in this region. We combined and reprocessed earthquake catalogs from the Department of Meteorology and Hydrology of Myanmar and the International Seismological Centre. Additional waveform data from various sources were processed as well. A total of 419 earthquakes were selected based on azimuthal gap, minimum number of stations, and root mean square travel‐time residuals. A set of initial seismic velocity models was derived from various seismic velocity models. These models were randomly perturbed and used as initial models in a coupled hypocenter and 1D seismic velocity inversion procedure. We compared the average mean travel‐time residuals from the initial and inverted models. The best final model showed an improvement of location standard errors compared to the old model. Furthermore, the local magnitude scale inversion for the Myanmar region was performed using 194 earthquakes having a minimum of two amplitude observations. The following ML scale was obtained ML=logA(nm)+1.485×logR(km)+0.00118×R(km)−2.77+S. This scale is valid for hypocentral distance up to 1000 km and magnitudes up to ML 6.2.

CSEM-regularized seismic velocity inversion: A multiscale, hierarchical workflow for subsalt imaging

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. B241-B252 ◽  
Author(s):  
Daniele Colombo ◽  
Diego Rovetta ◽  
Ersan Turkoglu
Keyword(s):  
Seismic Velocity ◽  
Fundamental Problem ◽  
Velocity Model ◽  
Seismic Inversion ◽  
Velocity Fields ◽  
Coupling Mechanism ◽  
Correct Result ◽  
Velocity Analysis ◽  
Velocity Inversion ◽  
Velocity Models

Seismic imaging in salt geology is complicated by highly contrasted velocity fields and irregular salt geometries, which cause complex seismic wavefield scattering. Although the imaging challenges can be addressed by advanced imaging algorithms, a fundamental problem remains in the determination of robust velocity fields in high-noise conditions. Conventional migration velocity analysis is often ineffective, and even the most advanced methods for depth-domain velocity analysis, such as full-waveform inversion, require starting from a good initial estimate of the velocity model to converge to a correct result. Nonseismic methods, such as electromagnetics, can help guide the generation of robust velocity models to be used for further processing. Using the multiphysics data acquired in the deepwater section of the Red Sea, we apply a controlled-source electromagnetic (CSEM) resistivity-regularized seismic velocity inversion for enhancing the velocity model in a complex area dominated by nappe-style salt tectonics. The integration is achieved by a rigorous approach of multiscaled inversions looping over model dimensions (1D first, followed by 3D), variable offsets and increasing frequencies, data-driven and interpretation-supported approaches, leading to a hierarchical inversion guided by a parameter sensitivity analysis. The final step of the integration consists of the inversion of seismic traveltimes subject to CSEM model constraints in which a common-structure coupling mechanism is used. Minimization is performed over the seismic data residuals and cross-gradient objective functions without inverting for the resistivity model, which is used as a reference for the seismic inversion (hierarchical approach). Results are demonstrated through depth imaging in which the velocity model derived through CSEM-regularized hierarchical inversion outperforms the results of a seismic-only derived velocity model.

scholarly journals Earthquake location in tectonic structures of the Alpine Chain: the case of the Constance Lake (Central Europe) seismic sequence

2021 ◽  
Author(s):  
Francesca D’Ajello Caracciolo ◽  
Rodolfo Console
Keyword(s):  
Central Europe ◽  
Velocity Model ◽  
Moho Depth ◽  
Seismic Sequence ◽  
Tectonic Structures ◽  
Study Region ◽  
Waveform Data ◽  
Seismic Stations ◽  
Velocity Models ◽  
Master Event

AbstractA set of four magnitude Ml ≥ 3.0 earthquakes including the magnitude Ml = 3.7 mainshock of the seismic sequence hitting the Lake Constance, Southern Germany, area in July–August 2019 was studied by means of bulletin and waveform data collected from 86 seismic stations of the Central Europe-Alpine region. The first single-event locations obtained using a uniform 1-D velocity model, and both fixed and free depths, showed residuals of the order of up ± 2.0 s, systematically affecting stations located in different areas of the study region. Namely, German stations to the northeast of the epicenters and French stations to the west exhibit negative residuals, while Italian stations located to the southeast are characterized by similarly large positive residuals. As a consequence, the epicentral coordinates were affected by a significant bias of the order of 4–5 km to the NNE. The locations were repeated applying a method that uses different velocity models for three groups of stations situated in different geological environments, obtaining more accurate locations. Moreover, the application of two methods of relative locations and joint hypocentral determination, without improving the absolute location of the master event, has shown that the sources of the four considered events are separated by distances of the order of one km both in horizontal coordinates and in depths. A particular attention has been paid to the geographical positions of the seismic stations used in the locations and their relationship with the known crustal features, such as the Moho depth and velocity anomalies in the studied region. Significant correlations between the observed travel time residuals and the crustal structure were obtained.

