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1.
Active tectonic morphology and submarine deformation of the northern Gulf of Eilat/Aqaba from analyses of multibeam data 总被引:1,自引:0,他引:1
Gideon Tibor Tina M. Niemi Zvi Ben-Avraham Abdallah Al-Zoubi Ronnie A. Sade John K. Hall Gal Hartman Emad Akawi Abdelrahmem Abueladas Rami Al-Ruzouq 《Geo-Marine Letters》2010,30(6):561-573
A high-resolution marine geophysical study was conducted during October-November 2006 in the northern Gulf of Aqaba/Eilat,
providing the first multibeam imaging of the seafloor across the entire gulf head spanning both Israeli and Jordanian territorial
waters. Analyses of the seafloor morphology show that the gulf head can be subdivided into the Eilat and Aqaba subbasins separated
by the north-south-trending Ayla high. The Aqaba submarine basin appears starved of sediment supply, apparently causing erosion
and a landward retreat of the shelf edge. Along the eastern border of this subbasin, the shelf is largely absent and its margin
is influenced by the Aqaba Fault zone that forms a steep slope partially covered by sedimentary fan deltas from the adjacent
ephemeral drainages. The Eilat subbasin, west of the Ayla high, receives a large amount of sediment derived from the extensive
drainage basins of the Arava Valley (Wadi ’Arabah) and Yutim River to the north–northeast. These sediments and those entering
from canyons on the south-western border of this subbasin are transported to the deep basin by turbidity currents and gravity
slides, forming the Arava submarine fan. Large detached blocks and collapsed walls of submarine canyons and the western gulf
margin indicate that mass wasting may be triggered by seismic activity. Seafloor lineaments defined by slope gradient analyses
suggest that the Eilat Canyon and the boundaries of the Ayla high align along north- to northwest-striking fault systems—the
Evrona Fault zone to the west and the Ayla Fault zone to the east. The shelf–slope break that lies along the 100 m isobath
in the Eilat subbasin, and shallower (70–80 m isobaths) in the Aqaba subbasin, is offset by approx. 150 m along the eastern
edge of the Ayla high. This offset might be the result of horizontal and vertical movements along what we call the Ayla Fault
on the east side of the structure. Remnants of two marine terraces at 100 m and approx. 150 m water depths line the southwest
margin of the gulf. These terraces are truncated by faulting along their northern end. Fossil coral reefs, which have a similar
morphological appearance to the present-day, basin margin reefs, crop out along these deeper submarine terraces and along
the shelf–slope break. One fossil reef is exposed on the shelf across the Ayla high at about 60–63 m water depth but is either
covered or eroded in the adjacent subbasins. The offshore extension of the Evrona Fault offsets a fossil reef along the shelf
and extends south of the canyon to linear fractures on the deep basin floor. 相似文献
2.
Ahmad Rami El-Nabulsi 《中国天文和天体物理学报》2011,(8)
We explore a 5D Brans-Dicke scalar cosmology by conjecturing that the four-dimensional Hubble parameter varies as H = εφs,ε∈ R and s is some unknown power index and that the extra-dimensions compactify as the visible dimensions expand as b(t) ≈ ax(t) ,x ∈ R-. We mainly discuss the case x =-1. For critical values of ε close to unity,it was observed that the acceleration of the universe occurs at redshift close to z = 0.8 which indicates that in our model,accelerated expansion of the universe began only recen... 相似文献
3.
Ahmad Rami El-Nabulsi 《Astrophysics and Space Science》2011,332(1):37-42
We investigate the late-time dynamics of a four-dimensional universe based on the effective action of a Brans-Dicke scalar
field in the presence of the matter source term, conformal coupling of the scalar curvature to the scalar field, a dynamical
cosmological constant and Gauss-Bonnet higher-order terms in the scalar curvature. Many new interesting features are revealed
and discussed in some details. 相似文献
4.
