scholarly journals Assessment of Tomography Models of Taiwan Using First-Arrival Times from the TAIGER Active-Source Experiment

2017 ◽  
pp. 5-34
Author(s):  
Yu-Pin Lin
Keyword(s):  
Arrival Times ◽  
Active Source ◽  
First Arrival

Three-dimensional crustal Vp and Vs structures beneath the southern segment of the Tan-Lu fault revealed by active source and earthquake data

2020 ◽  
Vol 223 (3) ◽  
pp. 2148-2165
Author(s):  
Yunpeng Zhang ◽  
Baoshan Wang ◽  
Tao Xu ◽  
Wei Yang ◽  
Weitao Wang ◽  
...  
Keyword(s):  
Crustal Structure ◽  
Yangtze River ◽  
Velocity Structure ◽  
Eastern China ◽  
P Wave ◽  
Arrival Times ◽  
Local Earthquakes ◽  
Active Source ◽  
First Arrival ◽  
Earthquake Data

SUMMARY The 2400-km-long Tan-Lu fault, the largest deformation zone in eastern China, plays a decisive role in the seismicity, regional tectonics and mineral deposits distributions. However, the velocity structure beneath the Tan-Lu fault, particularly in the southern segment, is poorly imaged due to the lack of local earthquakes. To obtain a high-resolution crustal structure image, we carried out an active source experiment by firing mobile airgun sources along the Yangtze River in the Anhui Province in October 2015. We manually picked 4118 P wave and 1906 Swave first arrival times from the airgun signals. We also collected 28 957 P wave and 26 257 S wave first arrival times from local earthquakes in a larger area. 3-D crustal velocity images beneath the southern segment of the Tan-Lu fault and surrounding areas are studied using traveltime tomography. Compared with the local earthquake data, the active source data provide better constraints on the upper crustal structure, which further refines the resolution of the lower-crust structure. The Vp and Vs crustal structures are consistent with the local geological settings, and earthquakes are primarily clustered near faults and are spatially correlated with low-velocity zones. Strong velocity contrasts are observed across the Tan-Lu fault zone, which is the main factor controlling local anomalies. The high Vp, Vs and Vp/Vs beneath the Qinling-Dabie orogenic belt and the Middle-Lower Yangtze River Metallogenic Belt may relate to Mesozoic lithospheric delamination and asthenospheric upwelling. These results also demonstrate that the mobile large-volume airgun sources are promising tools for 3-D crustal structure surveys.

scholarly journals Sensor-based localization of epidemic sources on human mobility networks

2021 ◽  
Vol 17 (1) ◽  
pp. e1008545
Author(s):  
Jun Li ◽  
Juliane Manitz ◽  
Enrico Bertuzzo ◽  
Eric D. Kolaczyk
Keyword(s):  
Mixture Model ◽  
Human Mobility ◽  
Gaussian Mixture ◽  
Local Conditions ◽  
Arrival Times ◽  
Modes Of Transmission ◽  
Kwazulu Natal ◽  
First Arrival ◽  
Control Of Epidemics ◽  
The Impact

We investigate the source detection problem in epidemiology, which is one of the most important issues for control of epidemics. Mathematically, we reformulate the problem as one of identifying the relevant component in a multivariate Gaussian mixture model. Focusing on the study of cholera and diseases with similar modes of transmission, we calibrate the parameters of our mixture model using human mobility networks within a stochastic, spatially explicit epidemiological model for waterborne disease. Furthermore, we adopt a Bayesian perspective, so that prior information on source location can be incorporated (e.g., reflecting the impact of local conditions). Posterior-based inference is performed, which permits estimates in the form of either individual locations or regions. Importantly, our estimator only requires first-arrival times of the epidemic by putative observers, typically located only at a small proportion of nodes. The proposed method is demonstrated within the context of the 2000-2002 cholera outbreak in the KwaZulu-Natal province of South Africa.

scholarly journals Modeling across-trial variability in the Wald drift rate parameter

2020 ◽  
Author(s):  
Helen Steingroever ◽  
Dominik Wabersich ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Drift Rate ◽  
Model Parameters ◽  
Analysis Tool ◽  
Rate Parameter ◽  
Data Set ◽  
Arrival Times ◽  
First Arrival ◽  
Truncated Normal ◽  
Experimental Trials ◽  
Variance Parameter

Abstract The shifted-Wald model is a popular analysis tool for one-choice reaction-time tasks. In its simplest version, the shifted-Wald model assumes a constant trial-independent drift rate parameter. However, the presence of endogenous processes—fluctuation in attention and motivation, fatigue and boredom—suggest that drift rate might vary across experimental trials. Here we show how across-trial variability in drift rate can be accounted for by assuming a trial-specific drift rate parameter that is governed by a positive-valued distribution. We consider two candidate distributions: the truncated normal distribution and the gamma distribution. For the resulting distributions of first-arrival times, we derive analytical and sampling-based solutions, and implement the models in a Bayesian framework. Recovery studies and an application to a data set comprised of 1469 participants suggest that (1) both mixture distributions yield similar results; (2) all model parameters can be recovered accurately except for the drift variance parameter; (3) despite poor recovery, the presence of the drift variance parameter facilitates accurate recovery of the remaining parameters; (4) shift, threshold, and drift mean parameters are correlated.

