Probabilistic model to forecast earthquakes in the Zemmouri (Algeria) seismoactive area on the basis of moment magnitude scale distribution functions

Based on the moment magnitude scale, a probabilistic model was developed to predict the occurrences of strong earthquakes in the seismoactive area of Zemmouri, Algeria. Firstly, the distributions of earthquake magnitudes M _i were described using the distribution function F ₀(m), which adjusts the magnitudes considered as independent random variables. Secondly, the obtained result, i.e., the distribution function F ₀(m) of the variables M _i was used to deduce the distribution functions G(x) and H(y) of the variables Y _i = Log M _0,i and Z _i = M _0,i , where (Y _i ) _i and (Z _i ) _i are independent. Thirdly, some forecast for moments of the future earthquakes in the studied area is given.     

Seismic hazard assessment in the megacity of Blida (Algeria) and its surrounding regions using parametric-historic procedure

The region of Blida is characterized by a relatively high seismic activity, pointed especially during the past two centuries. Indeed, it experienced a significant number of destructive earthquakes such as the earthquakes of March 2, 1825 and January 2, 1867, with intensity of X and IX, respectively. This study aims to investigate potential seismic hazard in Blida city and its surrounding regions. For this purpose, a typical seismic catalog was compiled using historical macroseismic events that occurred over a period of a few hundred years, and the recent instrumental seismicity dating back to 1900. The parametric-historic procedure introduced by Kijko and Graham (1998, 1999) was applied to assess seismic hazard in the study region. It is adapted to deal with incomplete catalogs and does not use any subjective delineation of active seismic zones. Because of the lack of recorded strong motion data, three ground prediction models have been considered, as they seem the most adapted to the seismicity of the study region. Results are presented as peak ground acceleration (PGA) seismic hazard maps, showing expected peak accelerations with 10% probability of exceedance in 50-year period. As the most significant result, hot spot regions with high PGA values are mapped. For example, a PGA of 0.44 g has been found in a small geographical area centered on Blida city.     

A Modular Support Vector Machine for Active Learning of Urban Remote Sensing Images Classification in Algeria

In this work, we present a new strategy of active learning, based on a modular version of support vector machine (MSVM) applied to urban remote sensing images in Algeria. In general, the training set is highly imbalanced, which gives more complex models; this difficulty is solved by dividing the problem at hand into a set of sub-problems, where each sub-model could be simpler to solve. The support vector machine is introduced to solve the problem of classification based on image remote sensing data related to atmospheric conditions and illumination reflectance. The aim of the proposed method is to improve the accuracy in order to understand the correlated elements of urban structures (the site, the built, the parcels, the network, the space), to generate the final classification result. In particular, we propose a new method based on the modular support vector machine (MSVM) adopted to active learning method, using three different clustering methods (i) k-means, (ii) fuzzy c-means (FCM), and (iii) Gustafson–Kessel (GKclust). Experimental results obtained on two QuickBird multispectral images of Sétif and Batna cities in the eastern of Algeria confirm the capabilities of the proposed methods based on the ensemble of model trained with different task decomposition compared to a traditional model using active learning. This method improves each class presents a main register in urban structure tissues.     

Some Probabilistic and Statistical Properties of the Seismic Regime of Zemmouri (Algeria) Seismoactive Zone

Statistical tests have been used to adjust the Zemmouri seismic data using a distribution function. The Pareto law has been used and the probabilities of various expected earthquakes were computed. A mathematical expression giving the quantiles was established. The extreme values limiting law confirmed the accuracy of the adjustment method. Using the moment magnitude scale, a probabilistic model was made to predict the occurrences of strong earthquakes. The seismic structure has been characterized by the slope of the recurrence plot γ, fractal dimension D, concentration parameter K_sr, Hurst exponents H_r and H_t. The values of D, γ, K_sr, H_r, and H_t diminished many months before the principal seismic shock (M = 6.9) of the studied seismoactive zone has occurred. Three stages of the deformation of the geophysical medium are manifested in the variation of the coefficient G% of the clustering of minor seismic events.