In this work we study the association between eruptive filaments/prominences and coronal mass ejections (CMEs) using machine learning-based algorithms that analyse the solar data available between January 1996 and December 2001. The support vector machine (SVM) learning algorithm is used for the purpose of knowledge extraction from the association results. The aim is to identify patterns of associations that can be represented using SVM learning rules for the subsequent use in near real-time and reliable CME prediction systems. Timing and location data in the US National Geophysical Data Center (NGDC) filament catalogue and the Solar and Heliospheric Observatory/Large Angle and Spectrometric Coronagraph (SOHO/LASCO) CME catalogue are processed to associate filaments with CMEs. In the previous studies, which classified CMEs into gradual and impulsive CMEs, the associations were refined based on the CME speed and acceleration. Then the associated pairs were refined manually to increase the accuracy of the training dataset. In the current study, a data-mining system is created to process and associate filament and CME data, which are arranged in numerical training vectors. Then the data are fed to SVMs to extract the embedded knowledge and provide the learning rules that can have the potential, in the future, to provide automated predictions of CMEs. The features representing the event time (average of the start and end times), duration, type, and extent of the filaments are extracted from all the associated and not-associated filaments and converted to a numerical format that is suitable for SVM use. Several validation and verification methods are used on the extracted dataset to determine if CMEs can be predicted solely and efficiently based on the associated filaments. More than 14?000 experiments are carried out to optimise the SVM and determine the input features that provide the best performance. 相似文献
5.
Rami T.F. Rekola 《Planetary and Space Science》2009,57(4):430-433
Currently we are aware of only one biosphere in the entire Universe, our own. Various ongoing observational programmes are, however, attempting to locate more. These searches for extraterrestrial life are among the most challenging and interesting tasks of modern science. The Universe is immense, and even the distances to the nearest stars are beyond our present capabilities to traverse, so that search strategies must be thought through carefully in terms of how, where and what to search for. Life is undoubtedly more likely in some environments than others, and environmental criteria must be fulfilled for life to arise, survive, evolve and thrive. As search resources are limited we should concentrate our search on habitable zones that are suitable for the kind of life we can most easily recognise, in other words, searches should be guided by our own biosphere. 相似文献
6.
This paper presents a hybrid system for automatic detection and McIntosh-based classification of sunspot groups on SOHO/MDI
white-light images using active-region data extracted from SOHO/MDI magnetogram images. After sunspots are detected from MDI
white-light images they are grouped/clustered using MDI magnetogram images. By integrating image-processing and neural network
techniques, detected sunspot regions are classified automatically according to the McIntosh classification system. Our results
show that the automated grouping and classification of sunspots is possible with a high success rate when compared to the
existing manually created catalogues. In addition, our system can detect and classify sunspot groups in their early stages,
which are usually missed by human observers. 相似文献
7.
Machine-learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The
NGDC flares catalogue and the SOHO/LASCO CME catalogue are processed to associate X and M-class flares with CMEs based on
timing information. Automated systems are created to process and associate years of flare and CME data, which are later arranged
in numerical-training vectors and fed to machine-learning algorithms to extract the embedded knowledge and provide learning
rules that can be used for the automated prediction of CMEs. Properties representing the intensity, flare duration, and duration
of decline and duration of growth are extracted from all the associated (A) and not-associated (NA) flares and converted to
a numerical format that is suitable for machine-learning use. The machine-learning algorithms Cascade Correlation Neural Networks
(CCNN) and Support Vector Machines (SVM) are used and compared in our work. The machine-learning systems predict, from the
input of a flare’s properties, if the flare is likely to initiate a CME. Intensive experiments using Jack-knife techniques
are carried out and the relationships between flare properties and CMEs are investigated using the results. The predictive
performance of SVM and CCNN is analysed and recommendations for enhancing the performance are provided. 相似文献
8.
Jacek?StankiewiczEmail author Michael?H.?Weber Ayman?Mohsen Rami?Hofstetter 《Pure and Applied Geophysics》2012,169(4):615-623
In the framework of the Dead Sea Integrated Research project (DESIRE), 59 seismological stations were deployed in the region
of the Dead Sea Basin. Twenty of these stations recorded data of sufficiently high quality between May and September 2007
to be used for ambient seismic noise analysis. Empirical Green’s functions are extracted from cross-correlations of long term
recordings. These functions are dominated by Rayleigh waves, whose group velocities can be measured in the frequency range
from 0.1 to 0.5 Hz. Analysis of positive and negative correlation lags of the Green’s functions makes it possible to identify
the direction of the source of the incoming energy. Signals with frequencies higher than 0.2 Hz originate from the Mediterranean
Sea, while low frequencies arrive from the direction of the Red Sea. Travel times of the extracted Rayleigh waves were measured
between station pairs for different frequencies, and tomographically inverted to provide independent velocity models. Four
such 2D models were computed for a set of frequencies, all corresponding to different sampling depths, and thus together giving
an indication of the velocity variations in 3D extending to a depth of 10 km. The results show low velocities in the Dead
Sea Basin, consistent with previous studies suggesting up to 8 km of recent sedimentary infill in the Basin. The complex structure
of the western margin of the Basin is also observed, with sedimentary infill present to depths not exceeding 5 km west of
the southern part of the Dead Sea. The high velocities associated with the Lisan salt diapir are also observed down to a depth
of ~5 km. The reliability of the results is confirmed by checkerboard recovery tests. 相似文献
9.
