Qanat is not a hazard

Comments are presented on the article by Abbasnejad et al. (Environ Earth Sci 75:1306, 2016) dealing with qanat and hazards in Iran. My rebuttal starts from the direct attribution of the described hazards to qanat and addresses the importance in the correct use of terminology for geological hazards. All of the problems pointed out in Iran (subsidence, sinkholes, pollution) have, to me, an anthropogenic origin and cannot be directly ascribed to qanat. Eventually, I present some additional elements to highlight the remarkable importance of qanat systems and their influence on the development of similar underground structures in many countries of the Mediterranean Basin. This remarks the relevance of qanat as cultural heritage sites and the need for their preservation and valorization.     

Characteristics of deuterium excess parameters for geothermal water in Beijing

This study investigates the characteristics of geothermal water in 10 geothermal fields in Beijing. The relationships between the deuterium excess parameter (d) and temperature, depth, age of geothermal groundwater, groundwater flow field, and Eh were investigated using geothermal groundwater samples. Results showed that (1) the average d value of geothermal water is 5.4, whereas that of the groundwater in normal temperature is 6.04. The differences are induced by the oxygen isotope exchange during the water–rock interaction, which may be more easily completed in geothermal water than in cold groundwater. (2) The d value increases remarkably with the age of the geothermal groundwater. The d value increases from 11.2 to 14.6 when the age of the geothermal water is 12,760 ± 130 a and 38,960 ± 630 a, respectively. Moreover, the isotope heat exchange for composition of the hydrogen and oxygen isotopes in the geothermal groundwater proceeds sufficiently with time. (3) The d value decreases from 5.72 to 3.03 when the depth increases from 125.13 to 3221 m. Generally, in the same area, the d value decreases with depth because the temperature is increasing. (4) The d value of the groundwater gradually reduces from the northern recharge area to the southern discharge area. The average d value is 7.31 in the northern recharge area and 5.68 in the middle Beijing Depression, whereas the d value in the southern area of Fengheying is ?9.20. The larger difference in d values between the recharge and discharge areas is due to the slower velocity of underwater flow, which induces longer time for oxygen exchange. (5) The relationship between the d and Eh is complex. When Eh is <200 mV, the d value of the geothermal water decreases with the decrease in Eh. When Eh is higher than 200 mV, the d value increases slightly with the decrease in Eh. The study of the characteristics of deuterium excess parameters for geothermal water could provide a scientific isotopic evidence for assessment and exploitation measures in geothermal groundwater systems.     

2D probabilistic prediction of sparsely measured earth properties constrained by geophysical imaging fully accounting for tomographic reconstruction ambiguity

Many hydrological, environmental, or engineering exploration tasks require predicting spatially continuous scenarios of sparsely measured borehole logging data. We present a methodology to probabilistically predict such scenarios constrained by ill-posed geophysical tomography. Our approach allows for transducing tomographic reconstruction ambiguity into the probabilistic prediction of spatially continuous target parameter scenarios. It is even applicable to data sets where petrophysical relations in the survey area are non-unique, i.e., different facies related petrophysical relations may be present. We employ static two-layer artificial neural networks (ANNs) for prediction and additionally evaluate, whether the training performance of the ANNs can be used to rank geophysical tomograms, which are mathematically equal reconstructions of physical parameter distributions in the ground. We illustrate our methodology using a realistic synthetic database for maximal control about the prediction performance and ranking potential of the approach. For doing so, we try to link geophysical radar and seismic tomography as input parameters to porosity of the ground as target parameter of ANN. However, the approach is flexible and can cope with any combination of geophysical tomograms and hydrologic, environmental or engineering target parameters. Ranking of equivalent geophysical tomograms based on additional borehole logging data is found to be generally possible, but risks remain that the ranking based on the ANN training performance does not fully coincide with the closeness of geophysical tomograms to ground truth. Since geophysical field data sets do usually not offer control options similar to those used in our synthetic database, we do not recommend the utilization of recurrent ANNs to learn weights for the individual geophysical tomograms used in the prediction procedure.     

Landslide hazard mapping in the Constantine city,Northeast Algeria using frequency ratio,weighting factor,logistic regression,weights of evidence,and analytical hierarchy process methods

Landslides constitute the most widespread and damaging natural hazards in the Constantine city. They represent a significant constraint to development and urban planning. In order to reduce the risk related to potential landslide, there is a need to develop a comprehensive landslide hazard map (LHM) of the area for an efficient disaster management and for planning development activities. The purpose of this research is to prepare and compare the LHMs of the Constantine city, by applying frequency ratio (FR), weighting factor (Wf), logistic regression (LR), weights of evidence (WOE), and analytical hierarchy process (AHP) methods used in a framework of the geographical information system (GIS). Firstly, a landslide inventory map has been prepared based on the interpretation of aerial photographs, high resolution satellite images, fieldwork, and available literature. Secondly, eight landslide-conditioning factors such as lithology, slope, exposure, rainfall, land use, distance to drainage, distance to road, and distance to fault have been considered to establish LHMs using the FR, Wf, LR, WOE, and AHP models in GIS. For verification, the obtained LHMs have been validated comparing the LHMs with the known landslide locations using the receiver operating characteristics curves (ROC). The validated results indicate that the FR method provides more accurate prediction (86.59 %) of LHMs than the WOE (82.38 %), AHP (77.86 %), Wf (77.58 %), and LR (70.45 %) models. On the other hand, the obtained results showed that all the used models in this study provided a good accuracy in predicting landslide hazard in Constantine city. The established maps can be used as useful tools for risk prevention and land use planning in the Constantine region.     

An evaluation of the relationship between physical/mechanical properties and mineralogy of landscape rocks as determined by hyperspectral reflectance

We investigated the relationships between mineral content and the physical and mechanical properties of landscape rock using a non-destructive remote sensing method applied in the laboratory. Using this technique, the spectral properties of the landscape rock could be collected at different wavelengths without harming the samples. Differences in spectral reflectance were compared with the physical and mechanical properties of the stone. Significant correlations were observed between reflectance values and the rocks’ mechanical and physical properties, with correlation coefficients of 95 to 99 %. However, establishing a correlation between two variables is not a sufficient condition to establish a causal relationship. Mineral densities and mineral content are characteristics used for the classification of landscape rock. We have concluded that although spectral signatures from landscape rock can be used for predicting which stones might have similar features when comparing two batches of stone, the high correlations we discovered cannot confirm a cause and effect relationship that would allow for the prediction of a rock’s physical and mechanical properties. Although this conclusion is disappointing, the mineral content and the significant correlations discovered by hyperspectral reflectance scanning can be used as supplementary information when comparing two samples of landscape rock.     

Artificial neural network inversion of magnetic anomalies caused by 2D fault structures

A new approach is proposed to interpret magnetic anomalies caused by 2D fault structures. This approach is based on the artificial neural network inversion, utilizing particularly modular neural network algorithm. The inversion process is implemented to estimate the parameters of 2D fault structures where it has been verified first on synthetic models. The results of the inversion show that the parameters derived from the inversion agree well with the true ones. The analysis of noise has been studied in order to investigate the stability of the approach where it has been tested for contaminated anomalies with 5 and 10 % of white Gaussian noise. The results of the inversion provide satisfactory results even with contaminated signals.The validity of the approach has been demonstrated through real data taken from New South Wales, Australia. A comparable and satisfactory agreement is shown between the inversion results of the neural network and those from techniques published in literatures.