Identification of hidden faults using determining velocity structure profile by spatial autocorrelation method in the west of Mashhad plain(Northeast of Iran)

Characterizing the subsurface structure is an important parameter for the improvement of seismic hazard assessment.Due to the tectonic complexity of the earth,some deep fractures do not reach the earth's surface and are not detectable with visual analysis.Therefore,the lack of knowledge of faults and fractures can result in disasters,especially in urban planning.Many geophysical methods can be used to estimate subsurface structure characterization.However,a more reliable method is required to assess seismic hazards and reduce potential damage in metropolitan areas without destroying buildings and structures.This paper aims to identify hidden faults and structures using shear wave velocity sections.To do this,surface wave dispersion curve was extracted from the vertical component of microtremor array recording using the spatial autocorrelation(SPAC)method in two profiles and 13 array stations(perpendicular to the altitudes)to obtain shear wave velocity structure(Vs)in the west of Mashhad,northeast of Iran.The results of shear wave velocity profiles(Vs)indicate sudden changes in the thickness of sediments.This can be related to the displacement of a normal fault in this area causing the bottom rock to fall and an increase in the alluvial thickness in the central part of the plain.The velocity in the floor rock is 2000 meters per second in this area.According to the surface outcrops and water wells data,its material is slate and Phyllite metamorphic rocks that are exposed in the adjacent heights.Besides,the seismic profile results were well consistent with electrical resistance data and well logs indicating that the tool array method is flexible,non-invasive,relatively fast,and effective for urban areas with satisfactory accuracy.

FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE

Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model （HMM） and support vector machine （SVM）. HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox＇s faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.

Dangerous, Hidden Fault Found Under Los Angeles

洛杉矶是美国西海岸的一座大城市,处在一个地震多发地带。科学家们发现了洛杉矶地下的一个隐蔽的断层,并确认该断层是过去该市遭受地震灾害的元凶,还预言今后此断层将会造成更强烈的地震。这则消息无疑会在洛杉矶市民心头蒙上一层阴影,当然,对洛杉矶的房地产商人来说也许打击更大。本文涉及了不少地理知识,其中最有用的也许是:The amount of damage that a quake can do depends not only on its magnitude(量级),but its depth and the type of structures on the surface.】