Spectral reflectance measurements of estuarine waters

Shapes and variations of reflectance spectra in estuarine water were investigated for the purpose of monitoring chlorophyll in situ by optical means. A survey undertaken in an estuarine environment, using reflectance measurements between 400 and 850 nm with a full-width half maximum (FWHM) of about 2 nm, revealed that the first derivative in the neighbourhood of the chlorophyll absorption band shows a defined spectral region which can be used to estimate chlorophyll concentrations. Correlation between chlorophyll and the first derivative was found to be low, but a good relationship exists between the ratio of the reflectance R680/R670 and chlorophyll concentrations. Based on dissolved organic carbon measurements, it is assumed that chromophoric dissolved organic compounds mask the absorption band of chlorophyll in the blue part of the spectrum, resulting in a low correlation coefficient in that spectral range. Therefore, the use of the red bands is an alternative for measuring photosynthetic pigments in coastal water at longer wavelengths. Results presented here demonstrate that the spectral locations of bands in the visible are not adequate and that hyperspectral data are required for positioning the very narrow bands for measuring chlorophyll at longer wavelengths.     

Biogeochemical Prospecting of Sulphide Minerals in Winder Valley, Balochistan, Pakistan

Abstract: The Mor Range is mountainous terrain in southern Balochistan dominantly occupied by exposures of rocks of the Ferozabad Group which is comprised of Lower‐Middle Jurassic carbonates and siliciclastics. This group contains syngenetic and epigenetic Zn–Pb–Ba mineralization classified as Stratiform Sediment‐Hosted (SSH) and Mississippi Valley Type (MVT) deposits. Three important flora of the Saharo–Sindian, phytogeographical region of Pakistan, namely Tamarix aphyl–la, Salvadora oleoides and Acacia arabica have been studied as a mineralization prospecting tool. Samples of these species were collected from Winder Valley adjoining the Mor Range to prospect for new areas of mineralization, besides the known sulphide deposits. This study revealed the Draber, Thorar, Raj, Mithi and Kanrach localities as new prospect. Quantitative estimation of biophile elements (Zn–Cu–Pb) from the three species was made. A number of new Zn, Cu and Pb anomalies were distinguished in the area. The variation in the constituents of these species from different localities possibly varies with nature and distance from bedrock, mobility, climate, average abundance in the plant ash and exclusion mechanism of the flora. Acacia arabica with deep penetrating roots shows the best results and could be used to delineate new horizons of mineralization. In these species Zn and Cu are relatively concentrated whereas Pb shows limited enrichment. Lead therefore is the element best suited for pinpointing the mineralization owing to its relatively less mobile character. Relationships among Zn–Cu–Pb have been established using scattergrams and triangular variation diagram, which also demonstrate their genetic affiliation. Statistical analyses, such as determination of mean, mode, median, standard deviation, and coefficient of correlation, were also made to improve the raw geochemical data. These biogeochemical methods appear to be suitable in arid climate of Balochistan, if proper attention is paid to species selection.     

Le passage Ordovicien–Silurien et la partie inférieure du Silurien (Sud-Est du Massif armoricain, France)The Ordovician–Silurian transition and the lower part of the Silurian (southeastern Armorican Massif, France)

A new section in the Silurian graptolitic ‘phtanites’ (black cherts) of Les Fresnaies at Chalonnes-sur-Loire (SE Armorican Massif) shows for the first time that these rocks (1) succeed conformably to Uppermost Ordovician (Hirnantian) glaciomarine deposits and (2) contain successive graptolite assemblages that characterise the base of the Silurian, the whole Rhuddanian and Aeronian stages and the lower part of the Telychian. To cite this article: J.M. Piçarra et al., C. R. Geoscience 334 (2002) 1177–1183.     

Physical properties of three short period close binaries:KIC 2715417,KIC 6050116 and KIC 6287172

We present the physical parameters of three short period close binaries using data observed from the Kepler Space Telescope. All of these observations were taken in a single bandpass(which approximates the Johnson V-band). Our three systems are KIC 2715417, KIC 6050116 and KIC 6287172. The first system, KIC 2715417, is considered a semi-detached system with the secondary component filling its Roche lobe. The second system, KIC 6050116, is an overcontact system, while the third system, KIC 6287172, belongs to ellipsoidal variables as deduced from the Roche lobe geometry. For photometric analysis, we used the PHOEBE software package, which is based on the Wilson-Devinney code. Due to lack of spectroscopic data, the photometric mass ratios are determined from the analyses of light curves using the q-search method. The absolute parameters are determined using three different methods(Harmanec, Maceroni Van'tVeer and Gazeas Niarchos).     