AN ANALYTICAL METHOD OF MAKING WEATHERING CORRECTIONS

Geophysics ◽  
1942 ◽  
Vol 7 (4) ◽  
pp. 393-399
Author(s):  
M. B. Dobrin
Keyword(s):  
Least Squares ◽  
Analytical Method ◽  
Arrival Times ◽  
Mechanical Procedure ◽  
First Arrival ◽  
Actual Field ◽  
Time Distance

A method of weathering is described by which intercept times can be rapidly and accurately computed from first arrival times without the plotting of time‐distance curves. The velocities are determined by a mechanical procedure, based on least squares theory, which normally requires no exercise of judgment on the part of the computer. The application of the method to actual field set‐ups is illustrated by sample calculations.

The diminishing residual matrices method for surface-consistent statics — A review

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. V257-V274
Author(s):  
Necati Gülünay
Keyword(s):  
Synthetic Data ◽  
Reconstruction Technique ◽  
Data Set ◽  
Arrival Times ◽  
Long Wavelength ◽  
Iterative Reconstruction Technique ◽  
Land Survey ◽  
First Arrival ◽  
Waveform Distortions ◽  
The Individual

The diminishing residual matrices (DRM) method can be used to surface-consistently decompose individual trace statics into source and receiver components. The statics to be decomposed may either be first-arrival times after the application of linear moveout associated with a consistent refractor as used in refraction statics or residual statics obtained by crosscorrelating individual traces with corresponding model traces (known as pilot traces) at the same common-midpoint (CMP) location. The DRM method is an iterative process like the well-known Gauss-Seidel (GS) method, but it uses only source and receiver terms. The DRM method differs from the GS method in that half of the average common shot and receiver terms are subtracted simultaneously from the observations at each iteration. DRM makes the under-constrained statics problem a constrained one by implicitly adding a new constraint, the equality of the contribution of shots and receivers to the solution. The average of the shot statics and the average of the receiver statics are equal in the DRM solution. The solution has the smallest difference between shot and receiver statics profiles when the number of shots and the number of receivers in the data are equal. In this case, it is also the smallest norm solution. The DRM method can be derived from the well-known simultaneous iterative reconstruction technique. Simple numerical tests as well as results obtained with a synthetic data set containing only the field statics verify that the DRM solution is the same as the linear inverse theory solution. Both algorithms can solve for the long-wavelength component of the statics if the individual picks contain them. Yet DRM method is much faster. Application of the method to the normal moveout-corrected CMP gathers on a 3D land survey for residual statics calculation found that pick-decompose-apply-stack stages of the DRM method need to be iterated. These iterations are needed because of time and waveform distortions of the pilot traces due to the individual trace statics. The distortions lessen at every external DRM iteration.

scholarly journals Machine Learning Enabled Traveltime Inversion Based on the Horizontal Source Location Perturbation

Geophysics ◽  
2021 ◽  
pp. 1-40
Author(s):  
Isa Eren Yildirim ◽  
Tariq Alkhalifah ◽  
Ertugrul Umut Yildirim
Keyword(s):  
Linear Optimization ◽  
Deep Convolutional Neural Networks ◽  
Lateral Velocity ◽  
Suggested Approach ◽  
Near Surface ◽  
Arrival Times ◽  
Velocity Models ◽  
Uniqueness Of The Solution ◽  
First Arrival ◽  
High Level

Gradient based traveltime tomography, which aims to minimize the difference between modeled and observed first arrival times, is a highly non-linear optimization problem. Stabilization of this inverse problem often requires employing regularization. While regularization helps avoid local minima solutions, it might cause low resolution tomograms because of its inherent smoothing property. On the other hand, although conventional ray-based tomography can be robust in terms of the uniqueness of the solution, it suffers from the limitations inherent in ray tracing, which limits its use in complex media. To mitigate the aforementioned drawbacks of gradient and ray-based tomography, we approach the problem in a completely novel way leveraging data-driven inversion techniques based on training deep convolutional neural networks (DCNN). Since DCNN often face challenges in detecting high level features from the relatively smooth traveltime data, we use this type of network to map horizontal changes in observed first arrival traveltimes caused by a source shift to lateral velocity variations. The relationship between them is explained by a linearized eikonal equation. Construction of the velocity models from this predicted lateral variation requires information from, for example, a vertical well-log in the area. This vertical profile is then used to build a tomogram from the output of the network. Both synthetic and field data results verify that the suggested approach estimates the velocity models reliably. Because of the limited depth penetration of first arrival traveltimes, the method is particularly favorable for near-surface applications.

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