Mapping of crack edges by seismic methods

1982 ◽  
Vol 72 (3) ◽  
pp. 779-792
Author(s):  
J. D. Achenbach ◽  
A. Norris ◽  
K. Viswanathan
Keyword(s):  
Input Data ◽  
A Priori ◽  
Signal Propagation ◽  
Computational Procedure ◽  
Crack Edge ◽  
Inverse Mapping ◽  
Arrival Times ◽  
Ultrasonic Signals ◽  
Geometrical Theory Of Diffraction ◽  
Local Edge

abstract The inverse problem of diffraction of elastic waves by the edge of a large crack has been investigated on the basis of elastodynamic ray theory and the geometrical theory of diffraction. Two methods are discussed for the mapping of the edge of a crack-like flaw in an elastic medium. The methods require as input data the arrival times of diffracted ultrasonic signals. The first method maps flash points on the crack edge by a process of triangulation with the source and receiver as given vertices of the triangle. By the use of arrival times at neighboring positions of the source and/or the receiver, the directions of signal propagation, which determine the triangle, can be computed. This inverse mapping is global in the sense that no a priori knowledge of the location of the crack edge is necessary. The second method is a local edge mapping which determines planes relative to a known point close to the crack edge. Each plane contains a flash point. The envelope of the planes maps an approximation to the crack edge. The errors due to inaccuracies in the input data and in the computational procedure have been illustrated by specific examples.

A new approach for displacement and stress monitoring of tunnel based on iFEM methodology

2021 ◽  
Author(s):  
Pierclaudio Savino ◽  
Francesco Tondolo
Keyword(s):  
Input Data ◽  
Structural Response ◽  
Fem Analysis ◽  
Computational Procedure ◽  
Loading Conditions ◽  
Underground Structures ◽  
Stress Monitoring ◽  
Load Pattern ◽  
Structural Configurations ◽  
Structural Loading

Abstract Structural monitoring plays a key role for underground structures such as tunnels. Strain readings are expected to report structural conditions during construction and at the final delivery of the works. Furthermore, it is increasingly requested an extension to long-term monitoring from contractors with possible use of the same system in service during construction. A robust and efficient monitoring methodology from discrete strain measurements is the inverse Finite Element Method (iFEM), which allows to reconstruct the structural response without input data on the load pattern applied to the structure as well as material and inertial properties of the elements and therefore it is interesting for structural configurations affected by uncertain loading conditions, such as the tunnel. The formulation presented in this paper, based on the iFEM theory, is improved from the previous work available in literature for both the shape functions used and the computational procedure. Indeed, the approach allows to overcome inconsistencies related to structural loading conditions and a pseudo-inverse matrix preserve all the rigid body modes without imposing specific constraints which is typical for tunnels. Numerical validation of the iFEM procedure is performed by simulating the input data coming from a tunnel working in a heterogeneous soil under different loading conditions with direct FEM analysis.

COMPENSATION COMPETITIVE LEARNING

2001 ◽  
Vol 01 (03) ◽  
pp. 303-322
Author(s):  
ZHI-QIANG LIU ◽  
YAJUN ZHANG
Keyword(s):  
Input Data ◽  
Learning Algorithm ◽  
A Priori ◽  
Competitive Learning ◽  
Experimental Results ◽  
Initial Number ◽  
Number Of Clusters ◽  
And Performance ◽  
Competitive Algorithms

In general, in competitive learning the requirement for the initial number of prototypes is a difficult task, as we do not usually know the number of clusters in the input data a priori. The behavior and performance of the competitive algorithms are very sensitive to the initial locations and number of the prototypes. In this paper after investigating several important competitive learning paradigms, we present compensation techniques for overcoming the problems in competitive learning. Our experimental results show that competition with compensation can improve the performance of the learning algorithm.

