Xingmiao Yao

Enhanced coherence using principal component analysis

AbstractCoherence is a measure of similarity between seismic waveforms. It gives a quantitative description of lateral reflection changes and highlights variations of the geologic features within a seismic image. However, subtle changes in waveforms are often difficult to capture using traditional coherence measures because of the high similarity among the remaining parts in the vertical analysis window. We have developed an attribute called enhanced coherence based on principal component analysis (PCA) with the goal of reducing redundancy within the vertical analysis window, which is often composed of the parts with a high similarity between neighboring traces, and highlighting subtle lateral changes. In computing such a coherence image, we first extract seismic data within a specified time window along a picked horizon. Then, we calculate the enhanced coherence from reduced data obtained using a dimension-reduction technique. Because seismic data typically consist of large volumes, PCA is chosen for dim...

A method of reconstructing complex stratigraphic surfaces with multitype fault constraints

The construction of complex stratigraphic surfaces is widely employed in many fields, such as petroleum exploration, geological modeling, and geological structure analysis. It also serves as an important foundation for data visualization and visual analysis in these fields. The existing surface construction methods have several deficiencies and face various difficulties, such as the presence of multitype faults and roughness of resulting surfaces. In this paper, a surface modeling method that uses geometric partial differential equations (PDEs) is introduced for the construction of stratigraphic surfaces. It effectively solves the problem of surface roughness caused by the irregularity of stratigraphic data distribution. To cope with the presence of multitype complex faults, a two-way projection algorithm between threedimensional space and a two-dimensional plane is proposed. Using this algorithm, a unified method based on geometric PDEs is developed for dealing with multitype faults. Moreover, the corresponding geometric PDE is derived, and an algorithm based on an evolutionary solution is developed. The algorithm proposed for constructing spatial surfaces with real data verifies its computational efficiency and its ability to handle irregular data distribution. In particular, it can reconstruct faulty surfaces, especially those with overthrust faults.

3D modeling of geological anomalies based on segmentation of multiattribute fusion

Abstract3D modeling of geological bodies based on 3D seismic data is used to define the shape and volume of the bodies, which then can be directly applied to reservoir prediction, reserve estimation, and exploration. However, multiattributes are not effectively used in 3D modeling. To solve this problem, we propose a novel method for building of 3D model of geological anomalies based on the segmentation of multiattribute fusion. First, we divide the seismic attributes into edge- and region-based seismic attributes. Then, the segmentation model incorporating the edge- and region-based models is constructed within the levelset-based framework. Finally, the marching cubes algorithm is adopted to extract the zero level set based on the segmentation results and build the 3D model of the geological anomaly. Combining the edge-and region-based attributes to build the segmentation model, we satisfy the independence requirement and avoid the problem of insufficient data of single seismic attribute in capturing the boundaries of geological anomalies. We apply the proposed method to seismic data from the Sichuan Basin in southwestern China and obtain 3D models of caves and channels. Compared with 3D models obtained based on single seismic attributes, the results are better agreement with reality.

Unicast Network Loss Tomography Using #R-Cast Probes Scheme

Network tomography can not rely on the cooperation internal node, and use only a group of edge nodes to reveal the mathematical and statistical characterization of network behavior that is wished to be known. Therefore, network tomography has become one of the focused new technologies. Existing unicast network loss tomography need send large numbers of probe packet, which is lead to add network load. In the paper, we propose unicast network loss tomography using #R-cast probes scheme with two stage mechanism. Here R denotes leaf set (receivers nodes) of an arbitrary tree, and # is the number of its set. In my study, to eliminate the redundancy probe packet, we send first #R-cast probes to all receivers so that all paths are covered, simultaneously. Then, we exact different flexicast from the #R-cast received by leaf nodes, for all the internal link loss rates identifiability. Our method hence can send much lesser probe than existing method. Finally, we employ fast least-squares algorithm to obtain the solution of our overdetermined equations of my loss tomography. Ns2 simulation experiments demonstrate the performance of the propose approach.

Recurrent neural network inference of internal delays in nonstationary data network

By applying tomography theory which is highly developed in fieldssuchas medical computerized tomography and seismic tomography to communication network, network tomography has become one of the focused new technologies, which can infer the internal performance of the network by external end-to-end measurement. In this paper, we propose a novel Inference algorithm based on the recurrent multilayer perceptron (RMLP) network capable of tracking nonstationary network behavior and estimating time-varying, internal delay characteristics. Simulation experiments demonstrate the performance of the RMLP network.