Academic literature on the topic 'Sesimic source parameter retrieval'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Sesimic source parameter retrieval.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Sesimic source parameter retrieval"
1
Meiser, Thorsten. "Analyzing Stochastic Dependence of Cognitive Processes in Multidimensional Source Recognition." Experimental Psychology 61, no. 5 (May 15, 2014): 402–15. http://dx.doi.org/10.1027/1618-3169/a000261.
Full textAbstract:
Stochastic dependence among cognitive processes can be modeled in different ways, and the family of multinomial processing tree models provides a flexible framework for analyzing stochastic dependence among discrete cognitive states. This article presents a multinomial model of multidimensional source recognition that specifies stochastic dependence by a parameter for the joint retrieval of multiple source attributes together with parameters for stochastically independent retrieval. The new model is equivalent to a previous multinomial model of multidimensional source memory for a subset of the parameter space. An empirical application illustrates the advantages of the new multinomial model of joint source recognition. The new model allows for a direct comparison of joint source retrieval across conditions, it avoids statistical problems due to inflated confidence intervals and does not imply a conceptual imbalance between source dimensions. Model selection criteria that take model complexity into account corroborate the new model of joint source recognition.
2
Allen, Douglas R., Karl W. Hoppel, Gerald E. Nedoluha, Stephen D. Eckermann, and Cory A. Barton. "Ensemble-Based Gravity Wave Parameter Retrieval for Numerical Weather Prediction." Journal of the Atmospheric Sciences 79, no. 3 (March 2022): 621–48. http://dx.doi.org/10.1175/jas-d-21-0191.1.
Full textAbstract:
Abstract Gravity wave (GW) momentum and energy deposition are large components of the momentum and heat budgets of the stratosphere and mesosphere, affecting predictability across scales. Since weather and climate models cannot resolve the entire GW spectrum, GW parameterizations are required. Tuning these parameterizations is time-consuming and must be repeated whenever model configurations are changed. We introduce a self-tuning approach, called GW parameter retrieval (GWPR), applied when the model is coupled to a data assimilation (DA) system. A key component of GWPR is a linearized model of the sensitivity of model wind and temperature to the GW parameters, which is calculated using an ensemble of nonlinear forecasts with perturbed parameters. GWPR calculates optimal parameters using an adaptive grid search that reduces DA analysis increments via a cost-function minimization. We test GWPR within the Navy Global Environmental Model (NAVGEM) using three latitude-dependent GW parameters: peak momentum flux, phase-speed width of the Gaussian source spectrum, and phase-speed weighting relative to the source-level wind. Compared to a baseline experiment with fixed parameters, GWPR reduces analysis increments and improves 5-day mesospheric forecasts. Relative to the baseline, retrieved parameters reveal enhanced source-level fluxes and westward shift of the wave spectrum in the winter extratropics, which we relate to seasonal variations in frontogenesis. The GWPR reduces stratospheric increments near 60°S during austral winter, compensating for excessive baseline nonorographic GW drag. Tropical sensitivity is weaker due to significant absorption of GW in the stratosphere, resulting in less confidence in tropical GWPR values.
3
Jin, Sheng, Xiaojian Ding, Su Wang, Yao Dong, and Jianghui Ji. "Nii: a Bayesian orbit retrieval code applied to differential astrometry." Monthly Notices of the Royal Astronomical Society 509, no. 3 (November 17, 2021): 4608–19. http://dx.doi.org/10.1093/mnras/stab3317.
Full textAbstract:
ABSTRACT Here, we present an open source python-based Bayesian orbit retrieval code (Nii) that implements an automatic parallel tempering Markov chain Monte Carlo (APT-MCMC) strategy. Nii provides a module to simulate the observations of a space-based astrometry mission in the search for exoplanets, a signal extraction process for differential astrometric measurements using multiple reference stars, and an orbital parameter retrieval framework using APT-MCMC. We further verify the orbit retrieval ability of the code through two examples corresponding to a single-planet system and a dual-planet system. In both cases, efficient convergence on the posterior probability distribution can be achieved. Although this code specifically focuses on the orbital parameter retrieval problem of differential astrometry, Nii can also be widely used in other Bayesian analysis applications.
