Application Of Gravity Data For Hydrocarbon Exploration Using Machine Learning Assisted Workflow

Gravity survey has played an essential role in many geoscience fields ever since it was conducted, especially as an early screening tool for subsurface hydrocarbon exploration. With continued improvement in data processing techniques and gravity survey accuracy, in-depth gravity anomaly studies, such as characterization of Bouguer and isostatic residual anomalies, have the potential to delineate prolific regional structures and hydrocarbon basins. In this study, we focus on developing a cost-effective, quick, and computationally efficient screening tool for hydrocarbon exploration using gravity data employing machine learning techniques. Since land-based gravity surveys are often expensive and difficult to obtain in remote places, we explore the use of satellite-based gravity, which is available throughout the Earth and updated periodically. Since the accuracy and resolution of the satellite gravity data are lower than land-based gravity measurement, satellite data was enhanced through a deep-learning-based super-resolution technique. We compare the use of land-based, satellite-based, and enhanced-satellite Bouguer and isostatic gravity data for the classification of hydrocarbon regions using both supervised and unsupervised machine learning techniques. In addition, a comparison of geostatistical models and Random Forest regression is performed for geospatial interpolation. The use of different combinations of input features (Bouguer, isostatic gravity, latitude, longitude coordinates) and prediction classes (oil, gas, oil and gas, no hydrocarbons) are evaluated and compared. Results indicate the successful application of supervised machine learning workflow for hydrocarbon classification using Bouguer and isostatic gravity anomalies with good prediction accuracy for both land-based and satellite-based gravity data. The results from unsupervised machine learning were less robust in comparison

Spatiotemporal subpixel mapping of time-series images

Land cover/land use (LCLU) information extraction from multitemporal sequences of remote sensing imagery is becoming increasingly important. Mixed pixels are a common problem in Landsat and MODIS images that are used widely for LCLU monitoring. Recently developed subpixel mapping (SPM) techniques can extract LCLU information at the subpixel level by dividing mixed pixels into subpixels to which hard classes are then allocated. However, SPM has rarely been studied for time-series images (TSIs). In this paper, a spatiotemporal SPM approach was proposed for SPM of TSIs. In contrast to conventional spatial dependence-based SPM methods, the proposed approach considers simultaneously spatial and temporal dependences, with the former considering the correlation of subpixel classes within each image and the latter considering the correlation of subpixel classes between images in a temporal sequence. The proposed approach was developed assuming the availability of one fine spatial resolution map which exists among the TSIs. The SPM of TSIs is formulated as a constrained optimization problem. Under the coherence constraint imposed by the coarse LCLU proportions, the objective is to maximize the spatiotemporal dependence, which is defined by blending both spatial and temporal dependences. Experiments on three data sets showed that the proposed approach can provide more accurate subpixel resolution TSIs than conventional SPM methods. The SPM results obtained from the TSIs provide an excellent opportunity for LCLU dynamic monitoring and change detection at a finer spatial resolution than the available coarse spatial resolution TSIs

Multisource and Multitemporal Data Fusion in Remote Sensing

The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary datasets, however, opens up the possibility of utilizing multimodal datasets in a joint manner to further improve the performance of the processing approaches with respect to the application at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several spaceborne sensors, the integration of the temporal information with the spatial and/or spectral/backscattering information of the remotely sensed data is possible and helps to move from a representation of 2D/3D data to 4D data structures, where the time variable adds new information as well as challenges for the information extraction algorithms. There are a huge number of research works dedicated to multisource and multitemporal data fusion, but the methods for the fusion of different modalities have expanded in different paths according to each research community. This paper brings together the advances of multisource and multitemporal data fusion approaches with respect to different research communities and provides a thorough and discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to conduct novel investigations on this challenging topic by supplying sufficient detail and references

Machine Learning on Neutron and X-Ray Scattering

Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and X-ray scattering techniques. We highlight the integration of machine learning methods into the typical workflow of scattering experiments. We focus on scattering problems that faced challenge with traditional methods but addressable using machine learning, such as leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials' representations for learning tasks, mitigating spectral noise, and many others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.Comment: 56 pages, 12 figures. Feedback most welcom

딥러닝을 활용한 시공간 데이터 예측

학위논문(석사) -- 서울대학교대학원 : 자연과학대학 통계학과, 2023. 8. 임채영.크리깅은 관측한 시공간 데이터를 이용해 관측되지 않은 위치를 예측하는 통계적 기법이다. 데이터를 예측하여 보간하는데 시공간 데이터의 시공간 의존성을 이용한다. 그러나 복잡한 데이터의 경우 크리깅은 최적의 예측값이 되지 않을 수 있다. 최근 심층 신경망을 이용한 딥러닝은 많은 분야에서 활용되고 있다. 다층 퍼셉트론을 회귀에 활용할 수 있다는 점을 이용해 본 논문에서는 이 신경망 구조를 이용한 새로운 크리깅 방법을 제안한다. 이 방법은 더 복잡한 시공간 확률 과정을 학습할 수 있다. 그다음, 통계 학습 이론의 관점에서 기존의 크리깅 방법과 제안된 방법을 비교한다. 마지막으로, 실제 한국의 미세먼지 농도 데이터에 제안된 방법을 활용하여 이것의 성능을 평가한다. 여기서 교차 검증 방법을 사용한다.Kriging provides the Best Linear Unbiased Predictor (BLUP) for a spatial data or spatio-temporal data. This is a method of interpolation used to predict spatial process or spatio-temporal process at unobserved locations. However, for complex data, Kriging, the linear predictor, may not be optimal. Nowadays, Deep learning using Deep neural networks (DNNs) is being used in many fields. Deep feedforward networks can be used for regression, so I propose a novel prediction method using DNN structure in this study. This method may learn more complex spatio-temporal dependencies. Next, I study the traditional Kriging and my method in terms of statistical learning theory. Finally, I apply my method to Korea fine dust data to evaluate the performance. Here, the K-fold Cross Validation method for spatio-temporal data is used.Chapter 1 Introduction 1 Chapter 2 Preliminaries 3 2.1 Spatio-Temporal Model 3 2.2 Spatio-Temporal Universal Kriging 4 2.3 Deep Feedforward Networks 6 Chapter 3 The Methodology 9 3.1 Decomposition of the Spatio-Temporal Process 9 3.2 Deep Neural Networks Structure 10 Chapter 4 Theoretical Study 14 4.1 Statistical Learning Theory 14 4.2 Ensemble Method 20 Chapter 5 Application 22 5.1 Detailed Structural Settings 22 5.2 Data Description 23 5.3 Results 26 Chapter 6 Conclusion and Future Work 31 6.1 Conclusion 31 6.2 Future Work 32석

Topology Optimization via Machine Learning and Deep Learning: A Review

Topology optimization (TO) is a method of deriving an optimal design that satisfies a given load and boundary conditions within a design domain. This method enables effective design without initial design, but has been limited in use due to high computational costs. At the same time, machine learning (ML) methodology including deep learning has made great progress in the 21st century, and accordingly, many studies have been conducted to enable effective and rapid optimization by applying ML to TO. Therefore, this study reviews and analyzes previous research on ML-based TO (MLTO). Two different perspectives of MLTO are used to review studies: (1) TO and (2) ML perspectives. The TO perspective addresses "why" to use ML for TO, while the ML perspective addresses "how" to apply ML to TO. In addition, the limitations of current MLTO research and future research directions are examined