Seismic fluid identification using a nonlinear elastic impedance inversion method based on a fast Markov chain Monte Carlo method

Elastic impedance inversion with high efficiency and high stability has become one of the main directions of seismic pre-stack inversion. The nonlinear elastic impedance inversion method based on a fast Markov chain Monte Carlo （MCMC） method is proposed in this paper, combining conventional MCMC method based on global optimization with a preconditioned conjugate gradient （PCG） algorithm based on local optimization, so this method does not depend strongly on the initial model. It converges to the global optimum quickly and efficiently on the condition that effi- ciency and stability of inversion are both taken into consid- eration at the same time. The test data verify the feasibility and robustness of the method, and based on this method, we extract the effective pore-fluid bulk modulus, which is applied to reservoir fluid identification and detection, and consequently, a better result has been achieved.

LEARN algorithm:a novel option for predicting non-alcoholic steatohepatitis

Background:There is an unmet need for accurate non-invasive methods to diagnose non-alcoholic steatohepatitis(NASH).Since impedance-based measurements of body composition are simple,repeatable and have a strong association with non-alcoholic fatty liver disease(NAFLD)severity,we aimed to develop a novel and fully automatic machine learning algorithm,consisting of a deep neural network based on impedance-based measurements of body composition to identify NASH[the bioeLectrical impEdance Analysis foR Nash(LEARN)algorithm].Methods:A total of 1,259 consecutive subjects with suspected NAFLD were screened from six medical centers across China,of which 766 patients with biopsy-proven NAFLD were included in final analysis.These patients were randomly subdivided into the training and validation groups,in a ratio of 4:1.The LEARN algorithm was developed in the training group to identify NASH,and subsequently,tested in the validation group.Results:The LEARN algorithm utilizing impedance-based measurements of body composition along with age,sex,pre-existing hypertension and diabetes,was able to predict the likelihood of having NASH.This algorithm showed good discriminatory ability for identifying NASH in both the training and validation groups[area under the receiver operating characteristics(AUROC):0.81,95%CI:0.77-0.84 and AUROC:0.80,95%CI:0.73-0.87,respectively].This algorithm also performed better than serum cytokeratin-18 neoepitope M30(CK-18 M30)level or other non-invasive NASH scores(including HAIR,ION,NICE)for identifying NASH(P value<0.001).Additionally,the LEARN algorithm performed well in identifying NASH in different patient subgroups,as well as in subjects with partial missing body composition data.Conclusions:The LEARN algorithm,utilizing simple easily obtained measures,provides a fully automated,simple,non-invasive method for identifying NASH.