Earthquake Engineering

The book Earthquake Engineering - From Engineering Seismology to Optimal Seismic Design of Engineering Structures contains fifteen chapters written by researchers and experts in the fields of earthquake and structural engineering. This book provides the state-of-the-art on recent progress in the field of seimology, earthquake engineering and structural engineering. The book should be useful to graduate students, researchers and practicing structural engineers. It deals with seismicity, seismic hazard assessment and system oriented emergency response for abrupt earthquake disaster, the nature and the components of strong ground motions and several other interesting topics, such as dam-induced earthquakes, seismic stability of slopes and landslides. The book also tackles the dynamic response of underground pipes to blast loads, the optimal seismic design of RC multi-storey buildings, the finite-element analysis of cable-stayed bridges under strong ground motions and the acute psychiatric trauma intervention due to earthquakes

a feasibility study for the spatial reconstruction of conductivity distributions by means of sensitivities

To enhance interpretation capabilities of transient electromagnetic (TEM) methods, a multidimensional inverse solution is introduced, which allows for a explicit sensitivity calculation with reduced computational effort. The main conservation of computational load is obtained by solving Maxwell's equations directly in time domain. This is achieved by means of a high efficient Krylov-subspace technique that is particularly developed for the fast computation of EM fields in the diffusive regime. Traditional modeling procedures for Maxwell's equations yields solutions independently for every frequency or, in the time domain, at a given time through explicit time stepping. Because of this, frequency domain methods are rendered extremely time consuming for multi-frequency simulations. Likewise the stability conditions required by explicit time stepping techniques often result in highly inefficient calculations for large diffusion times and conductivity contrasts. The computation of sensitivities is carried out using the adjoint Green functions approach. For time domain applications, it is realized by convolution of the background electrical field information, originating from the primary signal, with the impulse response of the receiver acting as secondary source. In principle, the adjoint formulation may be extended allowing for a fast gradient calculation without calculating and storing the whole sensitivity matrix but just the gradient of the data residual. This technique, which is also known as migration, is widely used for seismic and, to some extend, for EM methods as well. However, the sensitivity matrix, which is not easily given by migration techniques, plays a central role in resolution analysis and would therefore be discarded ...thesi

Development and application of 2D and 3D transient electromagnetic inverse solutions based on adjoint Green functions: A feasibility study for the spatial reconstruction of conductivity distributions by means of sensitivities

To enhance interpretation capabilities of transient electromagnetic (TEM) methods, a multidimensional inverse solution is introduced, which allows for a explicit sensitivity calculation with reduced computational effort. The main conservation of computational load is obtained by solving Maxwell's equations directly in time domain. This is achieved by means of a high efficient Krylov-subspace technique that is particularly developed for the fast computation of EM fields in the diffusive regime. Traditional modeling procedures for Maxwell's equations yields solutions independently for every frequency or, in the time domain, at a given time through explicit time stepping. Because of this, frequency domain methods are rendered extremely time consuming for multi-frequency simulations. Likewise the stability conditions required by explicit time stepping techniques often result in highly inefficient calculations for large diffusion times and conductivity contrasts. The computation of sensitivities is carried out using the adjoint Green functions approach. For time domain applications, it is realized by convolution of the background electrical field information, originating from the primary signal, with the impulse response of the receiver acting as secondary source. In principle, the adjoint formulation may be extended allowing for a fast gradient calculation without calculating and storing the whole sensitivity matrix but just the gradient of the data residual. This technique, which is also known as migration, is widely used for seismic and, to some extend, for EM methods as well. However, the sensitivity matrix, which is not easily given by migration techniques, plays a central role in resolution analysis and would therefore be discarded. But, since it allows one to discriminate features in the a posteriori model which are data or regularization driven, it would therefore be very likely additional information to have. The additional cost of its storage and explicit computation is comparable low disbursement to the gain of a posteriori model resolution analysis. Inversion of TEM data arising from various types of sources is approached by two different methods. Both methods reconstruct the subsurface electrical conductivity properties directly in the time domain. A principal difference is given by the space dimensions of the inversion problems to be solved and the type of the optimization procedure. For two-dimensional (2D) models, the ill-posed and non-linear inverse problem is solved by means of a regularized Gauss-Newton type of optimization. For three-dimensional (3D) problems, due to the increase of complexity, a simpler, gradient based minimization scheme is presented. The 2D inversion is successfully applied to a long offset (LO)TEM survey conducted in the Arava basin (Jordan), where the joint interpretation of 168 transient soundings support the same subsurface conductivity structure as the one derived by inversion of a Magnetotelluric (MT) experiment. The 3D application to synthetic data demonstrates, that the spatial conductivity distribution can be reconstructed either by deep or shallow TEM sounding methods