Focused inversion of vertical radar profile (VRP) traveltime data

Geophysics ◽  
2012 ◽  
Vol 77 (1) ◽  
pp. H9-H18 ◽  
Author(s):  
Giulio Vignoli ◽  
Rita Deiana ◽  
Giorgio Cassiani
Keyword(s):  
Inverse Problem ◽  
Physical Properties ◽  
Reference Model ◽  
A Priori ◽  
S Wave ◽  
Compact Structure ◽  
Arrival Times ◽  
Minimum Support ◽  
Error Amplification ◽  
Zero Offset

The reconstruction of the GPR velocity vertical profile from vertical radar profile (VRP) traveltime data is a problem with a finite number of measurements and imprecise data, analogous to similar seismic techniques, such as the shallow down-hole test used for S-wave velocity profiling or the vertical seismic profiling (VSP) commonly used in deeper exploration. The uncertainty in data accuracy and the error amplification inherent in deriving velocity estimates from gradients of arrival times make this an example of an ill-posed inverse problem. In the framework of Tikhonov regularization theory, ill-posedness can be tackled by introducing a regularizing functional (stabilizer). The role of this functional is to stabilize the numerical solution by incorporating the appropriate a priori assumptions about the geometrical and/or physical properties of the solution. One of these assumptions could be the existence of sharp boundaries separating rocks with different physical properties. We apply a method based on the minimum support stabilizer to the VRP traveltime inverse problem. This stabilizer makes it possible to produce more accurate profiles of geological targets with compact structure. We compare more traditional inversion results with our proposed compact reconstructions. Using synthetic examples, we demonstrate that the minimum support stabilizer allows an improved recovery of the profile shape and velocity values of blocky targets. We also study the stabilizer behavior with respect to different noise levels and different choices of the reference model. The proposed approach is then applied to real cases where VPRs have been used to derive moisture content profiles as a function of depth. In these real cases, the derived sharper profiles are consistent with other evidence, such as GPR zero-offset profiles, GPR reflections and known locations of the water table.

Deep learning spectral enhancement considering features of seismic field data

Geophysics ◽  
2021 ◽  
pp. 1-60
Author(s):  
Yonggyu Choi ◽  
Yeonghwa Jo ◽  
Soon Jee Seol ◽  
Joongmoo Byun ◽  
Young Kim
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Input Data ◽  
Field Data ◽  
A Priori ◽  
Resolution Enhancement ◽  
Training Data ◽  
A Priori Information ◽  
Spectral Enhancement ◽  
Priori Information

The resolution of seismic data dictates the ability to identify individual features or details in a given image, and the temporal (vertical) resolution is a function of the frequency content of a signal. To improve thin-bed resolution, broadening of the frequency spectrum is required; this has been one of the major objectives in seismic data processing. Recently, many researchers have proposed machine learning based resolution enhancement and showed their applicability. However, since the performance of machine learning depends on what the model has learned, output from training data with features different from the target field data may be poor. Thus, we present a machine learning based spectral enhancement technique considering features of seismic field data. We used a convolutional U-Net model, which preserves the temporal connectivity and resolution of the input data, and generated numerous synthetic input traces and their corresponding spectrally broadened traces for training the model. A priori information from field data, such as the estimated source wavelet and reflectivity distribution, was considered when generating the input data for complementing the field features. Using synthetic tests and field post-stack seismic data examples, we showed that the trained model with a priori information outperforms the models trained without a priori information in terms of the accuracy of enhanced signals. In addition, our new spectral enhancing method was verified through the application to the high-cut filtered data and its promising features were presented through the comparison with well log data.

Blind Separation of Heart Sounds

2018 ◽  
Vol 26 (01) ◽  
pp. 1750035 ◽  
Author(s):  
Lingguang Chen ◽  
Sean F. Wu ◽  
Yong Xu ◽  
William D. Lyman ◽  
Gaurav Kapur
Keyword(s):  
Heart Valves ◽  
Heart Sound ◽  
A Priori ◽  
Source Separation ◽  
Propagation Speed ◽  
Signal Propagation ◽  
Heart Sounds ◽  
Blind Separation ◽  
Fluid Medium ◽  
First Heart Sound

This paper presents a theoretical foundation for the newly developed methodology that enables the prediction of blood pressures based on the heart sounds measured directly on the chest of a patient. The key to this methodology is the separation of heart sounds into first heart sound and second heart sound, from which components attributable to four heart valves, i.e.: mitral; tricuspid; aortic; and pulmonary valve-closure sounds are separated. Since human physiology and anatomy can vary among people and are unknown a priori, such separation is called blind source separation. Moreover, the sources locations, their surroundings and boundary conditions are unspecified. Consequently, it is not possible to obtain an exact separation of signals. To circumvent this difficulty, we extend the point source separation method in this paper to an inhomogeneous fluid medium, and further combine it with iteration schemes to search for approximate source locations and signal propagation speed. Once these are accomplished, the signals emitted from individual sources are separated by deconvoluting mixed signals with respect to the identified sources. Both numerical simulation example and experiment have demonstrated that this approach can provide satisfactory source separation results.

