Analytical study of ground motion caused by seismic wave propagation across faulted rock masses

Studying seismic wave propagation across rock masses and the induced ground motion is an important topic, which receives considerable attention in design and construction of underground cavern/tunnel constructions and mining activities. The current study investigates wave propagation across a rock mass with one fault and the induced ground motion using a recursive approach. The rocks beside the fault are assumed as viscoelastic media with seismic quality factors, Q_p and Q_s. Two kinds of interactions between stress waves and a discontinuity and between stress waves and a free surface are analyzed, respectively. As the result of the wave superposition, the mathematical expressions for induced ground vibration are deduced. The proposed approach is then compared with the existing analysis for special cases. Finally, parametric studies are carried out, which includes the influences of fault stiffness, incident angle, and frequency of incident waves on the peak particle velocities of the ground motions.     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

Phasing of segmented mirrors: a new algorithm for piston detection

The point spread function of a segmented-mirror telescope is severely affected by segment misalignment, which can nullify the performance of adaptive optics systems. The piston and tilt of each segment must be precisely adjusted in relation to the other segments. Furthermore, the direct detection of the alignment error with natural stars would be desirable in order to monitor the errors during astronomical observation.
We have studied the lost information of the piston error caused by the presence of atmospheric turbulence in the measurements of curvature, and present a new algorithm for obtaining the local piston using the curvature sensor. A phase-wrapping effect is shown as responsible for the loss of curvature information and so the piston errors can no longer adequately be mapped; this happens not only in the presence of atmospheric turbulence, but also in its absence.
Good results are obtained using a new iterative method for obtaining the local piston error map. In the presence of atmospheric perturbation, the turbulent phase information obtained from a Shack–Hartmann sensor is introduced in our new iterative method. We propose a hybrid sensor composed of a curvature sensor and a Shack–Hartmann sensor, in order to complete all the information for the phasing. This design takes a short computation time and could be used in real time inside an adaptive optics system, where tilt and piston errors must be corrected.     

Optimal Compensation and Implementation for Adaptive Optics Systems

This paper develops a compensation algorithm based on Linear–Quadratic–Gaussian (LQG) control system design whose parameters are determined (in part) by a model of the atmosphere. The model for the atmosphere is based on the open-loop statistics of the atmosphere as observed by the wavefront sensor, and is identified from these using an auto-regressive, moving average (ARMA) model. The (LQG) control design is compared with an existing compensation algorithm for a simulation developed at ESO that represents the operation of MACAO adaptive optics system on the 8.2 m telescopes at Paranal, Chile. This revised version was published online in July 2006 with corrections to the Cover Date.     

用遗传算法解算病态方程

对应用遗传算法解决病态方程问题进行了探讨。利用拟合法而不是通过法方程求解参数，从而避免了法方程系数求逆，使病态方程的解答有了较好的结果。通过模拟计算并和其他方法进行比较，证明该方法是可行的和有效的。     

Magnetotelluric investigations for imaging electrical structure of Garhwal Himalayan corridor,Uttarakhand, India

Magnetotelluric investigations have been carried out in the Garhwal Himalayan corridor to delineate the electrical structure of the crust along a profile extending from Indo-Gangetic Plain to Higher Himalayan region in Uttarakhand, India. The profile passing through major Himalayan thrusts: Himalayan Frontal Thrust (HFF), Main Boundary Thrust (MBT) and Main Central Thrust (MCT), is nearly perpendicular to the regional geological strike. Data processing and impedance analysis indicate that out of 44 stations MT data recorded, only 27 stations data show in general, the validity of 2D assumption. The average geoelectric strike, N70°W, was estimated for the profile using tensor decomposition. 2D smooth geoelectrical model has been presented, which provides the electrical image of the shallow and deeper crustal structure. The major features of the model are (i) a low resistivity (<50Ωm), shallow feature interpreted as sediments of Siwalik and Indo-Gangetic Plain, (ii) highly resistive (> 1000Ωm) zone below the sediments at a depth of 6 km, interpreted as the top surface of the Indian plate, (iii) a low resistivity (< 10Ωm) below the depth of 6 km near MCT zone coincides with the intense micro-seismic activity in the region. The zone is interpreted as the partial melting or fluid phase at mid crustal depth. Sensitivity test indicates that the major features of the geoelectrical model are relevant and desired by the MT data.     