The relationship between Vs, Vp, density and depth based on PS-logging data at K-NET and KiK-net sites

2021 ◽  
Author(s):  
Fumiaki Nagashima ◽  
Hiroshi Kawase
Keyword(s):  
Standard Deviation ◽  
Seismic Velocity ◽  
Saturated Soil ◽  
P Wave ◽  
Soil Types ◽  
Depth Range ◽  
Velocity Inversion ◽  
Velocity Models ◽  
Regression Curves ◽  
Ps Logging

Summary P-wave velocity (Vp) is an important parameter for constructing seismic velocity models of the subsurface structures by using microtremors and earthquake ground motions or any other geophysical exploration data. In order to reflect the ground survey information in Japan to the Vp structure, we investigated the relationships among Vs, Vp, and depth by using PS-logging data at all K-NET and KiK-net sites. Vp values are concentrated at around 500 m/s and 1,500 m/s when Vs is lower than 1,000 m/s, where these concentrated areas show two distinctive characteristics of unsaturated and saturated soil, respectively. Many Vp values in the layer shallower than 4 m are around 500 m/s, which suggests the dominance of unsaturated soil, while many Vp values in the layer deeper than 4 m are larger than 1,500 m/s, which suggests the dominance of saturated soil there. We also investigated those relationships for different soil types at K-NET sites. Although each soil type has its own depth range, all soil types show similar relationships among Vs, Vp, and depth. Then, considering the depth profile of Vp, we divided the dataset into two by the depth, which is shallower or deeper than 4 m, and calculated the geometrical mean of Vp and the geometrical standard deviation in every Vs bins of 200 m/s. Finally, we obtained the regression curves for the average and standard deviation of Vp estimated from Vs to get the Vp conversion functions from Vs, which can be applied to a wide Vs range. We also obtained the regression curves for two datasets with Vp lower and higher than 1,200 m/s. These regression curves can be applied when the groundwater level is known. In addition, we obtained the regression curves for density from Vs or Vp. An example of the application for those relationships in the velocity inversion is shown.

Comparing Higher-dimensional Velocity Models for Seismic Location Accuracy using a Consistent Travel Time Framework

2021 ◽  
Author(s):  
Michael Begnaud ◽  
Sanford Ballard ◽  
Andrea Conley ◽  
Patrick Hammond ◽  
Christopher Young
Keyword(s):  
Travel Time ◽  
Ray Tracing ◽  
Seismic Velocity ◽  
3D Seismic ◽  
Location Accuracy ◽  
Velocity Models ◽  
Higher Dimensional ◽  
1D Model ◽  
Tracing Algorithm ◽  
2D And 3D

<p>Historically, location algorithms have relied on simple, one-dimensional (1D, with depth) velocity models for fast, seismic event locations. The speed of these 1D models made them the preferred type of velocity model for operational needs, mainly due to computational requirements. Higher-dimensional (2D-3D) seismic velocity models are becoming more readily available from the scientific community and can provide significantly more accurate event locations over 1D models. The computational requirements of these higher-dimensional models tend to make their operational use prohibitive. The benefit of a 1D model is that it is generally used as travel-time lookup tables, one for each seismic phase, with travel-time predictions pre-calculated for event distance and depth. This simple, lookup structure makes the travel-time computation extremely fast.</p><p>Comparing location accuracy for 2D and 3D seismic velocity models tends to be problematic because each model is usually determined using different inversion parameters and ray-tracing algorithms. Attempting to use a different ray-tracing algorithm than used to develop a model almost always results in poor travel-time prediction compared to the algorithm used when developing the model.</p><p>We will demonstrate that using an open-source framework (GeoTess, www.sandia.gov/geotess) that can easily store 3D travel-time data can overcome the ray-tracing algorithm hurdle. Travel-time lookup tables (one for each station and phase) can be generated using the exact ray-tracing algorithm that is preferred for a specified model. The lookup surfaces are generally applied as corrections to a simple 1D model and also include variations in event depth, as opposed to legacy source-specific station corrections (SSSCs), as well as estimates of path-specific travel-time uncertainty. Having a common travel-time framework used for a location algorithm allows individual 2D and 3D velocity models to be compared in a fair, consistent manner.</p>

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.