Solar Flare Prediction Using Advanced Feature Extraction, Machine Learning, and Feature Selection 总被引:2,自引:0,他引:2
Omar W. Ahmed Rami Qahwaji Tufan Colak Paul A. Higgins Peter T. Gallagher D. Shaun Bloomfield 《Solar physics》2013,283(1):157-175
Novel machine-learning and feature-selection algorithms have been developed to study: i) the flare-prediction-capability of magnetic feature (MF) properties generated by the recently developed Solar Monitor Active Region Tracker (SMART); ii) SMART’s MF properties that are most significantly related to flare occurrence. Spatiotemporal association algorithms are developed to associate MFs with flares from April 1996 to December 2010 in order to differentiate flaring and non-flaring MFs and enable the application of machine-learning and feature-selection algorithms. A machine-learning algorithm is applied to the associated datasets to determine the flare-prediction-capability of all 21 SMART MF properties. The prediction performance is assessed using standard forecast-verification measures and compared with the prediction measures of one of the standard technologies for flare-prediction that is also based on machine-learning: Automated Solar Activity Prediction (ASAP). The comparison shows that the combination of SMART MFs with machine-learning has the potential to achieve more accurate flare-prediction than ASAP. Feature-selection algorithms are then applied to determine the MF properties that are most related to flare occurrence. It is found that a reduced set of six MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties. 相似文献
10.
Thamer Khazaal Al-Ameri Nagham Mohammed Al-Jubouri Murtadha J. Isa Rami Eidan Al-Azzawi 《Arabian Journal of Geosciences》2013,6(10):3725-3735
Organic geochemical analysis and palynological studies of the organic matters of subsurface Jurassic and Lower Cretaceous Formations for two wells in Ajeel oil field, north Iraq showed evidences for hydrocarbon generation potential especially for the most prolific source rocks Chia Gara and Sargelu Formations. These analyses include age assessment of Upper Jurassic (Tithonian) to Lower Cretaceous (Berriasian) age and Middle Jurassic (Bathonian–Tithonian) age for Chia Gara and Sargelu Formations, respectively, based on assemblages of mainly dinoflagellate cyst constituents. Rock-Eval pyrolysis have indicated high total organic carbon (TOC) content of up to 18.5 wt%, kerogen type II with hydrogen index of up to 415 mg HC/g TOC, petroleum potential of 0.70–55.56 kg hydrocarbon from each ton of rocks and mature organic matter of maximum temperature reached (Tmax) range between 430 and 440 °C for Chia Gara Formation, while Sargelu Formation are of TOC up to 16 wt% TOC, Kerogen type II with hydrogen index of 386 mg HC/g TOC, petroleum potential of 1.0–50.90 kg hydrocarbon from each ton of rocks, and mature organic matter of Tmax range between 430 and 450 °C. Qualitative studies are done in this study by textural microscopy used in assessing amorphous organic matter for palynofacies type belonging to kerogen type A which contain brazinophyte algae, Tasmanites, and foraminifera test linings, as well as the dinoflagellate cysts and spores, deposited in dysoxic–anoxic environment for Chia Gara Formation and similar organic constituents deposited in distal suboxic–anoxic environment for Sargelu Formation. The palynomorphs are of dark orange and light brown, on the spore species Cyathidites australis, that indicate mature organic matters with thermal alteration index of 2.7–3.0 for the Chia Gara Formation and 2.9–3.1 for the Sargelu Formation by Staplin's scale. These characters have rated the succession as a source rock for very high efficiency for generation and expulsion of oil with ordinate gas that charged mainly oil fields of Baghdad, Dyala (B?aquba), and Salahuddin (Tikrit) Governorates. Oil charge the Cretaceous-Tertiary total petroleum system (TPS) are mainly from Chia Gara Formation, because most oil from Sargelu Formation was prevented passing to this TPS by the regional seal Gotnia Formation. This case study of mainly Chia Gara oil source is confirmed by gas chromatography–mass spectrometry analysis for oil from reservoirs lying stratigraphically above the Chia Gara Formation in Ajeel and Hamrine oil fields, while oil toward the north with no Gotnia seal could be of mainly Sargelu Formation source. 相似文献