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Register a new userTectonic Study of Al-Sin Faults Zone
دراسة تكتونية لنطاق فوالق السن
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بينت الدراسة التكتونية و التحاليل البنيوية لنطاق فوالق السن أنه يتألف من مجموعة من الفوالق العادية الصرفة ذات اتجاهات E-W إلى NE- SW و تزداد فيها الرمية الشاقولية من الشرق باتجاه الغرب. إن تشكل و تطور نطاق فوالق السن مرتبط بشكل رئيسي بعملية نهوض السلسلة الساحلية أثناء تشكل الفالق المشرقي في البمليوسين الأسفل. استخدم نطاق فوالق السن بنيات فالقية NE- SW موروثة من زمن الكريتاسي الأسفل الأمر الذي يفسر الانح ا رف اليام في اتجاهه من E-W إلىNE- SW.
Tectonic study and structural analyses of Al-Sin Faults Zone show that it is composed from a group of E-W to NE-SW pure normal faults, characterizing by increasing of vertical throw from east to west. Formation and evolution of Al-Sin Faults Zone principally related to coastal Range uplift during Levant Fault formation in lower Pliocene. Al-Sin Faults Zone used heritable faults from lower Cretaceous, witch interpret the important deviation of its direction from E-W to NE-SW.
References used
AL ABDALLA A. Evolution Tectonique de la Plate-forme Arabe en Syrie depuis le Mésozoïque. Thèse Doct. Université de Paris6. (2008).302p
(ALJASSIM A.K. Stratigraphy and tectonic of Nahr Alkabir Alshmali Depression, Report on the geological survey, Damascus. (1969
(AL-JRMAKANI I. Geological Maps of Syria (Banyass), 1\50000. Damascus (1979
ANGELIER J. Tectonic analysis of fault slip data sets. J. 5- Geophys. Res. 89, (1984). p.5835-5848
GOMEZ F., KHAWLIE M., TABET C., DARKAL A., KHAIR K., BARAZANGI M. (2006). Late Cenozoic uplift along the northern Dead Sea transform in Lebanon and Syria, Earth and Planetary Science Letters 241 p.913– 931
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The obtained results by hydrogeological and Tectonic survey related to fresh water resources in Al-Sin Basin, indicated that the fundamental aquifer of underground water in AL-SIN region belong to Jurassic. The alimentary basin of Al-Sin spring depen
d upon filtration of rainfall water and snow throw Jurassic formation from north and north–east of basin. Direction of underground water circulation is toward west and south-west where we find flow place of Al-Sin spring and submarine fresh springs near seashore. Tectonical study shows the existence of fractures and fault groups with NE-SW, ENE-WSW and NW-SE directions. Groundwater movement has the same direction of fractures and faults NE-SW to ENE-WSW and Al-Sin fault structure which allow the existence of direct relation between principal Jurassic aquifer and secondary aquifer especially Cenomanian one.
AlSin Spring spurts at the foot of coastal mountains and pours in The Mediterranean sea
near Arab-Almulk village. Presently, spring water used for drinking, irrigation and
industry, while excess water goes to sea.
Current research aims to determin
e the daily discharge response to daily rainfall, and to set
an equation for recession discharge for predicting spring discharge and volumes of flow
after definite time from the beginning of spring base flow, which allows to operate and
manipulate available water resources through an optimum design of water intake from this
spring.
Response time of AlSin Spring between (3-5) days for average discrete daily rainfall with
high intensity which caused 0.5 ~ 1.0 m3/sec increasing in spring discharge value.
Yearly discharge trends to decrease with a rate of 0.0975 m3/sec between 1974 and 2016
years. While the monthly minimum discharges increase about 0.1284 m3/sec, and monthly
maximums decrease about 0.0752 m3/sec between 1994 and 2016.
We recommend adopting recession curve analysis to predict the optimal discharge of
springs within definite periods.
Modelling the relationship between drinking water turbidity and other indicators of water
quality in Al-Sin drinking water purification plant using Dynamic Artificial neural
networks could help in the implementation of the stabilization for the per
formance of the
plant because these neural networks provide efficient tool to deal with the complex,
dynamic and non-linear nature of purification processes. They have the ability to response
to various instant changes in parameters influencing water purification.
In this research, four models of feed-forward back-propagation dynamic neural network
were designed to predict the effluent turbidity from Al-Sin drinking water purification
plant. The models were built based on turbidity, pH and conductivity of raw water data
while the effluent turbidity data were used for verify the performance accuracy of each
network. The results of this research confirm the ability of dynamic neural networks in
modeling and simulating the non-linearity behavior of water turbidity as well as to predict
its values. They can be used in Al-Sin drinking water purification plant in order to achieve
the stabilization of its performance.
Results of this research which had been done by hydrogeological and geoelectrical survey for fresh water resources in the study region (wells and springs) show that the fundamental aquifer of underground water in AL-SIN region belongs to Jurassic. Th
e alimentary basin of AL-SIN spring depend upon filtration of rain water and snow throw Jurassic formation from north and north–east of basin. Direction of underground water circulation on west and south-west where we find flow place of AL-SIN spring and submarine springs near seashore.
The study and design of water-intakes on springs is based on the analysis of time series of
historical measurements to achieve prediction of incoming water volumes or future
expected.
The research aims to model the monthly water flows of AL-SIN Sp
ring in Syrian Coast
and future expectations of these flows, by adopting the Box-Jenkins models to analyze the
time series data, due to its reliable accuracy. Monthly water flows, thus, monthly volumes,
for 101 month (from June 2008 to October 2016) were processed. Performing the stability
of the time series on variance and median and non-seasonality and making the wanted tests
on model residuals, we found that the best model to represent the data is SARIMA(2,0,1)
(2,1,0)12 , and after dividing the data into 81 month to build the model and 20 month to test
it. Depending on the smallest of weighted mean of criteria RMSE, MAP, MAE,. The best
predicted model was SARIMA (3,1,0) (1,1,0)12 and the model gave the nearest predicted
values to actually measured data in spring.
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