Forward induced seismic hazard assessment: application to a synthetic seismicity catalogue from hydraulic stimulation modelling

The M _w 3.2-induced seismic event in 2006 due to fluid injection at the Basel geothermal site in Switzerland was the starting point for an ongoing discussion in Europe on the potential risk of hydraulic stimulation in general. In particular, further development of mitigation strategies of induced seismic events of economic concern became a hot topic in geosciences and geoengineering. Here, we present a workflow to assess the hazard of induced seismicity in terms of occurrence rate of induced seismic events. The workflow is called Forward Induced Seismic Hazard Assessment (FISHA) as it combines the results of forward hydromechanical-numerical models with methods of time-dependent probabilistic seismic hazard assessment. To exemplify FISHA, we use simulations of four different fluid injection types with various injection parameters, i.e. injection rate, duration and style of injection. The hydromechanical-numerical model applied in this study represents a geothermal reservoir with preexisting fractures where a routine of viscous fluid flow in porous media is implemented from which flow and pressure driven failures of rock matrix and preexisting fractures are simulated, and corresponding seismic moment magnitudes are computed. The resulting synthetic catalogues of induced seismicity, including event location, occurrence time and magnitude, are used to calibrate the magnitude completeness M _c and the parameters a and b of the frequency-magnitude relation. These are used to estimate the time-dependent occurrence rate of induced seismic events for each fluid injection scenario. In contrast to other mitigation strategies that rely on real-time data or already obtained catalogues, we can perform various synthetic experiments with the same initial conditions. Thus, the advantage of FISHA is that it can quantify hazard from numerical experiments and recommend a priori a stimulation type that lowers the occurrence rate of induced seismic events. The FISHA workflow is rather general and not limited to the hydromechanical-numerical model used in this study and can therefore be applied to other fluid injection models.     

Evidence for the occurrence of hydrogeochemical processes in the groundwater of Khoy plain,northwestern Iran,using ionic ratios and geochemical modeling

Rapid population growth, industrialization, and agricultural expansion in the Khoy area (northwestern Iran) have led to its dependence on groundwater and degradation of groundwater quality. This study attempts to decipher the major processes and factors that degrade the groundwater quality of the Khoy plain. For this purpose, 54 groundwater samples from unconfined and confined aquifers of the plain were collected in July 2017 and analyzed for major cations and anions (Na, K, Ca, Mg, HCO₃, SO_4, and Cl), minor ions (NO₃ and F), and Al. Magnesium and bicarbonate were identified as the dominant cation and anion, respectively. Several ionic ratios and geochemical modeling using PHREEQC indicated that the most important hydrogeochemical processes to affect groundwater quality in the plain were weathering and dissolution of evaporitic and silicate minerals, mixing, and ion exchange. There were smaller effects from evaporation and anthropogenic factors (e.g., industries). Results showed that the high salinity of the groundwater in the northeast area of the plain was due to the high solubility of the evaporitic minerals, e.g., halite and gypsum. Reverse ion exchange and the contribution of mineral dissolution were more significant than ion exchange in the northeastern part of the plain. Elevated salinity of the groundwater in the southeast was attributed mostly to reverse ion exchange and somewhat to evaporation.     

Spatial distribution of cadmium and lead in the sediments of the western Anzali wetlands on the coast of the Caspian Sea (Iran)

Spatial distribution patterns of total cadmium (Cd) and lead (Pb), their bioavailable fractions and total organic matter in sediment from Anzali wetlands are provided. Total sediment Pb was higher than Cd (34.95 versus 0.024 μg/g dry weight). The geoaccumulation index indicated that the sediment was “uncontaminated”, but some stations were categorized as “unpolluted” to “moderately polluted”. Less than 0.01 of Pb existed in exchangeable and carbonate fractions. The sum of exchangeable and carbonate-bound fractions of Cd was 42%, suggesting that Cd poses high risk to the aquatic ecosystems. Total Cd and Pb exhibited positive relationships with total organic matter. Considering spatial distribution maps of total and bioavailable fractions of metals suggested that high concentrations of metals does not necessarily indicate high bioavailable fraction. The methodologies we used in this study can be in more effective management of aquatic ecosystems, as well as ecological risk assessment of metals, and remediation programs.     

Direct CPT and CPTu methods for determining bearing capacity of helical piles

Helical piles are structural deep foundation elements, which can be categorized as torque-driven piles without any limitations to implement in marine situations. Different methods are used to predict the axial capacity of helical piles, such as static analysis, but have some limitation for this type of piles on marine conditions. In situ testing methods as supplement of static analysis have been rarely used for helical piles. In geotechnical engineering practice, the most common in situ tests particularly applicable for coastal or offshore site investigation are cone penetration test (CPT) and piezocone penetration test (CPTu). The CPT is simple, repeatable, and prepares the continuous records of soil layers. In this paper, a data bank has been compiled by collecting the results of static pile load tests on thirty-seven helical piles in ten different sites including CPT or CPTu data. Axial capacities of thirty-seven helical piles in different sites were predicted by direct CPT methods and static analysis. Accuracy estimation of ten direct CPT methods to predict the axial capacity of helical piles was investigated in this study. Comparisons have been made among predicted values and measured capacity from the pile load tests. Results indicated that the recently developed methods such as NGI-05 (2005), ICP-05 (2005), and UWA-05 (2005) predicted axial capacity of helical piles more accurately than the other methods such as Meyerhof (1983), Schmertmann (1978), Dutch (1979), LCPC (1982), or Unicone (1997). However, more investigations are required to establish better correlation between CPT data and axial capacity of helical piles.     

