Joint AVO inversion in the time and frequency domain with Bayesian interference

Amplitude variations with offset or incident angle （AVO/AVA） inversion are typically combined with statistical methods, such as Bayesian inference or deterministic inversion. We propose a joint elastic inversion method in the time and frequency domain based on Bayesian inversion theory to improve the resolution of the estimated P- and S-wave velocities and density. We initially construct the objective function using Bayesian inference by combining seismic data in the time and frequency domain. We use Cauchy and Gaussian probability distribution density functions to obtain the prior information for the model parameters and the likelihood function, respectively. We estimate the elastic parameters by solving the initial objective function with added model constraints to improve the inversion robustness. The results of the synthetic data suggest that the frequency spectra of the estimated parameters are wider than those obtained with conventional elastic inversion in the time domain. In addition, the proposed inversion approach offers stronger antinoising compared to the inversion approach in the frequency domain. Furthermore, results from synthetic examples with added Gaussian noise demonstrate the robustness of the proposed approach. From the real data, we infer that more model parameter details can be reproduced with the proposed joint elastic inversion.

Source-Independent Amplitude-Semblance Full-Waveform Inversion using a Hybrid Time-and Frequency-Domain Approach

Full-waveform inversion is a promising tool to produce accurate and high-resolution subsurface models.Conventional full-waveform inversion requires an accu-rate estimation of the source wavelet,and its computational cost is high.We develop a novel source-independent full-waveform inversion method using a hybrid time-and frequency-domain scheme to avoid the requirement of source wavelet estimation and to reduce the computational cost.We employ an amplitude-semblance objective function to not only effectively remove the source wavelet effect on full-waveform inver-sion,but also to eliminate the impact of the inconsistency of source wavelets among different shot gathers on full-waveform inversion.To reduce the high computational cost of full-waveform inversion in the time domain,we implement our new algorithm using a hybrid time-and frequency-domain approach.The forward and backward wave propagation operations are conducted in the time domain,while the frequency-domain wavefields are obtained during modeling using the discrete-time Fourier trans-form.The inversion process is conducted in the frequency domain for selected frequen-cies.We verify our method using synthetic seismic data for the Marmousi model.The results demonstrate that our novel source-independent full-waveform inversion pro-duces accurate velocity models even if the source signature is incorrect.In addition,our method can significantly reduce the computational time using the hybrid time-and frequency-domain approach compared to the conventional full-waveform inversion in the time domain.

Review of Photoacoustic Malaria Diagnostic Techniques

Malaria is one of the leading causes of mortality and morbidity in developing countries. Accurate and complete diagnosis is key for effective treatment of the disease. However, mainstream malaria diagnostic techniques suffer from a number of shortcomings. There is therefore an urgent need for development of new and more efficient techniques for malaria diagnosis. In vivo Photoacoustic spectroscopy is an emerging technique, which has great potential of delivering a nearly ideal method for early diagnosis of the disease. The technique promises to be highly sensitive and specific. In this paper, a description of photoacoustic malaria sensing is given. This is followed by a review of photoacoustic-based malaria diagnostic techniques and suggestions for future improvements.

A blind separation method of overlapped multi-components based on time varying AR model

A method utilizing single channel recordings to blindly separate the multicomponents overlapped in time and frequency domains is proposed in this paper. Based on the time varying AR model, the instantaneous frequency and amplitude of each signal component are estimated respectively, thus the signal component separation is achieved. By using prolate spheroidal sequence as basis functions to expand the time varying parameters of the AR model, the method turns the problem of linear time varying parameters estimation to a linear time invariant parameter estimation problem, then the parameters are estimated by a recursive algorithm. The computation of this method is simple, and no prior knowledge of the signals is needed. Simulation results demonstrate validity and excellent performance of this method.