1 Introduction
Solid Earth geoscience is immensely important, encompassing numerous directions and accumulating vast amounts of data ripe for machine learning analysis to assist scientists in clarifying their research paths (Bergen et al., 2019; Wang et al., 2021). This discipline faces significant challenges such as the sheer volume of data, the diversity of research directions, the multitude of researchers involved, and the inherent complexity of the field. Recent advances in data-driven solid Earth science discoveries illustrate the potential of these techniques in various areas, including the identification of mineral diversity patterns (Hazen & Morrison, 2022), high-resolution marine invertebrate biodiversity curves (Fan et al., 2020), a refined Cenozoic atmospheric CO2 record (CenCO2PIP Consortium, 2023), and a global landscape evolution model revealing stable Cenozoic sedimentation rates (Salles et al., 2023). As geosciences enter the era of big data, emerging research directions are continually appearing, presenting numerous new problems that urgently require solutions. However, in a crowded field of concerns, how can researchers prioritize the most significant questions?
To address this challenge, bibliometric analysis and science mapping offer powerful techniques (Pessin et al., 2022). Through the application of mathematics and statistical methods to books and other media of communication (Pritchard, 1969), bibliometric analysis effectively facilitates identifying and tracking research trends (Aksnes & Sivertsen, 2023; Mazov et al., 2020; Small, 2003; Upham & Small, 2010). Currently, scholars have employed bibliometric analysis to investigate research hotspots within the geosciences (Ai et al., 2022; Hazen, 1980; Ren et al., 2023; Wang et al., 2022; Xiao & Sun, 2005; Zhao et al., 2024). However, despite its advantages in exploring research trends, bibliometric analysis is limited by data source biases, citation delays, and metric standardization issues. These limitations underscore the necessity of incorporating expert insights for comprehensive trend evaluation (“Experts still needed”, 2009).
Expert insights are indispensable in geoscience research for interpreting and validating bibliometric data, providing a nuanced understanding of research trends beyond what quantitative analysis alone can offer. These insights deliver contextual knowledge, enable early identification of emerging trends, validate statistical findings, integrate qualitative perspectives, and facilitate comprehensive cross-disciplinary assessments, thus addressing the inherent limitations of bibliometric analysis (Allen et al., 2009; Iivari, 2008; Laurens et al., 2010). The Deep-time Digital Earth (DDE) initiative, recognized as the first big science program by the International Union of Geological Sciences (IUGS) (Normile, 2019), exemplifies an effective approach to overcoming these limitations. It leverages the expertise of internationally renowned members of the DDE Science Committee to enhance the robustness and depth of geoscience research.
In this paper, we integrate bibliometric analysis with expert insights to navigate the complexities of big data and prioritize research questions that will drive the Solid Earth geoscience forward.
2 Data and methodology
We define research trends at two levels: theoretical and practical. At the conceptual level, research trends refer to issues or subjects that are extensively discussed and studied within the current academic community. These research trends reflect the latest developments and advancements in the academic field and have the potential to address real-world problems closely associated with society, science, and technology. They attract a substantial number of high-quality papers published within a short period, resulting in a significant increase in the citation frequency within the specific field.
At the practical level, the research trends are identified through the clustering of highly co-cited papers and papers with significant keyword co-occurrence. These papers are obtained from the Web of Science and Dimensions. The time range was set from 2014 to 2023.
Our methodology is tailored to explore the evolving research trends specifically in the field of Solid Earth Sciences from data perspective and expert view. It meticulously sifts through and identifies key areas where innovative concepts and practical applications intersect within this scientific domain.
The data collection process for this article was meticulously conducted by leveraging two primary sources for bibliometrics: Web of Science and Dimensions databases. The publication time window of this article is a 10-year period. Inspired by the paper titled “A century of physics”(Sinatra et al., 2015), we collected data by two distinct methods to provide a comprehensive overview of research trends in Geosciences. The study drew insights from over 400,000 papers sourced from 466 Geosciences journals, focusing on diverse sub-fields such as Geochemistry & Geophysics and Geology. These journals were carefully selected from a pool of about 21,000 indexed journals in the Web of Science. Data was also extracted from interdisciplinary journals, which involved analyzing approximately 5,800 papers sourced from 93 interdisciplinary journals, including reputable publications like Nature and Science. Notably, a significant portion of references within these interdisciplinary journals originated from the 466 Geosciences journals analyzed earlier. By merging insights from 41 thousand articles in these two robust databases, we were able to conduct a thorough and comprehensive analysis of research trends in Geosciences, encompassing a broad spectrum of academic publications and interdisciplinary references.
We gathered metadata information such as titles, abstracts, keywords, and references of the articles. Subsequently, we employed CiteSpace (Chen, 2006) to construct citation networks and visualize the data. Through spectral clustering algorithms, we clustered the articles based on network structures to group together papers with similar or related topics, resulting in 407 clusters. For each cluster, we applied the Log-Likelihood Ratio (LLR) algorithm to extract research terms from the titles and abstracts of the articles and calculated the relevance among the papers. The highest scoring terms were selected as the cluster’s label keywords. We then conducted statistical analyses on the number of papers, average publication year, and average citation count within each cluster.
These trends were meticulously selected by the DDE Science Committee based on their representation, relevance, and impact within the Geosciences domain. Each trend was manually labeled by the DDE Science Committee, with careful consideration of the content and implications of the associated research. Additionally, the literature datasets intended for in-depth analysis have been thoroughly reviewed and validated by the DDE Science Committee to ensure their accuracy and comprehensiveness. Furthermore, the DDE Science Committee has ensured that the selected trends are not only relevant to current scientific inquiries but also have significant potential to influence future research directions.