4
Bressler, I., B. R. Pauw, and A. F. Thünemann. "McSAS: software for the retrieval of model parameter distributions from scattering patterns." Journal of Applied Crystallography 48, no. 3 (May 22, 2015): 962–69. http://dx.doi.org/10.1107/s1600576715007347.
Full textAbstract:
A user-friendly open-source Monte Carlo regression package (McSAS) is presented, which structures the analysis of small-angle scattering (SAS) using uncorrelated shape-similar particles (or scattering contributions). The underdetermined problem is solvable, provided that sufficient external information is available. Based on this, the user picks a scatterer contribution model (or `shape') from a comprehensive library and defines variation intervals of its model parameters. A multitude of scattering contribution models are included, including prolate and oblate nanoparticles, core–shell objects, several polymer models, and a model for densely packed spheres. Most importantly, the form-free Monte Carlo nature ofMcSASmeans it is not necessary to provide further restrictions on the mathematical form of the parameter distribution; without prior knowledge,McSASis able to extract complex multimodal or odd-shaped parameter distributions from SAS data. When provided with data on an absolute scale with reasonable uncertainty estimates, the software outputs model parameter distributions in absolute volume fraction, and provides the modes of the distribution (e.g.mean, varianceetc.). In addition to facilitating the evaluation of (series of) SAS curves,McSASalso helps in assessing the significance of the results through the addition of uncertainty estimates to the result. TheMcSASsoftware can be integrated as part of an automated reduction and analysis procedure in laboratory instruments or at synchrotron beamlines.
5
Zhang, Yun, Xu Chen, Wanting Meng, Jiwei Yin, Yanling Han, Zhonghua Hong, and Shuhu Yang. "Wind Direction Retrieval Using Support Vector Machine from CYGNSS Sea Surface Data." Remote Sensing 13, no. 21 (November 5, 2021): 4451. http://dx.doi.org/10.3390/rs13214451.
Full textAbstract:
In view of the difficulty of wind direction retrieval in the case of the large space and time span of the global sea surface, a method of sea surface wind direction retrieval using a support vector machine (SVM) is proposed. This paper uses the space-borne global navigation satellite systems reflected signal (GNSS-R) as the remote sensing signal source. Using the Cyclone Global Navigation Satellite System (CYGNSS) satellite data, this paper selects a variety of feature parameters according to the correlation between the features of the sea surface reflection signal and the wind direction, including the Delay Doppler Map (DDM), corresponding to the CYGNSS satellite parameters and geometric feature parameters. The Radial Basis Function (RBF) is selected, and parameter optimization is performed through cross-validation based on the grid search method. Finally, the SVM model of sea surface wind direction retrieval is established. The result shows that this method has a high retrieval classification accuracy using the dataset with wind speed greater than 10 m/s, and the root mean square error (RMSE) of the retrieval result is 26.70°.
6
Mercieca, Thomas, and Joseph G. Vella. "Multi-Dimensional Indexes in DBMSs." Journal of Cases on Information Technology 21, no. 3 (July 2019): 40–50. http://dx.doi.org/10.4018/jcit.2019070103.
Full textAbstract:
Multi-dimensional data is present across multimedia, data mining and other data-driven applications. The R-Tree is a popular index structure that DBMSs are implementing as core for efficient retrieval of such data. The gap between the best and worst-case performance is very wide in an R-tree. Thus, building quality R-trees quickly is desirable. Variations differ in how node overflow are approached during the building process. This article studies the R-Tree technique that the open-source PostgreSQL DBMS uses. Focus is on a specific parameter controlling node overflows as an optimisation target, and improved configurations are proposed. This parameter is hard-wired into the DBMS, and therefore, an implementation is presented to allow this parameter to become accessible through an SQL construct. The access method designer can resort to configuring this parameter when trying to meet specific storage or time-related performance targets. With this study, the reader can gain an insight into the effects of changing the parameter by considering the spatial indexes on well-known workloads.
7
Muckenhuber, Stefan, Anton Andreevich Korosov, and Stein Sandven. "Open-source feature-tracking algorithm for sea ice drift retrieval from Sentinel-1 SAR imagery." Cryosphere 10, no. 2 (April 26, 2016): 913–25. http://dx.doi.org/10.5194/tc-10-913-2016.
