Manufactured caverns in carbonate rock

Disclosed is a process for manufacturing underground caverns suitable in one embodiment for storage of large volumes of gaseous or liquid materials. The method is an acid dissolution process that can be utilized to form caverns in carbonate rock formations. The caverns can be used to store large quantities of materials near transportation facilities or destination markets. The caverns can be used for storage of materials including fossil fuels, such as natural gas, refined products formed from fossil fuels, or waste materials, such as hazardous waste materials. The caverns can also be utilized for applications involving human access such as recreation or research. The method can also be utilized to form calcium chloride as a by-product of the cavern formation process

Small drill-hole, gas mini-permeameter probe

The distal end of a basic tube element including a stopper device with an expandable plug is positioned in a pre-drilled hole in a rock face. Rotating a force control wheel threaded on the tube element exerts force on a sleeve that in turn causes the plug component of the stopper means to expand and seal the distal end of the tube in the hole. Gas under known pressure is introduced through the tube element. A thin capillary tube positioned in the tube element connects the distal end of the tube element to means to detect and display pressure changes and data that allow the permeability of the rock to be determined

Interpretable and Steerable Sequence Learning via Prototypes

One of the major challenges in machine learning nowadays is to provide predictions with not only high accuracy but also user-friendly explanations. Although in recent years we have witnessed increasingly popular use of deep neural networks for sequence modeling, it is still challenging to explain the rationales behind the model outputs, which is essential for building trust and supporting the domain experts to validate, critique and refine the model. We propose ProSeNet, an interpretable and steerable deep sequence model with natural explanations derived from case-based reasoning. The prediction is obtained by comparing the inputs to a few prototypes, which are exemplar cases in the problem domain. For better interpretability, we define several criteria for constructing the prototypes, including simplicity, diversity, and sparsity and propose the learning objective and the optimization procedure. ProSeNet also provides a user-friendly approach to model steering: domain experts without any knowledge on the underlying model or parameters can easily incorporate their intuition and experience by manually refining the prototypes. We conduct experiments on a wide range of real-world applications, including predictive diagnostics for automobiles, ECG, and protein sequence classification and sentiment analysis on texts. The result shows that ProSeNet can achieve accuracy on par with state-of-the-art deep learning models. We also evaluate the interpretability of the results with concrete case studies. Finally, through user study on Amazon Mechanical Turk (MTurk), we demonstrate that the model selects high-quality prototypes which align well with human knowledge and can be interactively refined for better interpretability without loss of performance.Comment: Accepted as a full paper at KDD 2019 on May 8, 201

Definitions, methods, and applications in interpretable machine learning.

Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the predictive, descriptive, relevant (PDR) framework for discussing interpretations. The PDR framework provides 3 overarching desiderata for evaluation: predictive accuracy, descriptive accuracy, and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post hoc categories, with subgroups including sparsity, modularity, and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often underappreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods

Seismic Coupling of Short-Period Wind Noise Through Mars’ Regolith for NASA’s InSight Lander

NASA’s InSight lander will deploy a tripod-mounted seismometer package onto the surface of Mars in late 2018. Mars is expected to have lower seismic activity than the Earth, so minimisation of environmental seismic noise will be critical for maximising observations of seismicity and scientific return from the mission. Therefore, the seismometers will be protected by a Wind and Thermal Shield (WTS), also mounted on a tripod. Nevertheless, wind impinging on the WTS will cause vibration noise, which will be transmitted to the seismometers through the regolith (soil). Here we use a 1:1-scale model of the seismometer and WTS, combined with field testing at two analogue sites in Iceland, to determine the transfer coefficient between the two tripods and quantify the proportion of WTS vibration noise transmitted through the regolith to the seismometers. The analogue sites had median grain sizes in the range 0.3–1.0 mm, surface densities of 1.3–1.8gcm−3, and an effective regolith Young’s modulus of 2.5−1.4+1.9MPa. At a seismic frequency of 5 Hz the measured transfer coefficients had values of 0.02–0.04 for the vertical component and 0.01–0.02 for the horizontal component. These values are 3–6 times lower than predicted by elastic theory and imply that at short periods the regolith displays significant anelastic behaviour. This will result in reduced short-period wind noise and increased signal-to-noise. We predict the noise induced by turbulent aerodynamic lift on the WTS at 5 Hz to be ∼2×10−10ms−2Hz−1/2 with a factor of 10 uncertainty. This is at least an order of magnitude lower than the InSight short-period seismometer noise floor of 10−8ms−2Hz−1/2