GeoTess: A Generalized Earth Model Software Utility

Ballard, Sanford; Hipp, James Richard; Kraus, B.; Encarnacao, Andre Villanova; Young, Christopher J.

doi:10.1785/0220150222

Cited by 14 publications

(8 citation statements)

References 7 publications

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“…K is the number of path segments for this path and P is the number of nodes for the model. In this study, I used the method of Ballard et al [], which is a linear barycentric‐interpolation method for triangles, to determine a pk for the three neighboring nodes of the k th path segment. For nodes that are not neighbors of the k th path segment, a pk = 0.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Compared with regular latitude‐longitude meshing, triangular tessellation provides more uniform spatial sampling of the Earth's surface, particularly near the poles. The software I used to construct the mesh is called GeoTess, which is a publicly available software utility developed by Ballard et al [] for Earth model construction, population, and interrogation. The sizes of mesh triangles are 1° on each side.…”

Section: Tomographic Inversionmentioning

confidence: 99%

“…I weighted the input data by the inverse of their measurement error estimated by Myers et al [2010] in W e . The damping matrix W m = L T L, where L is the regularization matrix, was implemented using the first-order (first derivative) Tikhonov regularization [Menke, 1989;Aster et al, 2013]. To select the weighting factor ε 2 , I performed a series of trial inversions with varying weighting factors and constructed a model-flatness-versus-prediction-error curve, or the so-called L curve, from these inversions.…”

Section: Tomographic Inversionmentioning

confidence: 99%

“…To select the weighting factor ε 2 , I performed a series of trial inversions with varying weighting factors and constructed a model-flatness-versus-prediction-error curve, or the so-called L curve, from these inversions. I then selected the weighting factor for the final inversion based on the location on the L curve where the curve has the largest curvature [Aster et al, 2013;Kline, 1998]. Figure 8 gives an example of the L curve from the trial inversions.…”

Section: Tomographic Inversionmentioning

confidence: 99%

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The lateral variation of P_n velocity gradient under Eurasia

Yang

2017

JGR Solid Earth

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Mantle lid P wave velocity gradient, or Pn velocity gradient, reflects the depth and lateral variations of thermal and rheological state of the uppermost mantle. Mapping the Pn velocity gradient and its lateral variation helps us gain insight into the temperature, composition, and dynamics of the uppermost mantle. In addition, because Pn velocity gradient has profound influence on Pn propagation behavior, an accurate mapping of Pn velocity gradient also improves the modeling and prediction of Pn travel times and amplitudes. In this study, I used measured Pn travel times to derive path‐specific Pn velocity gradients. I then inverted these velocity gradients for two‐dimensional (2‐D) Pn velocity‐gradient models for Eurasia based on the assumption that a path‐specific Pn velocity gradient is the mean of laterally varying Pn velocity gradients along the Pn path. Result from a Monte Carlo simulation indicates that the assumption is appropriate. The 2‐D velocity‐gradient models show that most of Eurasia has positive velocity gradients. High velocity gradients exist mainly in tectonically active regions. Most tectonically stable regions show low and more uniform velocity gradients. Strong velocity‐gradient variations occur largely along convergent plate boundaries, particularly under overriding plates.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Tomographic Inversionmentioning

confidence: 99%

Section: Tomographic Inversionmentioning

confidence: 99%

Section: Tomographic Inversionmentioning

confidence: 99%

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The lateral variation of P_n velocity gradient under Eurasia

Yang

2017

JGR Solid Earth

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“…For instance, the Oak Ridge National Laboratory develops, maintains, tests and manages the SCALE [1] Code System that is a widely-used modeling and simulation suite for nuclear safety analysis and design. As a second example, the Sandia National Laboratory developed the Geotess [2], a model parameterization and software support system that implements the construction, population, storage and interrogations of data stored in 3D Earth models. Although transposition is a very simple problem, its basic implementation may present a very low data cache hit ratio for large matrices [3].…”

Section: Introductionmentioning

confidence: 99%

Ideal and Predictable Hit Ratio for Matrix Transposition in Data Caches

et al. 2020

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Matrix transposition is a fundamental operation, but it may present a very low and hardly predictable data cache hit ratio for large matrices. Safe (worst-case) hit ratio predictability is required in real-time systems. In this paper, we obtain the relations among the cache parameters that guarantee the ideal (predictable) data hit ratio assuming a Least-Recently-Used (LRU) data cache. Considering our analytical assessments, we compare a tiling matrix transposition to a cache oblivious algorithm, modified with phantom padding to improve its data hit ratio. Our results show that, with an adequate tile size, the tiling version results in an equal or better data hit ratio. We also analyze the energy consumption and execution time of matrix transposition on real hardware with pseudo-LRU (PLRU) caches. Our analytical hit/miss assessment enables the usage of a data cache for matrix transposition in real-time systems, since the number of misses in the worst case is bound. In general and high-performance computation, our analysis enables us to restrict the cache resources devoted to matrix transposition with no negative impact, in order to reduce both the energy consumption and the pollution to other computations.

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