Socio-hydrological framework for investigating farmers’ activities affecting the shrinkage of Urmia Lake; hybrid data mining and agent-based modelling

ABSTRACT

Developing a general framework to capture the complexities associated with the non-linear and adaptive nature of farmers facing water resources scarcity is a challenging problem. This paper integrates agent-based modelling (ABM) and a data mining method to develop a hybrid socio-hydrological framework to provide future insights for policy-makers. The data associated with the farmers’ main characteristics were collected through field surveys and interviews. Afterwards, the association rule was employed to discover the main patterns representing the farmers’ agricultural decisions. The discovered patterns were then used as the behavioural rules in ABM to simulate the agricultural activities. The proposed framework has been was applied to explore the interactions between agricultural activities and the main river feeding the Urmia-Lake, Iran. The outcomes indicate that farmers’ acquisitive traits and belongings have significant impacts on their socio-hydrological interactions. The reported values of the efficiency criteria may support the satisfactory performance of the proposed framework.     

The bituminous mixtures of Tall-e Abu Chizan: A Vth millennium BC settlement in southwestern Iran

Various bituminous artifacts were excavated from the Tall-e Abu Chizan, a late prehistoric (Middle Susiana to Middle Uruk) settlement on the middle of the Curvy plain, between the Karun River and the Ram Hormoz Plain. All samples dated from the Vth millennium BC and cover three periods: 5000–4700 BC (Late Middle Susiana), 4700–4200 BC (Late Susiana 1) and 4200–3900 BC (Late Susiana 2). The bitumens were studied using the techniques of petroleum geochemistry and were compared both to the unaltered crude oils produced from the main oil fields in the area and to the famous Mamatain oil seeps. All samples are very rich in bitumen (average 46 wt%) which has been biodegraded and oxidized. Despite these alteration phenomena, δ¹³C of asphaltenes occur within a narrow range of less than 1‰ PDB. Biodegradation affected the steranes, terpanes, dibenzothiophenes and mono- and triaromatic steroids. Molecular characteristics of terpanes, especially the occurrence of 18α (H)-oleanane, suggest that the bitumen from Tall-e Abu Chizan is a mixture generated from Cretaceous Kazdhumi and Eocene Pabdeh petroleum source rocks. In that respect, bitumens from Tall-e Abu Chizan belong to the same oil family as oil from the Naft Safid field, which is in the vicinity of the archaeological site. In fact, the bitumen at Tall-e Abu Chizan likely originated from oil seepages at Naft Safid. These oil seeps have not yet been sampled or analysed.     

Wave data assimilation using a hybrid approach in the Persian Gulf

The main goal of this study is to develop an efficient approach for the assimilation of the hindcasted wave parameters in the Persian Gulf. Hence, the third generation SWAN model was employed for wave modeling forced by the 6-h ECMWF wind data with a resolution of 0.5°. In situ wave measurements at two stations were utilized to evaluate the assimilation approaches. It was found that since the model errors are not the same for wave height and period, adaptation of model parameter does not result in simultaneous and comprehensive improvement of them. Therefore, an approach based on the error prediction and updating of output variables was employed to modify wave height and period. In this approach, artificial neural networks (ANNs) were used to estimate the deviations between the simulated and measured wave parameters. The results showed that updating of output variables leads to significant improvement in a wide range of the predicted wave characteristics. It was revealed that the best input parameters for error prediction networks are mean wind speed, mean wind direction, wind duration, and the wave parameters. In addition, combination of the ANN estimated error with numerically modeled wave parameters leads to further improvement in the predicted wave parameters in contrast to direct estimation of the parameters by ANN.     

Improving urban cellular automata performance by integrating global and geographically weighted logistic regression models

In many of the conventional cellular automata (CA) models, particularly Urban‐CA which are used for urban growth, the spatial heterogeneities and local differences of the land use conversion processes are ignored. Global logistic regression (LR) is a popular model employed to define the transition rules of Urban‐CA. By considering the local characteristics, Geographically Weighted Logistic Regression (GWLR) provides interesting capabilities for urban growth modelling. In this research, in addition to using GWLR in the definition of transition rules, the advantages of integrating GWLR and LR for urban growth simulation were evaluated; these have not been considered in previous studies. Local and global probabilities obtained from the calibration of GWLR and LR were combined to define the transition rules of an Urban‐CA. Urban growth was simulated in the Islamshahr sub‐region located southwest of Tehran, Iran for the two periods 1992‐1996 and 1996‐2002, and data from these periods were used for training and testing the prediction abilities, respectively. In the first period, GWLR showed good performance and a significant contribution to the enhancement of the simulation performance, but in the second period, the effectiveness of LR on the prediction accuracy increased. Due to their complementary roles, the integration of the GWLR and LR models resulted in improved simulation performance in both periods.     

Non-stationary extreme value analysis in a changing climate

This paper introduces a framework for estimating stationary and non-stationary return levels, return periods, and risks of climatic extremes using Bayesian inference. This framework is implemented in the Non-stationary Extreme Value Analysis (NEVA) software package, explicitly designed to facilitate analysis of extremes in the geosciences. In a Bayesian approach, NEVA estimates the extreme value parameters with a Differential Evolution Markov Chain (DE-MC) approach for global optimization over the parameter space. NEVA includes posterior probability intervals (uncertainty bounds) of estimated return levels through Bayesian inference, with its inherent advantages in uncertainty quantification. The software presents the results of non-stationary extreme value analysis using various exceedance probability methods. We evaluate both stationary and non-stationary components of the package for a case study consisting of annual temperature maxima for a gridded global temperature dataset. The results show that NEVA can reliably describe extremes and their return levels.