Full textAbstract:
Abstract. A computationally efficient, open-source feature-tracking algorithm, called ORB, is adopted and tuned for sea ice drift retrieval from Sentinel-1 SAR (Synthetic Aperture Radar) images. The most suitable setting and parameter values have been found using four Sentinel-1 image pairs representative of sea ice conditions between Greenland and Severnaya Zemlya during winter and spring. The performance of the algorithm is compared to two other feature-tracking algorithms, namely SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features). Having been applied to 43 test image pairs acquired over Fram Strait and the north-east of Greenland, the tuned ORB (Oriented FAST and Rotated BRIEF) algorithm produces the highest number of vectors (177 513, SIFT: 43 260 and SURF: 25 113), while being computationally most efficient (66 s, SIFT: 182 s and SURF: 99 s per image pair using a 2.7 GHz processor with 8 GB memory). For validation purposes, 314 manually drawn vectors have been compared with the closest calculated vectors, and the resulting root mean square error of ice drift is 563 m. All test image pairs show a significantly better performance of the HV (horizontal transmit, vertical receive) channel due to higher informativeness. On average, around four times as many vectors have been found using HV polarization. All software requirements necessary for applying the presented feature-tracking algorithm are open source to ensure a free and easy implementation.
8
Blecic, Jasmina, Joseph Harrington, Patricio E. Cubillos, M. Oliver Bowman, Patricio M. Rojo, Madison Stemm, Ryan C. Challener, et al. "An Open-source Bayesian Atmospheric Radiative Transfer (BART) Code. III. Initialization, Atmospheric Profile Generator, Post-processing Routines." Planetary Science Journal 3, no. 4 (April 1, 2022): 82. http://dx.doi.org/10.3847/psj/ac3515.
Full textAbstract:
Abstract This and companion papers by Harrington et al. and Cubillos et al. describe an open-source retrieval framework, Bayesian Atmospheric Radiative Transfer (BART), available to the community under the reproducible-research license via https://github.com/exosports/BART. BART is a radiative transfer code (transit; https://github.com/exosports/transit; Rojo et al.), initialized by the Thermochemical Equilibrium Abundances (TEA; https://github.com/dzesmin/TEA) code (Blecic et al.), and driven through the parameter phase space by a differential-evolution Markov Chain Monte Carlo (MC3; https://github.com/pcubillos/mc3) sampler (Cubillos et al.). In this paper we give a brief description of the framework and its modules that can be used separately for other scientific purposes; outline the retrieval analysis flow; present the initialization routines, describing in detail the atmospheric profile generator and the temperature and species parameterizations; and specify the post-processing routines and outputs, concentrating on the spectrum band integrator, the best-fit model selection, and the contribution functions. We also present an atmospheric analysis of WASP-43b secondary eclipse data obtained from space- and ground-based observations. We compare our results with the results from the literature and investigate how the inclusion of additional opacity sources influences the best-fit model.
9
Nikonovas, T., P. R. J. North, and S. H. Doerr. "Smoke aerosol properties and ageing effects for Northern temperate and boreal regions derived from AERONET source and age attribution." Atmospheric Chemistry and Physics Discussions 15, no. 5 (March 5, 2015): 6445–79. http://dx.doi.org/10.5194/acpd-15-6445-2015.
Full textAbstract:
Abstract. Particulate emissions from wildfires impact human health and have a large but uncertain effect on climate. Modelling schemes depend on information about emission factors, emitted particle microphysical and optical properties and ageing effects, while satellite retrieval algorithms make use of characteristic aerosol models to improve retrieval. Ground based remote sensing provides detailed aerosol characterisation, but does not contain information on source. Here, a method is presented to estimate plume origin land cover type and age for AERONET aerosol observations, employing trajectory modelling using the HYSPLIT model, and satellite active fire and aerosol optical thickness (AOT) observations from MODIS and AATSR. It is applied to AERONET stations located in or near Northern temperate and boreal forests, for the period 2002–2013. The results from 629 fire attributions indicate significant differences in size distributions and particle optical properties between different land cover types. Smallest fine mode median radius are attributed to plumes from cropland – natural vegetation mosaic (0.143 μm) and grasslands (0.147 μm) fires. Evergreen needleleaf forest emissions show a significantly smaller fine mode median radius (0.164 μm) than plumes from woody savannas (0.184 μm) and mixed forest (0.193 μm) fires. Smoke plumes are predominantly scattering for all of the classes with median single scattering albedo at 440 nm (SSA(440)) values close to 0.95 except the cropland emissions which have a SSA(440) value of 0.9. Overall fine mode volume median radius increase rate is 0.0095 μm per day for the first 4 days of ageing and 0.0084 μm per day for seven days of ageing. Changes in size were consistent with a decrease in Angstrom Exponent and increase in Asymmetry parameter. No significant changes in SSA(λ) with ageing were found. These estimates have implications for improved modelling of aerosol radiative effects, relevant to both climate modelling and satellite aerosol retrieval schemes.
