Accurate Pseudo Log Prediction Using Machine Learning Based Automatic Log Regularization and Feature Augmentation Method

Objectives/Scope: The performance of ML-based rock properties prediction from seismic with limited and sparse well data is very often inadequate. To address this limitation, we propose a novel automatic well log regularization (ALR) method with specially designed feature augmentation strategy to improve the prediction accuracy. The effectiveness of ALR method is showcased on field data in Malay basin where we successfully predict elastic logs with 30% higher accuracy, while using only 28% less training dataset. Methods, Procedures, Process: The ALR workflow (Figure 1): (1) feature selection and augmentation; (2) training and prediction and (3) prediction optimizations. The workflow starts with predicting any logs type which are available at training but not in blind wells using standard ML workflow for all blind wells (Step 1-2). Then, these intermediately predicted logs at blind well were jointly used as input features together with seismic-derived attributes using a specially designed feature augmentation strategy (Step 3). Finally, Step 1and 2 are then repeated to predict the elastic logs using these augmented input features. Results, Observations, Conclusions: The ALR method was applied on an oil/gas field data in Malay basin to predict elastic logs (AI and SI) at five blind wells from seismic data only and compared to the standard ML workflow. Two wells were used as training (28% of all data). The prediction performance of standard ML workflow (Figure 2a) is poor and can only capture general mean values of the actual AI/SI logs. The results of ALR workflow (Figure 2b) shows 30% better prediction performance compared to the standard ML workflow. In general, the background and high-resolution trend are well captured, and the overall prediction performance is improved using the new proposed prediction method. There are conceivably two explanations for this result: a) the background (low frequency) trend of the well log is properly reconstructed in ALR using only using seismic data. This could mainly lie in the ability of augmented features in better learning the uncertain reflection-reception relationship between seismic data and elastic logs, as well as the spatial/time-varying property of seismic data; (b) The ability to learn meaningful nonlinear feature relationship between input (feature) and output (label) variables with little or no supervision seems to work properly using specially designed feature augmentation. Novel/Additive Information: The ALR method is an ML-based pseudo log generation from seismic data using specially designed feature augmentation strategy. The novel ALR implementation relaxes the requirement of having a massive amount of high-quality labeled data for training and can therefore be applied in areas with limited well data information. ALR method is proven to be highly accurate for direct elastic logs prediction and can potentially be extended to estimate petrophysical properties from seismic data.

FLASH: F ast Neura l A rchitecture S earch with H ardware Optimization

Neural architecture search (NAS) is a promising technique to design efficient and high-performance deep neural networks (DNNs). As the performance requirements of ML applications grow continuously, the hardware accelerators start playing a central role in DNN design. This trend makes NAS even more complicated and time-consuming for most real applications. This paper proposes FLASH, a very fast NAS methodology that co-optimizes the DNN accuracy and performance on a real hardware platform. As the main theoretical contribution, we first propose the NN-Degree, an analytical metric to quantify the topological characteristics of DNNs with skip connections (e.g., DenseNets, ResNets, Wide-ResNets, and MobileNets). The newly proposed NN-Degree allows us to do training-free NAS within one second and build an accuracy predictor by training as few as 25 samples out of a vast search space with more than 63 billion configurations. Second, by performing inference on the target hardware, we fine-tune and validate our analytical models to estimate the latency, area, and energy consumption of various DNN architectures while executing standard ML datasets. Third, we construct a hierarchical algorithm based on simplicial homology global optimization (SHGO) to optimize the model-architecture co-design process, while considering the area, latency, and energy consumption of the target hardware. We demonstrate that, compared to the state-of-the-art NAS approaches, our proposed hierarchical SHGO-based algorithm enables more than four orders of magnitude speedup (specifically, the execution time of the proposed algorithm is about 0.1 seconds). Finally, our experimental evaluations show that FLASH is easily transferable to different hardware architectures, thus enabling us to do NAS on a Raspberry Pi-3B processor in less than 3 seconds.

