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. Here, we developed a Moho imaging scheme for the characteristics of fiber borehole strainmeters based on ambient noise autocorrelation. S-wave reflection signals were extracted from the ambient noise through a series of processing steps, including phase autocorrelation (PAC), phase-weighted stacking (PWS), etc. Subsequently, the time-to-depth conversion crustal thickness beneath the station was calculated. We applied our scheme to continuous four-component recordings from four fiber borehole strainmeters in Lu'an, Anhui Province, China. The obtained Moho depth was consistent with the previous research results. Our work shows that this method is suitable for Moho imaging with fiber borehole strainmeters without relying on the number of stations.

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. Studies eligible for inclusion were performed using appropriate methodology with complete verification by means of histopathology, intraoperative observation and/or follow-up, and sufficient information to permit determination of true-positive (TP), false-negative (FN), and false-positive (FP) values. Sources of bias were assessed using the QUADAS-2 tool. An inverse variance-weighted random-effects model was used to obtain sensitivity and PPV estimates. Information was analyzed and presented using Cochran's Q statistic, funnel plots, and modified Deeks' analysis. RESULTS: Ten articles (256 patients, 562 metastases) were included. Sensitivity estimates for pre-contrast (unenhanced) imaging, gadobenate-enhanced dynamic imaging, and combined unenhanced, dynamic, and delayed hepatobiliary phase imaging for detecting liver metastases on a per-lesion basis were 77.8% (95% CI 71.4-84.3%, 7 assessments), 88.1% (95% CI, 84.0-92.2%, 13 assessments), and 95.1% (95% CI 93.1-97.1%, 15 assessments), respectively. The addition of hepatobiliary phase images significantly improved the detection of liver metastases. The overall PPV was 90.9% (95% CI 86.6-95.1%, 11 assessments). Deeks' funnel analysis revealed no association between sample size and sensitivity (ß = 0.02, p = 0.814) indicating no significant publication bias. CONCLUSIONS: Gadobenate-enhanced MR imaging has high sensitivity and PPV for the detection of liver metastases on a per-lesion basis. The sensitivity and PPV for detection is comparable to reported values for the pure liver-specific agent gadoxetate. KEY POINTS: • Gadobenate dimeglumine is a hepatobiliary MR contrast agent that permits acquisition of contrast-enhanced liver images during the immediate post-injection dynamic phase, like any extracellular agent, and in the delayed hepatobiliary phase, after specific uptake by the hepatocytes. • The hepatobiliary phase improves detection of liver metastases when compared either to pre-contrast unenhanced images alone or to pre-contrast + gadobenate-enhanced dynamic phase images. • The meta-analysis showed an overall sensitivity of 95.1% and PPV of 90.9% of gadobenate-enhanced MRI for the detection of metastases, when based on the evaluation of all available acquisitions.

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. Synthetic data examples demonstrate that the frequency bandwidth of inversion result is almost the same for time, frequency and joint time-frequency domain inversion in seismic dominant frequency band using the noise-free data, but the frequency bandwidth in joint time-frequency domain is larger than that in time and frequency domains using low- signal-to-noise-ratio (SNR) data. The results of cross-correlation coefficients validate that the joint time-frequency-domain inversion retains both the excellent characteristics of high resolution in frequency-domain inversion and the advantage of strong anti-noise ability in time-domain inversion. A field data example further demonstrates that our proposed inversion approach in joint time-frequency domain may provide a more stable technique for fracture detection in fractured reservoirs.

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. Based on the improved MCMC method, a Bayesian amplitude versus offset (AVO) inversion method on the basis of the exact Zoeppritz equation has been developed, with which the P- and S-wave velocities and the density can be obtained directly, and the uncertainty of AVO inversion results has been estimated as well. The study based on the logging data and the seismic data demonstrates the feasibility and robustness of the method and shows that all three parameters are well retrieved. So the exact Zoeppritz-based nonlinear inversion method by using the improved MCMC is not only suitable for reservoirs with strong-contrast interfaces and long-offset ranges but also it is more stable, accurate and anti-noise.