Slowness-domain kinematical characteristics for horizontally layered orthorhombic media. Part II: Pre-critical slowness match     

Zvi Koren  Igor Ravve 《Geophysical Prospecting》2019,67(5):1134-1162

Part II of this paper is a direct continuation of Part I, where we consider the same types of orthorhombic layered media and the same types of pure-mode and converted waves. Like in Part I, the approximations for the slowness-domain kinematical characteristics are obtained by combining power series coefficients in the vicinity of both the normal-incidence ray and an additional wide-angle ray. In Part I, the wide-angle ray was set to be the critical ray (‘critical slowness match’), whereas in Part II we consider a finite long offset associated with a given pre-critical ray (‘pre-critical slowness match’). Unlike the critical slowness match, the approximations in the pre-critical slowness match are valid only within the bounded slowness range; however, the accuracy within the defined range is higher. Moreover, for the pre-critical slowness match, there is no need to distinguish between the high-velocity layer and the other, low-velocity layers. The form of the approximations in both critical and pre-critical slowness matches is the same, where only the wide-angle power series coefficients are different. Comparing the approximated kinematical characteristics with those obtained by exact numerical ray tracing, we demonstrate high accuracy. Furthermore, we show that for all wave types, the accuracy of the pre-critical slowness match is essentially higher than that of the critical slowness match, even for matching slowness values close to the critical slowness. Both approaches can be valuable for implementation, depending on the target offset range and the nature of the subsurface model. The pre-critical slowness match is more accurate for simulating reflection data with conventional offsets. The critical slowness match can be attractive for models with a dominant high-velocity layer, for simulating, for example, refraction events with very long offsets.  相似文献   

Obtaining the distribution of quiescent periods directly from the power spectral densities of Sea waves     

《Applied Ocean Research》2019

There is a growing practical interest in the ability to increase the sea states at which marine operations can be safely undertaken by exploiting the quiescent periods that are well known to exist under a wide range of sea conditions. While the actual prediction of quiescent periods at sea for the control of operations is a deterministic process, the long term planning of future maritime tasks that rely on these quiescent periods is a statistical process involving the anticipated quiescence properties of the forecasted sea conditions in the geographical region of interest. It is in principle possible to obtain such data in tabular form either large scale simulation or from field data. However, such simulations are computationally intensive and libraries of appropriate field data are not common. Thus, it is clearly attractive to develop techniques that exploit standard wave spectral models for describing the quiescence statistics directly from such spectra. The present study focuses upon such techniques and is a first step towards the production of a computationally low-cost quiescence prediction tool and compares its efficacy against simulations. Two significant properties emerge for a large class of wave spectral models that encompasses the ubiquitous Neumann and Pierson Moskowitz or Bretschneider forms. Firstly, the auto-correlation function of the wave profile that are required to produce the quiescence property can be obtained analytically in terms of standard special functions. This considerably reduces the computational cost making desktop computer-based planning tools a reality. Secondly, for each class of these parametric spectra, the probability of a given number of consecutive wave heights (normalised to the significant wave heights) less than some critical value is in fact independent of absolute wave height. Thus, for a broad class of practically interesting wave spectra all that is required to obtain the statistical distribution of the quiescent periods is simple rescaling.  相似文献   

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer   总被引：1，自引：0，他引：1  

Wei Chen  Xi Chen  Jianbing Peng  Mahdi Panahi  Saro Lee 《地学前缘(英文版)》2021,12(1):93-107

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.  相似文献   

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks     

Husam A.H.Al-Najjar  Biswajeet Pradhan 《地学前缘(英文版)》2021,12(2):625-637

In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.  相似文献   

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management   总被引：1，自引：0，他引：1  

Zizheng Guo  Yu Shi  Faming Huang  Xuanmei Fan  Jinsong Huang 《地学前缘(英文版)》2021,12(6):243-261

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70％landslide pixels)and validation data(30％landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20％interval of landslide occurrence probability contained 89％of the historical landslides but only accounted for 10.3％of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices.  相似文献   

Modelling spatial distribution of critically endangered Asian elephant and Hoolock gibbon in Bangladesh forest ecosystems under a changing climate     