10
Knobelspiesse, Kirk, Amir Ibrahim, Bryan Franz, Sean Bailey, Robert Levy, Ziauddin Ahmad, Joel Gales, et al. "Analysis of simultaneous aerosol and ocean glint retrieval using multi-angle observations." Atmospheric Measurement Techniques 14, no. 5 (May 3, 2021): 3233–52. http://dx.doi.org/10.5194/amt-14-3233-2021.
Full textAbstract:
Abstract. Since early 2000, NASA's Multi-angle Imaging SpectroRadiometer (MISR) instrument has been performing remote sensing retrievals of aerosol optical properties from the polar-orbiting Terra spacecraft. A noteworthy aspect of MISR observations over the ocean is that, for much of the Earth, some of the multi-angle views have contributions from solar reflection by the ocean surface (glint, or glitter), while others do not. Aerosol retrieval algorithms often discard these glint-influenced observations because they can overwhelm the signal and are difficult to predict without knowledge of the (wind-speed-driven) ocean surface roughness. However, theoretical studies have shown that multi-angle observations of a location at geometries with and without reflected sun glint can be a rich source of information, sufficient to support simultaneous retrieval of both the aerosol state and the wind speed at the ocean surface. We are in the early stages of creating such an algorithm. In this paper, we describe our assessment of the appropriate level of parameterization for simultaneous aerosol and ocean surface property retrievals using sun glint. For this purpose, we use generalized nonlinear retrieval analysis (GENRA), an information content assessment (ICA) technique employing Bayesian inference, and simulations from the Ahmad–Fraser iterative radiative transfer code. We find that four parameters are suitable: aerosol optical depth (τ), particle size distribution (expressed as the fine mode fraction f of small particles in a bimodal size distribution), surface wind speed (w), and relative humidity (r, to define the aerosol water content and complex refractive index). None of these parameters define ocean optical properties, as we found that the aerosol state could be retrieved with the nine MISR near-infrared views alone, where the ocean body is strongly absorbing in the open ocean. We also found that retrieval capability varies with observation geometry and that as τ increases so does the ability to determine aerosol intensive optical properties (r and f, while it decreases for w). Increases in w decrease the ability to determine the true value of that parameter but have minimal impact on retrieval of aerosol properties. We explored the benefit of excluding the two most extreme MISR view angles for which radiative transfer with the plane-parallel approximation is less certain, but we found no advantage in doing so. Finally, the impact of treating wind speed as a scalar parameter, rather than as a two-parameter directional wind, was tested. While the simpler scalar model does contribute to overall aerosol uncertainty, it is not sufficiently large to justify the addition of another dimension to parameter space. An algorithm designed upon these principles is in development. It will be used to perform an atmospheric correction with MISR for coincident ocean color (OC) observations by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, also on the NASA Terra spacecraft. Unlike MISR, MODIS is a single-view-angle instrument, but it has a more complete set of spectral channels ideal for determination of optical ocean properties. The atmospheric correction of MODIS OC data can therefore benefit from MISR aerosol retrievals. Furthermore, higher-spatial-resolution data from coincident MISR observations may also improve glint screening.
Dissertations / Theses on the topic "Sesimic source parameter retrieval"
1
Artchounin, Daniel. "Tuning of machine learning algorithms for automatic bug assignment." Thesis, Linköpings universitet, Programvara och system, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139230.
