Late Paleozoic radiolarians from the Bentong-Raub suture zone, Peninsular Malaysia   总被引：3，自引：0，他引：3  

Frances C. P.  Spiller 《Island Arc》1996,5(2):91-103

Abstract Radiolarians extracted from marine siliceous sediments from the Bentong-Raub suture zone, Peninsular Malaysia have indicated a range of ages for olistostromal blocks of bedded chert, siliceous argillite and tuffaceous argillite, and chert clasts and lenses within the mélange from the suture zone. Late Devonian (Faniennian), Early Carboniferous (Tournaisian and Viséan) and Early Permian (Wolfcampian and Leonardian) ages are represented by seven radiolarian zones from ten localities along the suture zone. In stratigraphic order these include Holoeciscus 2–3 Assemblage Zones, Albaillella paradoxa Zone, Albaillella dejendrei Zone, Albaillella cartalla Zone, Pseudoalbaillella lomentaria Zone, Albaillella sinuata Zone and Pseudoalbaillella longtanensis Zone. Fifteen genera are represented by 35 species. The range of ages from Late Devonian to Early Permian suggests that an ocean existed between the Sibumasu and East Malaya terranes from at least Late Devonian to late Early Permian time and that closure of the ocean between the two terranes could not have occurred until after late Early Permian time. The range of ages and rock types from different depositional environments, indicate that the Bentong-Raub suture zone includes a disrupted accretionary complex.  相似文献   

Initial Calibration of the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO)     

Paul Boerner  Christopher Edwards  James Lemen  Adam Rausch  Carolus Schrijver  Richard Shine  Lawrence Shing  Robert Stern  Theodore Tarbell  Alan Title  C. Jacob Wolfson  Regina Soufli  Eberhard Spiller  Eric Gullikson  David McKenzie  David Windt  Leon Golub  William Podgorski  Paola Testa  Mark Weber 《Solar physics》2012,275(1-2):41-66

The Atmospheric Imaging Assembly (AIA) instrument onboard the Solar Dynamics Observatory (SDO) is an array of four normal-incidence reflecting telescopes that image the Sun in ten EUV and UV wavelength channels. We present the initial photometric calibration of AIA, based on preflight measurements of the response of the telescope components. The estimated accuracy is of order 25%, which is consistent with the results of comparisons with full-disk irradiance measurements and spectral models. We also describe the characterization of the instrument performance, including image resolution, alignment, camera-system gain, flat-fielding, and data compression.  相似文献   

Improvements in storm surge surrogate modeling for synthetic storm parameterization,node condition classification and implementation to small size databases     

Kyprioti  Aikaterini P.  Taflanidis  Alexandros A.  Plumlee  Matthew  Asher  Taylor G.  Spiller  Elaine  Luettich  Richard A.  Blanton  Brian  Kijewski-Correa  Tracy L.  Kennedy  Andrew  Schmied  Lauren 《Natural Hazards》2021,109(2):1349-1386

Surrogate models are becoming increasingly popular for storm surge predictions. Using existing databases of storm simulations, developed typically during regional flood studies, these models provide fast-to-compute, data-driven approximations quantifying the expected storm surge for any new storm (not included in the training database). This paper considers the development of such a surrogate model for Delaware Bay, using a database of 156 simulations driven by synthetic tropical cyclones and offering predictions for a grid that includes close to 300,000 computational nodes within the geographical domain of interest. Kriging (Gaussian Process regression) is adopted as the surrogate modeling technique, and various relevant advancements are established. The appropriate parameterization of the synthetic storm database is examined. For this, instead of the storm features at landfall, the features when the storm is at closest distance to some representative point of the domain of interest are investigated as an alternative parametrization, and are found to produce a better surrogate. For nodes that remained dry for some of the database storms, imputation of the surge using a weighted k nearest neighbor (kNN) interpolation is considered to fill in the missing data. The use of a secondary, classification surrogate model, combining logistic principal component analysis and Kriging, is examined to address instances for which the imputed surge leads to misclassification of the node condition. Finally, concerns related to overfitting for the surrogate model are discussed, stemming from the small size of the available database. These concerns extend to both the calibration of the surrogate model hyper-parameters, as well as to the validation approaches adopted. During this process, the benefits from the use of principal component analysis as a dimensionality reduction technique, and the appropriate transformation and scaling of the surge output are examined in detail.

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