Temperature and Salinity Inverted for a Mediterranean Eddy Captured With Seismic Data, Using a Spatially Iterative Markov Chain Monte Carlo Approach

Ocean submesoscale dynamics are thought to play a key role in both the climate system and ocean productivity, however, subsurface observations at these scales remain rare. Seismic oceanography, an established acoustic imaging method, provides a unique tool for capturing oceanic structure throughout the water column with spatial resolutions of tens of meters. A drawback to the seismic method is that temperature and salinity are not measured directly, limiting the quantitative interpretation of imaged features. The Markov Chain Monte Carlo (MCMC) inversion approach has been used to invert for temperature and salinity from seismic data, with spatially quantified uncertainties. However, the requisite prior model used in previous studies relied upon highly continuous acoustic reflection horizons rarely present in real oceanic environments due to instabilities and turbulence. Here we adapt the MCMC inversion approach with an iteratively updated prior model based on hydrographic data, sidestepping the necessity of continuous reflection horizons. Furthermore, uncertainties introduced by the starting model thermohaline fields as well as those from the MCMC inversion itself are accounted for. The impact on uncertainties of varying the resolution of hydrographic data used to produce the inversion starting model is also investigated. The inversion is applied to a mid-depth Mediterranean water eddy (or meddy) captured with seismic imaging in the Gulf of Cadiz in 2007. The meddy boundary exhibits regions of disrupted seismic reflectivity and rapid horizontal changes of temperature and salinity. Inverted temperature and salinity values typically have uncertainties of 0.16°C and 0.055 psu, respectively, and agree well with direct measurements. Uncertainties of inverted results are found to be highly dependent on the resolution of the hydrographic data used to produce the prior model, particularly in regions where background temperature and salinity vary rapidly, such as at the edge of the meddy. This further advancement of inversion techniques to extract temperature and salinity from seismic data will help expand the use of ocean acoustics for understanding the mesoscale to finescale structure of the interior ocean.

Hybridizing Physical and Data-driven Prediction Methods for Physicochemical Properties

We present a generic way to hybridize physical and data-driven methods for predicting physicochemical properties. The approach ‘distills’ the physical method's predictions into a prior model and combines it with sparse experimental data using Bayesian inference. We apply the new approach to predict activity coefficients at infinite dilution and obtain significant improvements compared to the physical and data-driven baselines and established ensemble methods from the machine learning literature.

Improving the Robustness of the MTI-Estimated Mining-Induced 3D Time-Series Displacements with a Logistic Model

The previous multi-track InSAR (MTI) method can be used to retrieve mining-induced three-dimensional (3D) surface displacements with high spatial–temporal resolution by incorporating multi-track interferometric synthetic aperture radar (InSAR) observations with a prior model. However, due to the track-by-track strategy used in the previous MTI method, no redundant observations are provided to estimate 3D displacements, causing poor robustness and further degrading the accuracy of the 3D displacement estimation. This study presents an improved MTI method to significantly improve the robustness of the 3D mining displacements derived by the previous MTI method. In this new method, a fused-track strategy, instead of the previous track-by-track one, is proposed to process the multi-track InSAR measurements by introducing a logistic model. In doing so, redundant observations are generated and further incorporated into the prior model to solve 3D displacements. The improved MTI method was tested on the Datong coal mining area, China, with Sentinel-1 InSAR datasets from three tracks. The results show that the 3D mining displacements estimated by the improved MTI method had the same spatial–temporal resolution as those estimated by the previous MTI method and about 33.5% better accuracy. The more accurate 3D displacements retrieved from the improved MTI method can offer better data for scientifically understanding the mechanism of mining deformation and assessing mining-related geohazards.

Modelling sound wave propagation through corrugated macro-geometry arrangement of porous material for combined heat sink and noise reduction applications

In convective air-cooled heat sink applications with space constraints, corrugated geometries can be used as in-duct sound absorbing structures offering lower duct-flow resistance than other geometries such as block-shape, wedge-shape geometries. Sound wave propagation through this geometry is presented using a simple 1-D acoustic model. Using the model, acoustic performance of corrugated sample is evaluated in terms of its transmission loss in dB. Thermal resistance and pressure drop values are also reported and compared with acoustic performance as function of number of corrugations and length of corrugated sample. A rectangular corrugated geometry has alternate inlet and outlet channels separated by porous walls. Sound propagation across this arrangement is modelled by extending prior model from literature with similar geometries. Prior model by Allam and Åbom (2005) is highly symmetric about the channels and porous walls are modelled by simple steady flow resistance equation. In current work, appropriate considerations are taken into account for the configuration of corrugated geometries suitable to general heat sink applications and sound wave propagation through porous walls is predicted by using Johnson-Champoux-Allard (jca) model. The porous walls at ends of the geometry are modelled as in acoustically series-parallel network combinations. Further, effect of heat sink temperature on sound wave propagation is also explored using the model.

Prediction Method of Bearing RUL Based on DNN and GBDT Algorithm

In view of the lack of effective model and the large prediction error in the traditional prediction methods, a collaborative prediction method for remaining useful life of bearing based on DNN and GBDT is proposed. Firstly, the degradation characteristics are constructed through normalization processing of parameters in time domain and frequency domain that can clearly represent the healthy running state of bearings, in order to improve the correlation of degradation characteristics, the prior model features are generated based on DNN. Secondly, a regression model of GBDT based on the prior model features is presented. Finally, the experimental results show that compared with other algorithms such as DNN, GBDT, SVR, RF, DT, the proposed method has better prediction performance evaluation results, higher prediction accuracy and efficiency compared with other algorithms.