An operational multiscale system for hazards prediction, mapping, and response

By definition, a crisis is a situation that requires assistance to be managed. Hence, response to a crisis involves the merging of local and non-local emergency response personnel. In this situation, it is critical that each participant: (1) know the roles and responsibilities of each of the other participants; (2) know the capabilities of each of the participants; and (3) have a common basis for action. For many types of natural disasters, this entails having a common operational picture of the unfolding events, including detailed information on the weather, both current and forecasted, that may impact on either the emergency itself or on response activities. The Consequences Assessment Tool Set (CATS) is a comprehensive package of hazard prediction models and casualty and damage assessment tools that provides a linkage between a modeled or observed effect and the attendant consequences for populations, infrastructure, and resources, and, hence, provides the common operational picture for emergency response. The Operational Multiscale Environment model with Grid Adaptivity (OMEGA) is an atmospheric simulation system that links the latest methods in computational fluid dynamics and high-resolution gridding technologies with numerical weather prediction to provide specific weather analysis and forecast capability that can be merged into the geographic information system framework of CATS. This paper documents the problem of emergency response as an end-to-end system and presents the integrated CATS–OMEGA system as a prototype of such a system that has been used successfully in a number of different situations.     

Total Lipid and Fatty Acid Classes in Decomposing Mangrove Leaves of <Emphasis Type="Italic">Bruguiera gymnorrhiza</Emphasis> and <Emphasis Type="Italic">Kandelia candel</Emphasis>: Significance with respect to Lipid Input

Changes in the concentration of total lipid and fatty acids (FAs) during the decomposition of mangrove leaves were investigated by field experiments using yellow leaves of Bruguiera gymnorrhiza (L.) Lamk. and Kandelia candel (L.) Druce, in order to quantify mangrove contribution to lipid and fatty acid inputs to marine sediments. Total lipid and total FA in the fresh (green and yellow) and decomposing leaves of both species were significantly higher during winter than summer. During decomposition, total lipid content and FA concentration, in particular branched chain fatty acids (BrFAs) and bacterial fatty acids (BFAs), increased to a maximum concentration in 45 days during winter and in 17 days during summer. Lipids were lost faster in K. candel leaf detritus than in B. gymnorrhiza leaf detritus in which >90% of the total lipid original weight was lost during the summer experiment and <60% during the winter experiment. The changes in the concentrations of total lipids and FAs in the decomposing leaves also indicate that mangrove leaves are significant sources of fatty acids and probably other lipid compounds to estuarine ecosystems and that tidal waters transport the lipids and FAs adsorbed to particulate matter from mangroves to adjacent estuarine sediments and the ocean.     

Robust adaptive control of underactuated ships on a linear course with comfort

This paper addresses an important problem in ship control application—the robust stabilization of underactuated ships on a linear course with comfort. Specifically, we develop a multivariable controller to stabilize ocean surface ships without a sway actuator on a linear course and to reduce roll and pitch simultaneously. The controller adapts to unknown parameters of the ship and constant environmental disturbances induced by wave, ocean current and wind. It is also robust to time-varying environmental disturbances, time-varying change in ship parameters and other motions of the ship such as surge and heave. The roll and pitch can be made arbitrarily small while the heading angle and sway are kept to be in reasonably small bounds. The controller development is based on Lyapunov’s direct method and backstepping technique. A Lipschitz continuous projection algorithm is used to update the estimate of the unknown parameters to avoid the parameters’ drift due to time-varying environmental disturbances. Simulations on a full-scale catamaran illustrate the effectiveness of our proposed controller.