Ground motion modelling in Tehran based on the stochastic method

This paper presents the development of a seismological model for the Tehran area. This modelling approach, which was originally developed in Eastern North America, has been used successfully in other parts of the world including Australia and China for simulating accelerograms and elastic response spectra. Parameters required for input into the model were inferred from seismological and geological information obtained locally. The attenuation properties of the earth crust were derived from the analysis of accelerogram records that had been collated from within the region in a previous study. In modelling local modifications of seismic waves in the upper crust, shear-wave velocity profiles have been constructed in accordance with the power law. Information inferred from micro-zonation studies (for near-surface conditions) and from measurements of teleseismic P-waves reflected from the deeper crusts as reported in the literature has been used to constrain parameters in the power-law relationships. This method of obtaining amplification factors for the upper crust distinguishes this study from earlier studies in the Tehran area (in which site amplification factors were inferred from the H/V ratio of the recorded ground motions). The regional specific seismological model so constructed from the study enabled accelerograms to be simulated and elastic response spectra calculated for a series of magnitude–distance combinations. Importantly, elastic response spectra calculated from the simulated accelerograms have been compared with those calculated from accelerograms recorded from earthquakes with magnitudes ranging between M6.3 and M7.4. The peak ground velocity values calculated from the simulated accelerograms have also been correlated with values inferred from macro-seismic intensity data of 17 historical earthquakes with magnitudes varying between 5.4 and 7.7 and with distances varying between 40 and 230 km. This paper forms part of the long-term strategy of the authors of applying modern techniques for modelling the attenuation behaviour of earthquakes in countries which are lacking in instrumental data of earthquakes.     

Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches

The main objective of this study is to integrate adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural network (ANN) to design an integrated supervised committee machine artificial intelligence (SCMAI) model to spatially predict the groundwater vulnerability to seawater intrusion in Gharesoo-Gorgan Rood coastal aquifer placed in the northern part of Iran. Six hydrological GALDIT parameters (i.e., G groundwater occurrence, A aquifer hydraulic conductivity, L level of groundwater above sea level, D distance from the shore, I impact of the existing status of seawater intrusion in the region, and T thickness of the aquifer) were considered as inputs for each model. In the training step, the values of GALDIT’s vulnerability index were conditioned by using the values of TDS concentration in order to obtain the conditioned vulnerability index (CVI). The CVI was considered as the target for each model. After training the models, each model was tested using a separate TDS dataset. The results indicated that the ANN and ANFIS algorithms performed better than the SVM algorithm. The values of correlation were obtained as 88, 87, and 80% for ANN, ANFIS, and SVM models, respectively. In the testing step of the SCMAI model, the values of RMSE, R², and r were obtained as 6.4, 0.95, and 97%, respectively. Overall, SCMAI model outperformed other models to spatially predicting vulnerable zones. The result of the SCMAI model confirmed that the western zones along the shoreline had the highest vulnerability to seawater intrusion; therefore, it seems critical to consider emergency protection plans for study area.

Graphic abstract