Impact of IQ Imbalance on RIS-Assisted SISO Communication Systems

Reconfigurable intelligent surface (RIS) for wireless networks has emerged as a promising future transmission technique to create smart radio environments that improve the system performance by turning the wireless channel into an adjustable system block. However, transceivers come with various hardware impairments, such as phase noise and in-phase/quadrature-phase imbalance (IQI). Hence, for robust configuration of RIS-based communication under practical conditions, assuming the identical performance analysis when subject to IQI, will lead to inaccurate analysis. In this paper, the implementation of this novel transmission technique is thoroughly investigated under intensive realistic circumstances. For this purpose, based on the maximum likelihood (ML) detector, a novel analytical expression of average pairwise error probability under IQI is proposed and compared to the standard ML detector. Further, the proposed analytical approaches are confirmed by numerical simulations.

Using Machine Learning to reduce uncertainty in historical ocean temperature measurements

<p>Historical ocean temperature measurements are important in studying climate change due to the high proportion of heat absorbed by the ocean. These measurements come from a variety of sources, including Expendable Bathythermographs (XBTs), which are an important source of such data. Their measurements need bias corrections which are dependent on the type of XBT used, but poor metadata collection practices mean the type is often missing, increasing the measurement uncertainty and thus the uncertainty of the downstream dataset. </p><p> </p><p>This talk will describe efforts to fill in missing instrument type metadata using machine learning techniques so better bias corrections can be applied and the uncertainty in ocean temperature datasets reduced. I will describe the challenge arising from the nature of the dataset in applying standard ML techniques to the problem. I will also describe how we have used this project to explore the benefits of different platforms for ML and what open reproducible science looks like for Machine Learning projects.</p>

Real-time MLton: A Standard ML runtime for real-time functional programs

There is a growing interest in leveraging functional programming languages in real-time and embedded contexts. Functional languages are appealing as many are strictly typed, amenable to formal methods, have limited mutation, and have simple but powerful concurrency control mechanisms. Although there have been many recent proposals for specialized domain-specific languages for embedded and real-time systems, there has been relatively little progress on adapting more general purpose functional languages for programming embedded and real-time systems. In this paper, we present our current work on leveraging Standard ML (SML) in the embedded and real-time domains. Specifically, we detail our experiences in modifying MLton, a whole-program optimizing compiler for SML, for use in such contexts. We focus primarily on the language runtime, reworking the threading subsystem, object model, and garbage collector. We provide preliminary results over a radar-based aircraft collision detector ported to SML.

Integrating region memory management and tag-free generational garbage collection

We present a region-based memory management scheme with support for generational garbage collection. The scheme features a compile-time region inference algorithm, which associates values with logical regions, and builds on a region type system that deploys region types at runtime to avoid the overhead of write barriers and to support partly tag-free garbage collection. The scheme is implemented in the MLKit Standard ML compiler, which generates native x64 machine code. Besides demonstrating a number of important formal properties of the scheme, we measure the scheme’s characteristics, for a number of benchmarks, and compare the performance of the generated executables with the performance of executables generated with the MLton state-of-the-art Standard ML compiler and configurations of the MLKit with and without region inference and generational garbage collection enabled. Although region inference often serves the purpose of generations, combining region inference with generational garbage collection is shown often to be superior to combining region inference with non-generational collection despite the overhead introduced by a larger amount of memory waste, due to region fragmentation.

Towards IR4.0 implementation in e-manufacturing: artificial intelligence application in steel plate fault detection

<span>Fault detection is the task of discovering patterns of a certain fault in industrial manufacturing. Early detection of fault is an essential task in industrial manufacturing. Traditionally, faults are detected by human experts. However, this method suffers from cost and time. In this era of Industrial revolution IR 4.0, machine learning (ML) methods and techniques are developed to solve fault detection problem. In this study, three standard ML models: LR, NB, and SVM are developed for the classification problem. The experimental dataset used in this study consists of steel plates faults. The dataset is retrieved from UCI machine learning repository. Three standard evaluation methods: accuracy, precision, and recall are validated on the classification models. Logistic regression (LR) model achieved the highest accuracy and precision scores of 94.5% and 0.756 respectively. In addition, the SVM model had the highest recall score of 0.317. The results showed the significant impact of AI/ML approach in steel plates fault diagnosis problem. </span>