scholarly journals Matching Spectroscopy with the Ultrasonic Polar Scan for Advanced NDT of Composites

Proceedings ◽  
2018 ◽  
Vol 2 (8) ◽  
pp. 536 ◽  
Author(s):  
Erik Verboven ◽  
Mathias Kersemans ◽  
Arvid Martens ◽  
Jannes Daemen ◽  
Steven Delrue ◽  
...  
Keyword(s):  
A Priori ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Ultrasonic Signals ◽  
Reinforced Plastics ◽  
Stiffness Properties ◽  
Local Stiffness ◽  
Pulsed Ultrasonic ◽  
Ultrasonic Polar Scan

The Pulsed Ultrasonic Polar Scan (P-UPS) is a powerful technique for characterizing anisotropic materials like fiber reinforced plastics. A time-domain analysis of the ultrasonic signals yields amplitude and time-of-flight polar diagrams that provide a fingerprint of the local stiffness properties. Though, this simple analysis ignores a lot of information contained in the ultrasonic signals. In this study, we propose to use the P-UPS technique in combination with the spectroscopic analysis of broadband pulses, to obtain plane wave transmission spectra for all in-plane polar angles. This allows us to combine on one hand the strengths of the P-UPS technique, that does not require a priori knowledge about the sample anisotropy, and on the other hand the frequency-domain analysis that utilizes information contained in the broadband pulses.

scholarly journals Estimation of rock physics properties from seismic attributes — Part 1: Strategy and sensitivity analysis

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. M35-M53 ◽  
Author(s):  
Bastien Dupuy ◽  
Stéphane Garambois ◽  
Jean Virieux
Keyword(s):  
Input Data ◽  
Reservoir Characterization ◽  
Quantitative Estimation ◽  
A Priori ◽  
Rock Physics ◽  
Fluid Phase ◽  
Quality Factors ◽  
Simultaneous Inversion ◽  
Fluid Saturation ◽  
Wide Range

The quantitative estimation of rock physics properties is of great importance in any reservoir characterization. We have studied the sensitivity of such poroelastic rock physics properties to various seismic viscoelastic attributes (velocities, quality factors, and density). Because we considered a generalized dynamic poroelastic model, our analysis was applicable to most kinds of rocks over a wide range of frequencies. The viscoelastic attributes computed by poroelastic forward modeling were used as input to a semiglobal optimization inversion code to estimate poroelastic properties (porosity, solid frame moduli, fluid phase properties, and saturation). The sensitivity studies that we used showed that it was best to consider an inversion system with enough input data to obtain accurate estimates. However, simultaneous inversion for the whole set of poroelastic parameters was problematic due to the large number of parameters and their trade-off. Consequently, we restricted the sensitivity tests to the estimation of specific poroelastic parameters by making appropriate assumptions on the fluid content and/or solid phases. Realistic a priori assumptions were made by using well data or regional geology knowledge. We found that (1) the estimation of frame properties was accurate as long as sufficient input data were available, (2) the estimation of permeability or fluid saturation depended strongly on the use of attenuation data, and (3) the fluid bulk modulus can be accurately inverted, whereas other fluid properties have a low sensitivity. Introducing errors in a priori rock physics properties linearly shifted the estimations, but not dramatically. Finally, an uncertainty analysis on seismic input data determined that, even if the inversion was reliable, the addition of more input data may be required to obtain accurate estimations if input data were erroneous.

scholarly journals Convergence analysis of subdivision processes on the sphere

2020 ◽  
Author(s):  
Svenja Hüning ◽  
Johannes Wallner
Keyword(s):  
Convergence Analysis ◽  
Input Data ◽  
Positive Curvature ◽  
A Priori ◽  
First Time ◽  
Subdivision Rules

Abstract We analyse the convergence of nonlinear Riemannian analogues of linear subdivision processes operating on data in the sphere. We show how for curve subdivision rules we can derive bounds guaranteeing convergence if the density of input data is below that threshold. Previous results only yield thresholds that are several magnitudes smaller and are thus useless for a priori checking of convergence. It is the first time that such a result has been shown for a geometry with positive curvature and for subdivision rules not enjoying any special properties like being interpolatory or having non-negative mask.

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