Assessing uncertainties in velocity models and images with a fast nonlinear uncertainty quantification method

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. R63-R75 ◽  
Author(s):  
Gregory Ely ◽  
Alison Malcolm ◽  
Oleg V. Poliannikov
Keyword(s):  
Uncertainty Quantification ◽  
Seismic Imaging ◽  
Low Frequency ◽  
Expansion Method ◽  
Velocity Model ◽  
Image Features ◽  
Visible Image ◽  
Waveform Data ◽  
Velocity Models ◽  
Seismic Reflections

Seismic imaging is conventionally performed using noisy data and a presumably inexact velocity model. Uncertainties in the input parameters propagate directly into the final image and therefore into any quantity of interest, or qualitative interpretation, obtained from the image. We considered the problem of uncertainty quantification in velocity building and seismic imaging using Bayesian inference. Using a reduced velocity model, a fast field expansion method for simulating recorded wavefields, and the adaptive Metropolis-Hastings algorithm, we efficiently quantify velocity model uncertainty by generating multiple models consistent with low-frequency full-waveform data. A second application of Bayesian inversion to any seismic reflections present in the recorded data reconstructs the corresponding structures’ position along with its associated uncertainty. Our analysis complements rather than replaces traditional imaging because it allows us to assess the reliability of visible image features and to take that into account in subsequent interpretations.

Tomographic inversion by matrix transformation

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE35-VE38 ◽  
Author(s):  
Jonathan Liu ◽  
Lorie Bear ◽  
Jerry Krebs ◽  
Raffaella Montelli ◽  
Gopal Palacharla
Keyword(s):  
Linear System ◽  
Seismic Velocity ◽  
Synthetic Data ◽  
Velocity Model ◽  
Matrix Transformation ◽  
Uniform Grid ◽  
Complex Structures ◽  
Tomographic Inversion ◽  
Velocity Models ◽  
The Stability

We have developed a new method to build seismic velocity models for complex structures. In our approach, we use a spatially nonuniform parameterization of the velocity model in tomography and a uniform grid representation of the same velocity model in ray tracing to generate the linear system of tomographic equations. Subsequently, a matrix transformation is applied to the system of equations to produce a new linear system of tomographic equations using nonuniform parameterization. In this way, we improved the stability of tomographic inversion without adding computing costs. We tested the effectiveness of our process on a 3D synthetic data example.

scholarly journals Relocation of earthquakes in the southern and eastern Alps (Austria, Italy) recorded by the dense, temporary SWATH-D network using a Markov chain Monte Carlo inversion

Solid Earth ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 1087-1109
Author(s):  
Azam Jozi Najafabadi ◽  
Christian Haberland ◽  
Trond Ryberg ◽  
Vincent F. Verwater ◽  
Eline Le Breton ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Velocity Model ◽  
Eastern Alps ◽  
Inversion Method ◽  
Parameter Uncertainties ◽  
Waveform Data ◽  
Velocity Models ◽  
Austroalpine Nappes