《Applied geography (Sevenoaks, England)》2015

The Asian elephant (Elephas maximus) and Hoolock gibbon (Hoolock hoolock) are two globally endangered wildlife species limited to only tropical Asian forests. In Bangladesh both species are critically endangered and distributed mainly in the northeast and southeast hilly regions bordering neighboring India and Myanmar. Using existing distribution data, land-use/land cover, elevation and bio-climatic variables, we modeled the likely distribution of Asian elephant and Hoolock gibbon in Bangladesh for 2050 and 2070. We used the IPCC's Representative Concentration Pathways (RCPs) – RCP6.0 and RCP8.5 and Maximum Entropy algorithm for our modelling. Our study indicated that the Asian elephant will be more resilient to climate change compared with the Hoolock gibbon. Habitat loss for the Asian elephant is also expected to remain constant (i.e. 38%) throughout the period, whilst Hoolock gibbon habitat will be more sensitive to climatic variations, with the species predicted to be extirpated from the country by 2070. Being highly exposed to climate change with ever increasing land use pressures, we believe our study in Bangladesh can be used to enhance our understanding of future vulnerabilities of wildlife in a rapidly changing climate. A trans-boundary conservation program with greater attention to the species that are less resilient to climate change is also essential.  相似文献   

GPS RTK技术用于滑坡动态实时变形监测的研究   总被引：2，自引：0，他引：2  

王利  张勤  李寻昌  张永奇  管建安  涂锐 《工程地质学报》2011,19(2):193-198

为了研究GPS RTK技术用于滑坡动态变形监测的精度和可靠性,本文结合某类滑坡的大型物理模型试验,在滑坡体上布设了若干监测点,并用GPS RTK技术、全站仪三维测量技术和GPS单历元定位技术实时跟踪监测了该滑坡在自然状态下从稳定到产生破坏的全部过程。通过对监测数据的处理和分析,获得了RTK技术用于滑坡变形监测的可靠性和精度等技术参数,即在基准站和流动站同步观测到的卫星数在7颗以上且RTK系统的数据链能够正常工作的情况下,RTK测量的平面和高程精度就能分别控制在15mm和20mm以内。研究结果表明,RTK技术在一定条件下完全可用于滑坡灾害的动态实时变形监测。  相似文献   

含软弱夹层库岸滑坡滑带发育特征研究     

肖拥军  殷坤龙  柴波 《工程地质学报》2011,19(1):65-69

本文分析了巴东新城区巴东组第3段岩体中软弱夹层的分布特征、滑坡滑带的发育特征,结果表明巴东组第3段岩体中发育的滑坡滑带可与原岩中的软弱夹层对应,软弱夹层受构造剪切和地下水泥化作用发育成以碎石夹泥或黏土夹碎石为主的滑带.分析认为黄土坡滑坡、赵树岭滑坡的深层变形与巴东组第3段次级褶皱发育、层间劈理密集导致岩体破碎有关,而两...  相似文献   

彭州市干溪沟流域地震诱发地质灾害成因分析     

魏云杰  鲍艳  李战鲁 《工程地质学报》2011,19(4):582-587

干溪沟属于湔江的支流,在大地构造部位上位于龙门山断褶带中段前缘,地貌上属于侵蚀构造地貌和河流地貌,切割深,降雨量丰富,河谷、河流较发育.由于人工开采矿石普遍,地质灾害较发育,典型的地质灾害主要有大白岩崩塌体、大团包滑坡体、干溪沟潜在泥石流.大白岩逆冲崩塌体是在汶川大地震发生时,映秀一北川断层发生逆冲,上盘灰岩错出山坡,...  相似文献   

瓦屋山水电站厂房区红层岩体古滑坡稳定性研究     

王子忠  许模  杨绍平 《工程地质学报》2011,19(2):213-218

红层岩体以其岩体结构的软硬相间及软弱夹层发育而在边坡稳定方面表现为"易滑地层".瓦屋山水电站厂房区边坡为顺向坡,砂岩类岩石与黏土岩类岩石呈缓倾、互层状(倾角10°～20°)产出.构造与表生改造作用下,在这两类软硬相间的岩石界面上常形成分布较为连续的软弱夹层,这类岩体较易沿软弱夹层产生顺层滑动.地质历史时期,瓦屋山厂房区...  相似文献