• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.1-8
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• 2010

Seismic reflection surveying is one of the most widely used and effective techniques for coal seam structure delineation and risk mitigation for underground longwall mining. However, the ability of the method can be compromised by the presence of volcanic cover. This problem arises within parts of the Bowen and Sydney Basins of Australia and seismic surveying can be unsuccessful. As a consequence, such areas are less attractive for coal mining. Techniques to improve the success of seismic surveying over basalt flows are needed. In this paper, we use elastic wave-equation-based forward modelling techniques to investigate the effects and characteristics of seismic wave propagation under different settings involving changes in basalt properties, its thickness, lateral extent, relative position to the shot position and various forms of inhomogeneity. The modelling results suggests that: 1) basalts with high impedance contrasts and multiple flows generate strong multiples and weak reflectors; 2) thin basalts have less effect than thick basalts; 3) partial basalt cover has less effect than full basalt cover; 4) low frequency seismic waves (especially at large offsets) have better penetration through the basalt than high frequency waves; and 5) the deeper the coal seams are below basalts of limited extent, the less influence the basalts will have on the wave propagation. In addition to providing insights into the issues that arise when seismic surveying under basalts, these observations suggest that careful management of seismic noise and the acquisition of long-offset seismic data with low-frequency geophones have the potential to improve the seismic results.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.518-528
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• 2014

In this paper, we propose a method to recognize the action direction of human by developing 4D space-time (4D-ST, [x,y,z,t]) features. For this, we propose 4D space-time interest points (4D-STIPs, [x,y,z,t]) which are extracted using 3D space (3D-S, [x,y,z]) volumes reconstructed from images of a finite number of different views. Since the proposed features are constructed using volumetric information, the features for arbitrary 2D space (2D-S, [x,y]) viewpoint can be generated by projecting the 3D-S volumes and 4D-STIPs on corresponding image planes in training step. We can recognize the directions of actors in the test video since our training sets, which are projections of 3D-S volumes and 4D-STIPs to various image planes, contain the direction information. The process for recognizing action direction is divided into two steps, firstly we recognize the class of actions and then recognize the action direction using direction information. For the action and direction of action recognition, with the projected 3D-S volumes and 4D-STIPs we construct motion history images (MHIs) and non-motion history images (NMHIs) which encode the moving and non-moving parts of an action respectively. For the action recognition, features are trained by support vector data description (SVDD) according to the action class and recognized by support vector domain density description (SVDDD). For the action direction recognition after recognizing actions, each actions are trained using SVDD according to the direction class and then recognized by SVDDD. In experiments, we train the models using 3D-S volumes from INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset and recognize action direction by constructing a new SNU dataset made for evaluating the action direction recognition.