Abstract. In this study, we analyzed a large seismological dataset from temporary and permanent networks in the southern and eastern Alps to establish high-precision hypocenters and 1-D VP and VP/VS models. The waveform data of a subset of local earthquakes with magnitudes in the range of 1–4.2 ML were recorded by the dense, temporary SWATH-D network and selected stations of the AlpArray network between September 2017 and the end of 2018. The first arrival times of P and S waves of earthquakes are determined by a semi-automatic procedure. We applied a Markov chain Monte Carlo inversion method to simultaneously calculate robust hypocenters, a 1-D velocity model, and station corrections without prior assumptions, such as initial velocity models or earthquake locations. A further advantage of this method is the derivation of the model parameter uncertainties and noise levels of the data. The precision estimates of the localization procedure is checked by inverting a synthetic travel time dataset from a complex 3-D velocity model and by using the real stations and earthquakes geometry. The location accuracy is further investigated by a quarry blast test. The average uncertainties of the locations of the earthquakes are below 500 m in their epicenter and ∼ 1.7 km in depth. The earthquake distribution reveals seismicity in the upper crust (0–20 km), which is characterized by pronounced clusters along the Alpine frontal thrust, e.g., the Friuli-Venetia (FV) region, the Giudicarie–Lessini (GL) and Schio-Vicenza domains, the Austroalpine nappes, and the Inntal area. Some seismicity also occurs along the Periadriatic Fault. The general pattern of seismicity reflects head-on convergence of the Adriatic indenter with the Alpine orogenic crust. The seismicity in the FV and GL regions is deeper than the modeled frontal thrusts, which we interpret as indication for southward propagation of the southern Alpine deformation front (blind thrusts).

Diffraction imaging and depth-velocity inversion with 3D P-Cable seismic data

2021 ◽  
Author(s):  
Alexander Bauer ◽  
Benjamin Schwarz ◽  
Dirk Gajewski
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Computational Cost ◽  
Velocity Model ◽  
Data Cube ◽  
Velocity Inversion ◽  
Velocity Models ◽  
North East ◽  
3D Velocity Model ◽  
Zero Offset

<p>Most established methods for the estimation of subsurface velocity models rely on the measurements of reflected or diving waves and therefore require data with sufficiently large source-receiver offsets. For seismic data that lacks these offsets, such as vintage data, low-fold academic data or near zero-offset P-Cable data, these methods fail. Building on recent studies, we apply a workflow that exploits the diffracted wavefield for depth-velocity-model building. This workflow consists of three principal steps: (1) revealing the diffracted wavefield by modeling and adaptively subtracting reflections from the raw data, (2) characterizing the diffractions with physically meaningful wavefront attributes, (3) estimating depth-velocity models with wavefront tomography. We propose a hybrid 2D/3D approach, in which we apply the well-established and automated 2D workflow to numerous inlines of a high-resolution 3D P-Cable dataset acquired near Ritter Island, a small volcanic island located north-east of New Guinea known for a catastrophic flank collapse in 1888. We use the obtained set of parallel 2D velocity models to interpolate a 3D velocity model for the whole data cube, thus overcoming possible issues such as varying data quality in inline and crossline direction and the high computational cost of 3D data analysis. Even though the 2D workflow may suffer from out-of-plane effects, we obtain a smooth 3D velocity model that is consistent with the data.</p>

Seismic Velocity Model Building Using Neural Networks: Training Data Design and Learning Generalization

Geophysics ◽  
2021 ◽  
pp. 1-73
Author(s):  
Hani Alzahrani ◽  
Jeffrey Shragge
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Training Data ◽  
Training Process ◽  
Training Models ◽  
Velocity Models ◽  
Velocity Model Building ◽  
Layer Velocity

Data-driven artificial neural networks (ANNs) offer a number of advantages over conventional deterministic methods in a wide range of geophysical problems. For seismic velocity model building, judiciously trained ANNs offer the possibility of estimating high-resolution subsurface velocity models. However, a significant challenge of ANNs is training generalization, which is the ability of an ANN to apply the learning from the training process to test data not previously encountered. In the context of velocity model building, this means learning the relationship between velocity models and the corresponding seismic data from a set of training data, and then using acquired seismic data to accurately estimate unknown velocity models. We ask the following question: what type of velocity model structures need be included in the training process so that the trained ANN can invert seismic data from a different (hypothetical) geological setting? To address this question, we create four sets of training models: geologically inspired and purely geometrical, both with and without background velocity gradients. We find that using geologically inspired training data produce models with well-delineated layer interfaces and fewer intra-layer velocity variations. The absence of a certain geological structure in training models, though, hinders the ANN's ability to recover it in the testing data. We use purely geometric training models consisting of square blocks of varying size to demonstrate the ability of ANNs to recover reasonable approximations of flat, dipping, and curved interfaces. However, the predicted models suffer from intra-layer velocity variations and non-physical artifacts. Overall, the results successfully demonstrate the use of ANNs in recovering accurate velocity model estimates, and highlight the possibility of using such an approach for the generalized seismic velocity inversion problem.

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