Moho Imaging with Fiber Borehole Strainmeters Based on Ambient Noise Autocorrelation.

Moho tomography is important for studying the deep Earth structure and geodynamics, and fiber borehole strainmeters are broadband, low-noise, and attractive tools for seismic observation. Recently, many studies have shown that fiber optic seismic sensors can be used for subsurface structure imaging based on ambient noise cross-correlation, similar to conventional geophones. However, this array-dependent cross-correlation method is not suitable for fiber borehole strainmeters.

Fracture detection from Azimuth-dependent seismic inversion in joint time-frequency domain.

Detection of fracture properties can be implemented using azimuth-dependent seismic inversion for optimal model parameters in time or frequency domain. Considering the respective potentials for sensitivities of inversion resolution and anti-noise performance in time and frequency domain, we propose a more robust azimuth-dependent seismic inversion method to achieve fracture detection by combining the Bayesian inference and joint time-frequency-domain inversion theory. Both Cauchy Sparse and low-frequency constraint regularizations are introduced to reduce multi-solvability of model space and improve inversion reliability of model parameters.

Detection of liver metastases on gadobenate dimeglumine-enhanced MRI: systematic review, meta-analysis, and similarities with gadoxetate-enhanced MRI.

Objectives: To determine the sensitivity and positive predictive value (PPV) of gadobenate-enhanced MR imaging for the detection of liver metastases.

Methods: This systematic review and meta-analysis was conducted according to PRISMA guidelines. A comprehensive search (EMBASE, PubMed) was performed to identify relevant articles up to December 2017.

Zoeppritz-based AVO inversion using an improved Markov chain Monte Carlo method.

The conventional Markov chain Monte Carlo (MCMC) method is limited to the selected shape and size of proposal distribution and is not easy to start when the initial proposal distribution is far away from the target distribution. To overcome these drawbacks of the conventional MCMC method, two useful improvements in MCMC method, adaptive Metropolis (AM) algorithm and delayed rejection (DR) algorithm, are attempted to be combined. The AM algorithm aims at adapting the proposal distribution by using the generated estimators, and the DR algorithm aims at enhancing the efficiency of the improved MCMC method.