سن‌یابی به‌روش لومینسانس نوری نمونه‌های رسوبی برداشت‌شده از تراس‌هایی رودخانه‌ای کارون در خوزستان، جنوب غرب ایران

The River Karun in lowland Khuzestan, SW Iran is influenced by various factors including tectonics, human activities, climate, and relative sea-level changes. Therefore, it is necessary to study these features from different aspects such as geology, geomorphology, paleoclimatology and Archeology. Disentangling these influences can be improved by investigating where river channels incise across active folds to produce river terraces. Determining the age of river terrace deposits has a fundamental role in these studies; especially since average rates of river incision since the time of terrace deposition can be a guide to average rates of tectonic uplift, particularly over longer timescales of thousands or tens of thousands of years where the influences of changes in aggradation and incision due to changes in sediment supply tend to be evened out (Bull, 1991; Burbank and Anderson 2012).
River terraces of the Karun river system were found associated with active folds in the Upper Khuzestan Plains. These folds were mostly asymmetric detachment folds and fault bend folds trending approximately NW-SE, with a more steeply dipping fore-limb to the south-west and a more gently dipping back-limb to the north-east (Blanc et al., 2003).
Woodbridge (2013) described these river terraces, and assigned each terrace a new name (from a nearby village or fold). As shown in Figure 1, four river terraces were associated with the Naft-e Safid Anticline: the 'Dar Khazineh terrace', the 'Batvand terrace', the 'Naft-e Safid terrace' and the 'Abgah terrace', on the fold fore-limb and back-limb. One river terrace was associated with the Sardarabad Anticline: the 'Kabutarkhan-e Sufla terrace', and one river terrace was associated with the Shushtar Anticline: the 'Kushkak terrace'; both on the fold back-limb Sediment samples were collected from the river terrace deposits and subjected to Optically Stimulated Luminescence (OSL) dating (Woodbridge and Frostick, 2014; Woodbridge et al., 2016). OSL dating was performed in the luminescence laboratory at the University of Sheffield, U.K. Both the palaeodose (De) and the dose rate was determined to derive an OSL age.
For De Determination the procedure outlined in Bateman and Catt (1996) was employed. The single aliquot regenerative (SAR) approach (Murray and Wintle 2000), was used for De determination. 
All the samples showed a weak naturally OSL decay curves. Many aliquots failed to show good growth curves. All aliquots where the recycling ratio exceeded 10% of unity were excluded from further analysis. Thus, only around 10-20 percent of measured aliquots for each sample passed the criteria of the SAR protocol and their De are reported. The most appropriate preheat temperature for each sample was selected using a dose recovery preheat plateau test. This resulted in selection of preheat temperatures of 220 °C for 10 seconds and cutheat of 200 °C for 10 seconds, which were applied to each sample prior to OSL measurement to remove unstable signal generated by laboratory irradiation. 
Analyst software was used for De determination. All samples demonstrated a high amount of replicate scatter with a large range of De values. Some of the distribution shape may reflect the limited population size of replicates but it also may reflect incomplete bleaching. Typically, poorly bleached sediments retain a significant level of residual signal from previous phases of sedimentary cycling, leading to inherent inaccuracies in the calculation of a palaeodose value. This is difficult to establish with any certainty from OSL data and should be taken in consideration with the site stratigraphy. In principle a well bleached unpost-depositionally disturbed sample should have replicate palaeodose (De) data which is normally distributed (See Bateman et al. 2003, Fig 3). By plotting the replicate data for each sample as a probability density function, some assessment of where older or younger material has been included in the sample measurements can be made. However, by determining the De of aliquots that contains 1000-2000 grains any heterogeneity in De that individual grains have may still be masked. We tried to overcome this problem by using smaller aliquots or at the single grain level. However, for these particular samples the weak OSL signal and low sensitivity to laboratory dose prevented such analysis.
In order to calculate an age, different models can be used. Woodbridge and Frostick (2014) and Woodbridge et al. (2016) published the age for each sample based on the mean De value determined by Finite Mixture Modelling or the Central Age Model. This paper provide the technical information behind dating these samples and provide all ages based on different models and without any judgement about partial bleaching, bioturbation or cryoturbation. Ages are quoted in years from the present day (2010) and are presented with one sigma confidence intervals which incorporate systematic uncertainties with the dosimetry data, uncertainties with the palaeomoisture content and errors associated with the De determination.