Applications of Modern Mathematical Methods in Geosciences

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: closed (28 June 2023) | Viewed by 4104

Special Issue Editors


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Guest Editor
Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC 3350, Australia
Interests: rock physics; image processing; geosciences; fracture mechanics; tunneling

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Guest Editor
Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC 3350, Australia
Interests: rock mechanics; rock blasting; geomechanics; soft computing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Earthquake Research Center, Ferdowsi University of Mashhad, Mashhad, Iran
Interests: physics of waves; simulation; applied mathematics; IT

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Special Issue Information

Dear Colleagues,

The current Special Issue titled “Applications of Modern Mathematical Methods in Geosciences” is devoted to the publication of the latest research, theoretical developments, and analytical methods in different fields of geosciences. This Special Issue publishes the novel developments in the research using advanced mathematical and applied physics tools, including modern mathematical structures (group theory, graphs and networks, fractals, topology, and fuzzy structures), modern modeling methods (e.g., nonlinear dynamic systems, game theory, cellular automata, complex systems, and artificial intelligence), and modern inverse methods (e.g., multi-criteria decision making, artificial intelligence, and modern optimization and meta-heuristic algorithms) in different areas within the earth sciences, such as remote sensing, automated petrography, fractal analysis, analytical methods in fracture mechanics and rock engineering, reservoir modeling and fluid mechanics, slope stability and ground control, acoustic emission and induced seismicity, earthquakes and wave propagation in the earth, mining and rock blasting, tunneling and rock drillability, tribology, geophysics, and geomechanics.

The focus of this Special Issue is the development of advanced analytical and computational methods used for solving problems in any field of earth and geosciences. Articles submitted to this Special Issue can also address the most important recently conducted artificial intelligence, optimization algorithms, hybrid intelligent systems, and their applications in the area of earth-system modeling, as well as the physics and mechanics of solid earth. In recent years, there has been an increasing interest in the use of such advanced mathematical tools to model and predict the non-linear behaviour of earth systems and geo-materials under different conditions. We invite researchers to contribute original research and review articles that will encourage and expand the continuing research efforts concerning the applications of recent analytical, computational, and intelligence methods to assess/solve mathematical problems in any field within geosciences.

Dr. Saeed Aligholi
Dr. Manoj Khandelwal
Dr. Reza Khajavi
Dr. Danial Jahed Armaghani
Guest Editors

Manuscript Submission Information

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Keywords

  • machine learning
  • automated mineral identification
  • remote sensing
  • multifractals in earth sciences
  • fracture mechanics
  • natural and induced seismicity
  • resreviour engineering
  • rock mechanics
  • tunneling
  • tribology
  • artificial intelligence techniques
  • meta-heuristic and optimization algorithms
  • hybrid intelligent systems

Published Papers (2 papers)

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Research

25 pages, 11941 KiB  
Article
Prediction of Coal Dilatancy Point Using Acoustic Emission Characteristics: Insight Experimental and Artificial Intelligence Approaches
by Muhammad Ali, Naseer Muhammad Khan, Qiangqiang Gao, Kewang Cao, Danial Jahed Armaghani, Saad S. Alarifi, Hafeezur Rehman and Izhar Mithal Jiskani
Mathematics 2023, 11(6), 1305; https://doi.org/10.3390/math11061305 - 8 Mar 2023
Cited by 5 | Viewed by 1786
Abstract
This research offers a combination of experimental and artificial approaches to estimate the dilatancy point under different coal conditions and develop an early warning system. The effect of water content on dilatancy point was investigated under uniaxial loading in three distinct states of [...] Read more.
This research offers a combination of experimental and artificial approaches to estimate the dilatancy point under different coal conditions and develop an early warning system. The effect of water content on dilatancy point was investigated under uniaxial loading in three distinct states of coal: dry, natural, and water-saturated. Results showed that the stiffness-stress curve of coal in different states was affected differently at various stages of the process. Crack closure stages and the propagation of unstable cracks were accelerated by water. However, the water slowed the elastic deformation and the propagation of stable cracks. The peak strength, dilatancy stress, elastic modulus, and peak stress of natural and water-saturated coal were less than those of dry. An index that determines the dilatancy point was derived from the absolute strain energy rate. It was discovered that the crack initiation point and dilatancy point decreased with the increase in acoustic emission (AE) count. AE counts were utilized in artificial neural networks, random forest, and k-nearest neighbor approaches for predicting the dilatancy point. A comparison of the evaluation index revealed that artificial neural networks prediction was superior to others. The findings of this study may be valuable for predicting early failures in rock engineering. Full article
(This article belongs to the Special Issue Applications of Modern Mathematical Methods in Geosciences)
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15 pages, 3011 KiB  
Article
Amplitude-Versus-Angle (AVA) Inversion for Pre-Stack Seismic Data with L0-Norm-Gradient Regularization
by Ronghuo Dai and Jun Yang
Mathematics 2023, 11(4), 880; https://doi.org/10.3390/math11040880 - 9 Feb 2023
Cited by 3 | Viewed by 1365
Abstract
Amplitude-versus-angle (AVA) inversion for pre-stack seismic data is a key technology in oil and gas reservoir prediction. Conventional AVA inversion contains two main stages. Stage one estimates the relative change rates of P-wave velocity, S-wave velocity and density, and stage two obtains the [...] Read more.
Amplitude-versus-angle (AVA) inversion for pre-stack seismic data is a key technology in oil and gas reservoir prediction. Conventional AVA inversion contains two main stages. Stage one estimates the relative change rates of P-wave velocity, S-wave velocity and density, and stage two obtains the P-wave velocity, S-wave velocity and density based on their relative change rates through trace integration. An alternative way merges these two stages to estimate P-wave velocity, S-wave velocity and density directly. This way is less sensitive to noise in seismic data compared to conventional two-stage AVA inversion. However, the regularization for the direct AVA inversion is more complex. To regularize this merged inverse problem, the L0-norm-gradient of P-wave velocity, S-wave velocity and density was used. L0-norm-gradient regularization can provide inversion results with blocky features to make formation interfaces and geological edges precise. Then, L0-norm-gradient regularized AVA inversion was performed on the synthetic seismic traces. Next, a real seismic data line that contains three partial angle stack profiles was used to test the practice application. The inversion results from synthetic and real seismic data showed that L0-norm-gradient regularized AVA inversion is an effective way to estimate P-wave velocity, S-wave velocity and density. Full article
(This article belongs to the Special Issue Applications of Modern Mathematical Methods in Geosciences)
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