Advanced Geostatistics and Data Analysis: Novel Mathematical Methods and Their Applications

Dear Colleagues,

We are honored to invite you to submit your latest research developments to our Special Issue.

Accurate characterization of Earth and environmental phenomena is crucial due to its fundamental role in understanding natural processes, assessing risks, and making informed decisions in the context of developing various scientific and engineering applications. Driven by innovations such as stochastic modeling, high-dimensional statistics, and machine learning, geostatistics and spatial data analysis technology have been experiencing revolutionary breakthroughs, including the complex multiple-source data revolution, interdisciplinary integration, practical engineering applications, etc. For instance, with the popularization of high-resolution remote sensing and distributed sensor networks, geoscientific data present multiple-source, multiple-scale, and high-dimensional features, requiring mathematical theories and approaches such as Maximum Likelihood Estimation, Gaussian Random Fields, and Bayesian hierarchical modeling for effective processing and analysis. Advanced geostatistical methods have improved in computational efficiency and accuracy when dealing with spatial–temporal dynamic coupling problems in practical applications.

This Special Issue aims to offer a big-picture exploration of recent mathematical advances in geostatistics and data analysis in the fields of geological and environmental engineering. By assembling a variety of papers focusing on new methods of geostatistics and spatial data analysis, we highlight how emerging mathematical methods are transforming the field and enabling novel engineering applications.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

1. Multiple-scale spatial-temporal Modeling and uncertainty analysis;
2. New methods for coupling geostatistics and machine learning;
3. High-dimensional and non-structural spatial data analysis;
4. Credibility assessment of geoscientific big data;
5. Gaussian random fields;
6. Nonparametric spatial regression;
7. Information geometry in geostatistics;
8. Interdisciplinary applications of geostatistics.

We look forward to receiving your contributions.

Dr. Qiyu Chen
Dr. Weisheng Hou
Dr. Shaoqun Dong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• geostatistics
• stochastic simulation
• multiple-point statistics
• spatial–temporal modeling
• high-dimensional data analysis
• machine learning