Improving Gather Picking for Tomography in Complex Velocity Models, a Case Study on Shale Diapirs

Author(s):  
C.E. Jones ◽  
V. Valler ◽  
S. Dean ◽  
H. Sherazi-Selby ◽  
L. Bystoel
2021 ◽  
Author(s):  
Jonathan Smith ◽  
Zachary Ross ◽  
Kamyar Azizzadenesheli ◽  
Jack Muir

<p>High resolution earthquake hypocentral locations are of critical importance for understanding the regional context driving seismicity. We introduce a scheme to reliably approximate a hypocenter posterior in a continuous domain that relies on recent advances in deep learning.</p><p>Our method relies on a differentiable forward model in the form of a deep neural network, which is trained to solve the Eikonal equation (EikoNet). EikoNet can rapidly determine the travel-time between any source-receiver pair for a non-gridded solution. We demonstrate the robustness of these travel-time solutions are for a series of complex velocity models.</p><p>For the inverse problem, we utilize Stein Variational Inference, which is a recent approximate inference procedure that iteratively updates a configuration of particles to approximate a target posterior by minimizing the so-called Stein discrepancy. The gradients of this objective function can be rapidly calculated due to the differentiability of the EikoNet. The particle locations are updated until convergence, after which we utilize clustering techniques and kernel density methods to determine the optimal hypocenter and its uncertainty.</p><p>The inversion procedure outlined in this work is validated using a series of synthetic tests to determine the parameter optimisation and the validity for large observational datasets, which can locate earthquakes in 439s per event for 2039 observations. In addition, we apply this technique to a case study of seismicity in the Southern California region for earthquakes from 2019.</p>


Author(s):  
D. Pandolfi ◽  
E. Rebel-Schisselé ◽  
E. Auger ◽  
T. Bardainne

2016 ◽  
Vol 121 (11) ◽  
pp. 8113-8135 ◽  
Author(s):  
D. Latorre ◽  
F. Mirabella ◽  
L. Chiaraluce ◽  
F. Trippetta ◽  
A. Lomax

Geophysics ◽  
2006 ◽  
Vol 71 (1) ◽  
pp. U1-U11 ◽  
Author(s):  
Andreas Rüger ◽  
Dave Hale

In seismic processing, velocity fields are commonly represented on finely sampled Cartesian grids. Attractive alternatives are unstructured grids such as meshes composed of triangles or tetrahedra. Meshes provide a space-filling framework that enables editing of velocity models while facilitating numerical tasks such as seismic modeling and inversion. In this paper, we introduce an automated process to generate meshes of subsurface velocity structures for highly resolved velocity fields without providing additional external constraints such as horizons and faults. Our analysis shows that these new meshes can represent both smooth and discontinuous velocity profiles accurately and with less computer memory than grids.


2019 ◽  
Vol 38 (3) ◽  
pp. 226-231 ◽  
Author(s):  
Andreas Wuestefeld ◽  
Matt Wilks

The success of a distributed acoustic sensing (DAS) survey depends on strain energy impeding at favorable angles at most sections of the fiber. Although constrained to the path of the wellbore, there are various design parameters that can influence the recorded DAS amplitude. We present here a method to model the performance of DAS installations. We use precise raypath modeling in complex velocity models to determine ray incidence angles and show variations between different wrapping angles and detection thresholds. We then propose a way to evaluate the performance of the DAS acquisition design, and how to optimize processing, based on the percentage of DAS channels above a chosen amplitude threshold. For microseismic studies, the best wrapping angle of the fiber can be determined, which may be defined as covering the target area most homogeneously. For vertical seismic profiling projects, surface shot positions can be evaluated for their predicted recorded energy.


First Break ◽  
2008 ◽  
Vol 26 (1119) ◽  
Author(s):  
P. Whitfield ◽  
M. Dazley ◽  
B. Santos-Luis ◽  
F. Nieuwland ◽  
L. Lemaistre

2010 ◽  
Author(s):  
Anjaneyulu Singavarapu ◽  
Abdul malik Salah abdulla ◽  
Manowar A.M. Ahmed ◽  
Sanjeev S. Thakur ◽  
Hanan Al-Owihan ◽  
...  

1990 ◽  
Vol 80 (5) ◽  
pp. 1284-1296
Author(s):  
Claude F. Lafond ◽  
Alan R. Levander

Abstract We have developed a fast and accurate dynamic raytracing method for 2.5-D heterogeneous media based on the kinematic algorithm proposed by Langan et al. (1985). This algorithm divides the model into cells of constant slowness gradient, and the positions, directions, and travel times of the rays are expressed as polynomials of the travel path length, accurate to the second other in the gradient. This method is efficient because of the use of simple polynomials at each raytracing step. We derived similar polynomial expressions for the dynamic raytracing quantities by integrating the raytracing system and expanding the solutions to the second order in the gradient. This new algorithm efficiently computes the geometrical spreading, amplitude, and wavefront curvature on individual rays. The two-point raytracing problem is solved by the shooting method using the geometrical spreading. Paraxial corrections based on the wavefront curvature improve the accuracy of the travel time and amplitude at a given receiver. The computational results for two simple velocity models are compared with those obtained with the SEIS83 seismic modeling package (Cerveny and Psencik, 1984); this new method is accurate for both travel times and amplitudes while being significantly faster. We present a complex velocity model that shows that the algorithm allows for realistic models and easily computes rays in structures that pose difficulties for conventional methods. The method can be extended to raytracing in 3-D heterogeneous media and can be used as a support for a Gaussian beam algorithm. It is also suitable for computing the Green's function and imaging condition needed for prestack depth migration.


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