Three-dimensional inversion of transient-electromagnetic data: A comparative study

Inversion of transient-electromagnetic (TEM) data arising from galvanic types of sources is approached by two different methods. Both methods reconstruct the subsurface three-dimensional (3D) electrical conductivity properties directly in the time-domain. A principal difference is given by the scale of the inversion problems to be solved. The first approach represents a small-scale 3D inversion and is based upon well-known tools. It uses a stabilized unconstrained least-squares inversion algorithm in combination with an existing 3D forward modeling solver and is customized to invert for 3D earth models with a limited model complexity. The limitation to only as many model unknowns as typical for classical least-squares problems involves arbitrary and rather unconventional types of model parameters. The inversion scheme has mainly been developed for the purpose of refining a priori known 3D underground structures by means of an inversion. Therefore, a priori information is an important requirement to design a model such that its limited degrees of freedom describe the structures of interest. The inversion is successfully applied to data from a long-offset TEM survey at the active volcano Merapi in Central Java (Indonesia). Despite the restriction of a low model complexity, the scheme offers some versatility as it can be adapted easily to various kinds of model structures. The interpretation of the resistivity images obtained by the inversion have substantially advanced the structural knowledge about the volcano. The second part of this work presents a theoretically more elaborate scheme. It employs imaging techniques originally developed for seismic wavefields. Large-scale 3D problems arising from the inversion for finely parameterized and arbitrarily complicated earth models are addressed by the method. The algorithm uses a conjugate-gradient search for the minimum of an error functional, where the gradient information is obtained via migration or backpropagation of the differences between the data observations and predictions back into the model in reverse time. Treatment for electric field and time derivative of the magnetic field data is given for the specification of the cost functional gradients. The inversion algorithm is successfully applied to a synthetic TEM data set over a conductive anomaly embedded in a half-space. The example involves a total number of more than 376000 model unknowns. The realization of migration techniques for diffusive EM fields involves the backpropagation of a residual field. The residual field excitation originates from the actual receiver positions and is continued during the simulated time range of the measurements. An explicit finite-difference time-stepping scheme is developed in advance of the imaging scheme in order to accomplish both the forward simulation and backpropagation of 3D EM fields. The solution uses a staggered grid and a modified version of the DuFort-Frankel stabilization method and is capable of simulating non-causal fields due to galvanic types of sources. Its parallel implementation allows for reasonable computation times, which are inherently high for explicit time-stepping schemes

COMBAT SYSTEMS Volume 1. Sensor Elements Part I. Sensor Functional Characteristics

This document includes: CHAPTER 1. SIGNATURES, OBSERVABLES, & PROPAGATORS. CHAPTER 2. PROPAGATION OF ELECTROMAGNETIC RADIATION. I. – FUNDAMENTAL EFFECTS. CHAPTER 3. PROPAGATION OF ELECTROMAGNETIC RADIATION. II. – WEATHER EFFECTS. CHAPTER 4. PROPAGATION OF ELECTROMAGNETIC RADIATION. III. – REFRACTIVE EFFECTS. CHAPTER 5. PROPAGATION OF ELECTROMAGNETIC RADIATION IV. – OTHER ATMOSPHERIC AND UNDERWATER EFFECTS. CHAPTER 6. PROPAGATION OF ACOUSTIC RADIATION. CHAPTER 7. NUCLEAR RADIATION: ITS ORIGIN AND PROPAGATION. CHAPTER 8. RADIOMETRY, PHOTOMETRY, & RADIOMETRIC ANALYSIS. CHAPTER 9. SENSOR FUNCTIONS. CHAPTER 10. SEARCH. CHAPTER 11. DETECTION. CHAPTER 12. ESTIMATION. CHAPTER 13. MODULATION AND DEMODULATION. CHAPTER 14. IMAGING AND IMAGE-BASED PERCEPTION. CHAPTER 15. TRACKING. APPENDIX A. UNITS, PHYSICAL CONSTANTS, AND USEFUL CONVERSION FACTORS. APPENDIX B. FINITE DIFFERENCE AND FINITE ELEMENT TECHNIQUES. APPENDIX C. PROBABILITY AND STATISTICS. INDEX TO VOLUME 1. Note by author: Note: Boldface entries in the table of contents are not yet completed