Coal seam thickness prediction based on transition probability of structural elements

Coal seam thickness prediction is crucial in coal mine design and coal mining. In order to improve the prediction accuracy, an improved Kriging interpolation method on the basis of efficient data and Radial Basis Function (RBF-Kriging) is firstly proposed to interpolate the cutting data obtained in pre-mining, especially at the edge of the geological surface of coal seam by taking into account the spatial structure and the efficient spatial range, ensuring the integrity of the edge data during the movement of structural elements. Then, a structural element transition probability based Gaussian process progression (STTP-GPR) method is proposed to predict the coal seam thickness from the interpolated coal seam data. The experimental results demonstrated that the proposed STTP-GPR method has superior performance in coal seam thickness prediction. The average absolute error of thickness prediction for thin coal seams is 0.025 m which significantly improves the prediction accuracy in comparison to the existing BP neural networks, support vector machine and Gaussian process regression methods

Intelligent classification of coal structure using multinomial logistic regression, random forest and fully connected neural network with multisource geophysical logging data

Acknowledgments This research was funded by the National Natural Science Foundation of China (grant nos. 42130806, 41922016 and 41830427).Peer reviewe

Land subsidence susceptibility mapping in South Korea using machine learning algorithms

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results

Paleohydrology and Machine-Assisted Estimation of Paleogeomorphology of Fluvial Channels of the Lower Middle Pennsylvanian Allegheny Formation, Birch River, WV

Rivers transport sediments in a source to sink system while responding to allogenic controls of the depositional system. Stacked fluvial sandstones of the Middle Pennsylvanian (Desmoinesian Stage, ∼310–306 Ma) Allegheny Formation (MPAF) exposed at Birch River, West Virginia exhibit change in sedimentary structure and depositional style, reflecting changes in allogenic behavior. Paleohydrologic and numerical analysis were used to quantify geomorphological and paleohydrologic variations reflected by MPAF fluvial deposits with the goal of understanding the controls on resulting fluvial sandstone architecture in these different systems. Channel body geometry, sedimentary structures, and sandstone grain size distribution were used to reconstruct the paleoslope and flow velocity of the MPAF fluvial systems. In order to enhance paleohydrological estimates, machine learning methods including multiple regression and support vector regression (SVR) algorithms were used to improve the dune height, and channel depth estimated from cross-set thickness. Results show that the channel depths of the lower MPAF beneath the Lower Kittanning coal beds tend to decrease upsection; this decrease is interpreted to reflect a transition from fluvial systems formed in a humid ever-wet climate to fluvial systems formed in less humid, seasonally wet, semi-arid climate. Paleohydrologic estimations enabled the evaluation of hydraulic changes in the fluvial depositional systems of the Appalachian Basin during the Desmoinesian stage. Paleoslope estimates indicated that the slope was low, which indicated that the fluvial gradient response was not driven by the effect of tectonic subsidence or uplift and sea-level change

Mathematical Problems in Rock Mechanics and Rock Engineering

With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering

Process Based Analysis Of Fluvial Stratigraphic Record: Middle Pennsylvanian Allegheny Formation, North-Central WV

Fluvial deposits represent some of the best hydrocarbon reservoirs, but the quality of fluvial reservoirs varies depending on the reservoir architecture, which is controlled by allogenic and autogenic processes. Allogenic controls, including paleoclimate, tectonics, and glacio-eustasy, have long been debated as dominant controls in the deposition of fluvial strata. However, recent research has questioned the validity of this cyclicity and may indicate major influence from autogenic controls. To further investigate allogenic controls on stratal order, I analyzed the facies architecture, geomorphology, paleohydrology, and the stratigraphic framework of the Middle Pennsylvanian Allegheny Formation (MPAF), a fluvial depositional system in the Appalachian basin, to test for the dominant allogenic and/ or autogenic controls during deposition. A sedimentological process based approach has been used to analyze controls on the depositional reservoir quality of fluvial sandstone units. In this research, I utilized facies architectural analysis to identify four depositional styles for channel deposits of the MPAF. The depositional facies were used to identify paleoclimatic controls on fluvial sedimentary fill. I introduced a new, efficient numerical model to aid in channel geometry and paleohydrological modeling of the MPAF channels. The new numerical modeling method increased the accuracy of estimated channel geomorphology and hydrologic processes. I proposed a sequence stratigraphic framework, which utilized surfaces of floodplain paleosols and erosional channel bases, to correlate fluvial depositional packages across the Appalachian basin. The integration of facies architectural analysis and sequence stratigraphic allowed the differentiation of accommodation and controls on accommodation within vertically stacked deposits of the fluvial depositional system

Predicting the Future

Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled “Predicting the Future—Big Data and Machine Learning” is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings

Evaluation of CO2 Injection in Shale Gas Reservoirs through Numerical Reservoir Simulation and Supervised Machine Learning

CO2 geological storage is an important means to decarbonize the economy, but also expensive in field applications. CO2 injection into shale gas reservoirs can significantly increase the economic incentive by enhancing shale gas production through CO2 preferential adsorption in shales. This work presents a practical framework to evaluate and predict the CO2 adsorption process and CH4 production in shales through a combination of numerical reservoir simulation and supervised machine learning