Wave-equation based seismic multiple attenuation

Reflection seismology is widely used to map the subsurface geological structure of the Earth. Seismic multiples can contaminate seismic data and are therefore due to be removed. For seismic multiple attenuation, wave-equation based methods are proved to be effective in most cases, which involve two aspects: multiple prediction and multiple subtraction. Targets of both aspects are to develop and apply a fully datadriven algorithm for multiple prediction, and a robust technique for multiple subtraction. Based on many schemes developed by others regarding to the targets, this thesis addresses and tackles the problems of wave-equation based seismic multiple attenuation by several approaches. First, the issue of multiple attenuation in land seismic data is discussed. Multiple Prediction through Inversion (MPTI) method is expanded to be applied in the poststack domain and in the CMP domain to handle the land data with low S/N ratio, irregular geometry and missing traces. A running smooth filter and an adaptive threshold K-NN (nearest neighbours) filter are proposed to help to employ MPTI on land data in the shot domain. Secondly, the result of multiple attenuation depends much upon the effectiveness of the adaptive subtraction. The expanded multi-channel matching (EMCM) filter is proved to be effective. In this thesis, several strategies are discussed to improve the result of EMCM. Among them, to model and subtract the multiples according to their orders is proved to be practical in enhancing the effect of EMCM, and a masking filter is adopted to preserve the energy of primaries. Moreover, an iterative application of EMCM is proposed to give the optimized result. Thirdly, with the limitation of current 3D seismic acquisition geometries, the sampling in the crossline direction is sparse. This seriously affects the application of the 3D multiple attenuation. To tackle the problem, a new approach which applies a trajectory stacking Radon transform along with the energy spectrum is proposed in this thesis. It can replace the time-consuming time-domain sparse inversion with similar effectiveness and much higher efficiency. Parallel computing is discussed in the thesis so as to enhance the efficiency of the strategies. The Message-Passing Interface (MPI) environment is implemented in most of the algorithms mentioned above and greatly improves the efficiency

Wave-equation based seismic multiple attenuation

Reflection seismology is widely used to map the subsurface geological structure of the Earth. Seismic multiples can contaminate seismic data and are therefore due to be removed. For seismic multiple attenuation, wave-equation based methods are proved to be effective in most cases, which involve two aspects: multiple prediction and multiple subtraction. Targets of both aspects are to develop and apply a fully datadriven algorithm for multiple prediction, and a robust technique for multiple subtraction. Based on many schemes developed by others regarding to the targets, this thesis addresses and tackles the problems of wave-equation based seismic multiple attenuation by several approaches. First, the issue of multiple attenuation in land seismic data is discussed. Multiple Prediction through Inversion (MPTI) method is expanded to be applied in the poststack domain and in the CMP domain to handle the land data with low S/N ratio, irregular geometry and missing traces. A running smooth filter and an adaptive threshold K-NN (nearest neighbours) filter are proposed to help to employ MPTI on land data in the shot domain. Secondly, the result of multiple attenuation depends much upon the effectiveness of the adaptive subtraction. The expanded multi-channel matching (EMCM) filter is proved to be effective. In this thesis, several strategies are discussed to improve the result of EMCM. Among them, to model and subtract the multiples according to their orders is proved to be practical in enhancing the effect of EMCM, and a masking filter is adopted to preserve the energy of primaries. Moreover, an iterative application of EMCM is proposed to give the optimized result. Thirdly, with the limitation of current 3D seismic acquisition geometries, the sampling in the crossline direction is sparse. This seriously affects the application of the 3D multiple attenuation. To tackle the problem, a new approach which applies a trajectory stacking Radon transform along with the energy spectrum is proposed in this thesis. It can replace the time-consuming time-domain sparse inversion with similar effectiveness and much higher efficiency. Parallel computing is discussed in the thesis so as to enhance the efficiency of the strategies. The Message-Passing Interface (MPI) environment is implemented in most of the algorithms mentioned above and greatly improves the efficiency.EThOS - Electronic Theses Online ServiceDHPA scholarship and Centre for Reservoir GeophysicsGBUnited Kingdo

Spatial–temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram

Functional connectivity of the human brain, representing statistical dependence of information flow between cortical regions, significantly contributes to the study of the intrinsic brain network and its functional mechanism. To fully explore its potential in the early diagnosis of Alzheimer's disease (AD) using electroencephalogram (EEG) recordings, this article introduces a novel dynamical spatial–temporal graph convolutional neural network (ST-GCN) for better classification performance. Different from existing studies that are based on either topological brain function characteristics or temporal features of EEG, the proposed ST-GCN considers both the adjacency matrix of functional connectivity from multiple EEG channels and corresponding dynamics of signal EEG channel simultaneously. Different from the traditional graph convolutional neural networks, the proposed ST-GCN makes full use of the constrained spatial topology of functional connectivity and the discriminative dynamic temporal information represented by the 1D convolution. We conducted extensive experiments on the clinical EEG data set of AD patients and Healthy Controls. The results demonstrate that the proposed method achieves better classification performance (92.3%) than the state-of-the-art methods. This approach can not only help diagnose AD but also better understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on resting-state EEG

A Revised Hilbert-Huang Transformation to Track Non-Stationary Association of Electroencephalography Signals

The time-varying cross-spectrum method has been used to effectively study transient and dynamic brain functional connectivity between non-stationary electroencephalography (EEG) signals. Wavelet-based cross-spectrum is one of the most widely implemented methods, but it is limited by the spectral leakage caused by the finite length of the basic function that impacts the time and frequency resolutions. This paper proposes a new time-frequency brain functional connectivity analysis framework to track the non-stationary association of two EEG signals based on a Revised Hilbert-Huang Transform (RHHT). The framework can estimate the cross-spectrum of decomposed components of EEG, followed by a surrogate significance test. The results of two simulation examples demonstrate that, within a certain statistical confidence level, the proposed framework outperforms the wavelet-based method in terms of accuracy and time-frequency resolution. A case study on classifying epileptic patients and healthy controls using interictal seizure-free EEG data is also presented. The result suggests that the proposed method has the potential to better differentiate these two groups benefiting from the enhanced measure of dynamic time-frequency association.Not heldAccepted versio