Relative model score: a scoring rule for evaluating ensemble simulations with application to microbial soil respiration modeling

This paper defines a new scoring rule, namely relative model score (RMS), for evaluating ensemble simulations of environmental models. RMS implicitly incorporates the measures of ensemble mean accuracy, prediction interval precision, and prediction interval reliability for evaluating the overall model predictive performance. RMS is numerically evaluated from the probability density functions of ensemble simulations given by individual models or several models via model averaging. We demonstrate the advantages of using RMS through an example of soil respiration modeling. The example considers two alternative models with different fidelity, and for each model Bayesian inverse modeling is conducted using two different likelihood functions. This gives four single-model ensembles of model simulations. For each likelihood function, Bayesian model averaging is applied to the ensemble simulations of the two models, resulting in two multi-model prediction ensembles. Predictive performance for these ensembles is evaluated using various scoring rules. Results show that RMS outperforms the commonly used scoring rules of log-score, pseudo Bayes factor based on Bayesian model evidence (BME), and continuous ranked probability score (CRPS). RMS avoids the problem of rounding error specific to log-score. Being applicable to any likelihood functions, RMS has broader applicability than BME that is only applicable to the same likelihood function of multiple models. By directly considering the relative score of candidate models at each cross-validation datum, RMS results in more plausible model ranking than CRPS. Therefore, RMS is considered as a robust scoring rule for evaluating predictive performance of single-model and multi-model prediction ensembles.     

Dynamic behavior of a smart device on a surface subjected to earthquake motion

This paper leverages concepts from an existing model to simulate the planar response of a smart device subjected to friction forces induced by an underlying moving plane. An interpolation technique is used to enhance detection of transition points (between sticking and sliding states), which must be accurately identified because of the frequency of their occurrence during seismic motion. The behavior of a smart device on an unconstrained table or desk, which is itself on a moving floor, is introduced and discussed. After validation of the results using experimental data, the revised model is used to study the sliding potential of smart devices on a surface during strong seismic events. Sliding spectra associated with selected ground motions are presented and extended to incorporate the effect of vertical accelerations with the purpose of assessing their influence. It is shown that vertical accelerations have a minimal effect on the sliding behavior of smart devices and that a “probability of exceeding the slip limit” curve can be developed to relate the probability of sticking to a demand parameter that represents the ground motion.     

Multi scenario seismic hazard assessment for Egypt

Egypt is located in the northeastern corner of Africa within a sensitive seismotectonic location. Earthquakes are concentrated along the active tectonic boundaries of African, Eurasian, and Arabian plates. The study area is characterized by northward increasing sediment thickness leading to more damage to structures in the north due to multiple reflections of seismic waves. Unfortunately, man-made constructions in Egypt were not designed to resist earthquake ground motions. So, it is important to evaluate the seismic hazard to reduce social and economic losses and preserve lives. The probabilistic seismic hazard assessment is used to evaluate the hazard using alternative seismotectonic models within a logic tree framework. Alternate seismotectonic models, magnitude-frequency relations, and various indigenous attenuation relationships were amended within a logic tree formulation to compute and develop the regional exposure on a set of hazard maps. Hazard contour maps are constructed for peak ground acceleration as well as 0.1-, 0.2-, 0.5-, 1-, and 2-s spectral periods for 100 and 475 years return periods for ground motion on rock. The results illustrate that Egypt is characterized by very low to high seismic activity grading from the west to the eastern part of the country. The uniform hazard spectra are estimated at some important cities distributed allover Egypt. The deaggregation of seismic hazard is estimated at some cities to identify the scenario events that contribute to a selected seismic hazard level. The results of this study can be used in seismic microzonation, risk mitigation, and earthquake engineering purposes.     

Elastic properties of sands,Part 1: Micro computed tomography image analysis of grain shapes and their relationship with microstructure

Elastic properties of an unconsolidated sand are largely dependent on the elastic properties of its constituent grain and the micro-structure that defines how the grains are arranged within themselves. Coordination number, that is the average number of contacts a grain has with its neighbours, and contact surface area are the two parameters closely related to the microstructure. Moreover, grain shapes and sorting also have substantial influence on these parameters. To calculate these parameters and find any potential relationships with the shape factors, we acquire high-resolution micro computed tomography images of four mechanically compacted unconsolidated dry sand samples that are of different shape factors and sorting indices. After a comprehensive voxel-based data processing, we calculate shape factors such as sphericity and roundness of each grain in all samples. Using own algorithm, we then calculate the coordination number and contact surface area. Results show that samples of well-sorted and higher spherical and rounded grains have higher coordination number and contact surface area than the samples of poorly sorted and lower spherical and rounded grains. Among the poorly sorted samples, coordination number is largely dependent on the fraction of larger grain sizes present in the sample. Inside any given sample, grains of lower sphericity tend to have higher coordination numbers. Moreover, more spherical and rounded grains have greater contact surface area with their neighbours.     

A new fluid factor and its application using a deep learning approach

Amplitude interpretation for hydrocarbon prediction is an important task in the oil and gas industry. Seismic amplitude is dominated by porosity, the volume of clay, pore-filled fluid type and lithology. A few seismic attributes are proposed to predict the existence of hydrocarbon. This paper proposes a new fluid factor by adding a correct item based on the J attribute. The algorithm is verified through stochastic Monte Carlo modelling that contains various rock physical properties of sand and shale. Both gas and oil responses are separated by the new fluid factor. Furthermore, an approach based on the neural network model is trained using the deep learning method to predict the new fluid factor. The confusion matrix shows that this model performs well. This model allows the application of the new fluid factor in the seismic data. In this study, the Marmousi II data set is used to examine the performance of the new fluid factor, and the result is good. Most hydrocarbon reservoirs are identified in the shale–sandstone sequences. The combination of deep learning and the new fluid factor provides a more accurate way for hydrocarbon prediction.