Assessment of groundwater suitability for irrigation in a gold mine surrounding area,NE Iran

The Kouh-e Zar mining area with iron oxide-rich types of Cu–Au (IOCG)-type gold mineralization is located in a fractured zone between two main “Darouneh” and “Taknar” faults in 35 km northwest of Torbat-e Heydarieh. In this study, the hydrogeochemistry and water quality of groundwater were examined for irrigation uses. Totally, 11 groundwater samples were collected in semi-arid area surrounding the mine. According to the irrigation water quality indices such as sodium absorption ratio, sodium percentage, residual sodium carbonate, residual sodium bicarbonate, potential salinity, salinity index, salinity hazard, permeability index and magnesium hazard, the water resources were appraised suitable to unsuitable. Na⁺ was a dominant cation and HCO₃^? was a dominant anion in the water samples. Fortunately, SO₄^2? content is low (<?250 mg/L) in the water samples because of low-sulfide content mineralization in this mine. Water–rock interaction was defined as the controlling process on groundwater chemistry based on the Gibbs diagram. Calculated saturation indices revealed that the anion and cations in groundwater originated from dissolution of minerals and evaporation process. In the case of dominant Ca²⁺ and Mg²⁺, they were originated by dissolution of carbonate minerals such as calcite, dolomite and aragonite. Na⁺ was likely originated by plagioclase weathering in the brecciated volcanic rocks. Though the sulfidic mineralization is not so high in the Kouh-e Zar area, however, considering the existence of metalogenic mineralization in the Kouh-e Zar area, there is also a risk potential of release of toxic elements into the groundwater on which further deep investigation is ongoing in the area.     

Ultrasonic prediction of crack density using machine learning:A numerical investigation

Cracks are accounted as the most destructive discontinuity in rock, soil, and concrete. Enhancing our knowledge from their properties such as crack distribution, density, and/or aspect ratio is crucial in geo-systems. The most well-known mechanical parameter for such an evaluation is wave velocity through which one can qualitatively or quantitatively characterize the porous media. In small scales, such information is obtained using the ultrasonic pulse velocity(UPV) technique as a non-destructive test. In large-scale geo-systems, however, it is inverted from seismic data. In this paper, we take advantage of the recent advancements in machine learning(ML) for analyzing wave signals and predict rock properties such as crack density(CD) – the number of cracks per unit volume. To this end, we designed numerical models with different CDs and, using the rotated staggered finite-difference grid(RSG) technique, simulated wave propagation. Two ML networks, namely Convolutional Neural Networks(CNN) and Long Short-Term Memory(LSTM), are then used to predict CD values. Results show that, by selecting an optimum value for wavelength to crack length ratio, the accuracy of predictions of test data can reach R2> 96% with mean square error(MSE) < 25e-4(normalized values). Overall, we found that:(i) performance of both CNN and LSTM is highly promising,(ii) accuracy of the transmitted signals is slightly higher than the reflected signals,(iii) accuracy of 2D signals is marginally higher than 1D signals,(iv)accuracy of horizontal and vertical component signals are comparable,(v) accuracy of coda signals is less when the whole signals are used. Our results, thus, reveal that the ML methods can provide rapid solutions and estimations for crack density, without the necessity of further modeling.     

Comparing Training-Image Based Algorithms Using an Analysis of Distance

As additional multiple-point statistical (MPS) algorithms are developed, there is an increased need for scientific ways for comparison beyond the usual visual comparison or simple metrics, such as connectivity measures. In this paper, we start from the general observation that any (not just MPS) geostatistical simulation algorithm represents two types of variability: (1) the within-realization variability, namely, that realizations reproduce a spatial continuity model (variogram, Boolean, or training-image based), (2) the between-realization variability representing a model of spatial uncertainty. In this paper, it is argued that any comparison of algorithms needs, at a minimum, to be based on these two randomizations. In fact, for certain MPS algorithms, it is illustrated with different examples that there is often a trade-off: Increased pattern reproduction entails reduced spatial uncertainty. In this paper, the subjective choice that the best algorithm maximizes pattern reproduction is made while at the same time maximizes spatial uncertainty. The discussion is also limited to fairly standard multiple-point algorithms and that our method does not necessarily apply to more recent or possibly future developments. In order to render these fundamental principles quantitative, this paper relies on a distance-based measure for both within-realization variability (pattern reproduction) and between-realization variability (spatial uncertainty). It is illustrated in this paper that this method is efficient and effective for two-dimensional, three-dimensional, continuous, and discrete training images.