Full textAbstract:
In software development projects, bug triage consists mainly of assigning bug reports to software developers or teams (depending on the project). The partial or total automation of this task would have a positive economic impact on many software projects. This thesis introduces a systematic four-step method to find some of the best configurations of several machine learning algorithms intending to solve the automatic bug assignment problem. These four steps are respectively used to select a combination of pre-processing techniques, a bug report representation, a potential feature selection technique and to tune several classifiers. The aforementioned method has been applied on three software projects: 66 066 bug reports of a proprietary project, 24 450 bug reports of Eclipse JDT and 30 358 bug reports of Mozilla Firefox. 619 configurations have been applied and compared on each of these three projects. In production, using the approach introduced in this work on the bug reports of the proprietary project would have increased the accuracy by up to 16.64 percentage points.
2
Stramondo, S. "Seismic Source Quantitative Parameters Retrieval from InSAR Data and Neural Networks." Thesis, 2007. http://hdl.handle.net/2122/3043.
Full textAbstract:
The basic idea of this thesis is to exploit the capabilities of neural networks in a very new
framework: the quantitative modelling of the seismic source and the interferogram inversion for
retrieving its geometric parameters. The problem can be sum up as follows. When a moderateto-
strong earthquake occurs we can apply SAR Interferometry (InSAR) technique to compute a
differential interferogram. The latter is used to detect and measure the surface displacement
field. The earthquake has been generated by an active, seismogenic, fault having its own specific
geometry. Therefore each differential interferogram contains the information concerning the
geometry of the seismic source the earthquake comes from; its shape and size, the number of
fringes, the lobe orientation and number are the main features of the surface effects field. Two
problems have been analysed in this work. The first is the identification of the seismic source
mechanism. The second is a typical inversion exercise concerning the fault plane parameter. To
perform both exercises of the seismic fault a huge number of synthetic interferograms has been
computed. Each of them is generated by a different combination of such geometric parameters.
As far as the retrieval of the geometric parameters is concerned an artificial neural network has
been properly generated and trained to provide an inversion procedure to single out the
geometric parameters of the fault. Five among these latter, Length, Width, Dip, Strike, Depth,
have been simultaneously inverted. The result is in agreement with those results based on
different approaches. Furthermore the method seems very promising and leads to improve the
studies concerning the combined use of neural networks and InSAR technique.Tor Vergata University
Unpublished
open
Book chapters on the topic "Sesimic source parameter retrieval"
1
Khalid, Nafisah, Noraain Mohamed Saraf, Juazer Rizal Abdul Hamid, and Zulkiflee Abd. Latif. "Tree Biophysical Parameter Retrieval from Multi-source Remote Sensing Data Fusion." In Concepts and Applications of Remote Sensing in Forestry, 435–52. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4200-6_22.
Full textConference papers on the topic "Sesimic source parameter retrieval"
1
Zhong, Xin, Yoke San Wong, Wen Feng Lu, Kelvin W. C. Foong, and Alan Ho-lun Cheng. "A Dental Matching Approach Using Partial Surface Features for Human Identification." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70890.
Full textAbstract:
A novel 3D dental identification framework is presented. The objective is to develop a methodology to enable computer-automated matching of complex dental surfaces with possible missing regions for human identification. Thus far, there is no reported attempt at 3D dental identification given partially available dental casts or impressions. This approach overcomes a number of key hurdles in traditional 2D methods. Given the 3D digital form of a dental cast surface, the developed method will facilitate the search for the closest match in the database of digitized dental casts. A salient curvature matching algorithm (SCM) is proposed for pose estimation which includes algorithms for feature extraction, feature description and correspondence. The feature point extraction algorithm could extract more salient features and the correspondence algorithm is more robust for pose estimation compared to known works. Experimental results show 85.7% hit rate at rank-1 accuracy based on matching of 7 partial sets to a database of 100 full sets in significantly reduced retrieval time. The hit rate increases to 100% with parameter adjustment. This work aims to enable computer-aided 3D dental identification and the proposed method could be adjunctively used with the traditional 2D dental identification method, as the available dental source for identification is still primarily 2D radiographs. Limitations of the methodology and future directions in matching highly fragmented and partial dental surfaces are discussed.