3 Results
Leveraging the data-enhanced and scientific community-driven methods, 30 trends were identified as significant in the field of Geosciences, spanning five domains: deep space, deep time, deep Earth, habitable Earth, and big earth data (Figure 1 ). In this section, we will delve into one trend from each domain to highlight the diverse and influential research that is shaping the field of Geosciences.
Figure 1. 30 Trends in Geosciences. |
For a detailed exploration of each trend, we invite you to visit our website (trends.ddeworld.org), where you can find comprehensive insights into the other trends that are driving innovation and discovery in Geosciences. The five trends are selected and listed below.
3.1 Deep Space: Comparative Planetology
Comparative Planetology , an interdisciplinary field of study, examines planets, moons, and other celestial bodies throughout the cosmos to unravel their origins, development, and potential habitability. This discipline is crucial for gaining insights into Earth’s past and situating it within the broader cosmic context. It navigates through the intricacies of celestial phenomena, addressing questions about the Moon’s formation characterized by intense impacts and extensive volcanic activity, as well as Mars’ complex climatic history, evident in its diverse mineral composition and sedimentary remnants of ancient water bodies. The exploration extends to Jupiter’s enigmatic structure and magnetic properties, providing valuable clues about the formation of gas giants. The discovery of hydrothermal activity on Enceladus raises intriguing possibilities for extraterrestrial life, while isotopic analysis of meteorites sheds light on the early stages of our solar system. Understanding Earth’s resilience amidst human-induced changes is also a key focus, highlighting the importance of preserving planetary stability. Technological advancements have significantly advanced the field, with precise data from missions such as Juno, Curiosity, and Cassini revolutionizing our understanding of Jupiter, Mars, and Enceladus, respectively. Innovations like the Ames Stereo Pipeline have improved terrain analysis on planetary surfaces, while radiometric dating of lunar samples, including those from recent missions like Chang’e-5, has refined our understanding of the Moon’s geological evolution. The InSight mission has provided valuable insights into Martian seismic activity and internal structure.
Looking ahead, Comparative Planetology is poised to expand its scope with upcoming interplanetary missions and technological breakthroughs. Future research will delve deeper into the mysteries of celestial body formation and evolution, exploring phenomena such as volcanism and hydrothermal processes, and continuing the search for extraterrestrial life. Crucially, the field will assess the impact of human activities on Earth’s delicate balance within the solar system. This holistic approach not only enriches our understanding of the cosmos but also underscores the significance of Earth’s unique role and vulnerability in the vast expanse of the universe.
Figure 2. Trends in Comparative Planetology Over Time (2014-2023): (a) Publications and Cumulative Citations; (b) Contributions to SDGs. |
3.2 Deep Time: Past, Present and Future Climate Change
Past, Present and Future Climate Change represents an extensive modification of Earth’s atmospheric and oceanic patterns over time, covering past epochs, present conditions, and future projections. Its study is crucial for comprehending Earth’s climatic history, evaluating ongoing environmental shifts, and forecasting upcoming climatic changes, which are vital for global sustainability and risk mitigation. Addressing the challenges of deciphering past climate signals, accurately monitoring current climatic trends, and predicting future changes requires tackling complex, interacting natural and anthropogenic factors. Central scientific inquiries involve understanding the driving forces behind historical climate transitions, quantifying current climate alterations, and modeling future climatic scenarios under various emissions pathways. Breakthroughs in isotope analysis, speleothem records, clumped isotope thermometry, and climate modeling have advanced our grasp of climate dynamics. These tools have unveiled pivotal insights into past temperature variations, monsoon patterns, ice sheet dynamics, and sea-level shifts, enhancing current understanding and predictions of marine heatwaves, drought patterns, Arctic amplification, and flood events. The research amassed elucidates critical aspects of historical climate epochs, including the Cenozoic, Cretaceous, and Eocene, emphasizing the climate system’s complexity and sensitivity to factors like greenhouse gases and ocean temperatures. Future research must focus on integrating interdisciplinary approaches, refining climate models, and broadening observational data scope. This includes reconciling model predictions with observational data, especially in under-represented regions such as the Arctic and tropics. Advancements in technology and methodology will be pivotal in fine-tuning predictions for future climate scenarios, helping to shape effective global responses for climate change mitigation and adaptation. This body of work highlights the importance of ongoing investigations, merging historical data with innovative science, to deepen our climate change understanding and inform effective policy and management strategies for a resilient future.
Figure 3. Trends in Past, Present and Future Climate Change Over Time (2014-2023): (a) Publications and Cumulative Citations; (b) Contributions to SDGs. |
3.3 Deep Earth: 3D Earth Structure
The exploration of 3D Earth Structure , which delves into the intricate modeling of our planet’s internal layers, is pivotal for understanding Earth’s formation, evolution, and geological phenomena. Challenges in this field encompass complex internal dynamics, precise compositional determination, and the integration of diverse datasets. Advances have predominantly been driven by seismic imaging and tomography, revealing intricate mantle dynamics and crustal structures. Significant discoveries include the identification of extensive mantle plumes beneath hotspots and detailed insights into crust-upper mantle interactions across diverse regions. New perspectives on the inner core propose a superionic state for light elements and an oscillating rotation pattern. Additionally, integrating soil structure into Earth System Models and innovative methodologies for surface wave dispersion inversion represent significant interdisciplinary advancements. The development of comprehensive models such as LITHO1.0 and global marine gravity models has been crucial in uncovering concealed tectonic structures and updating global sediment thickness estimates. A consensus estimate on global glacier ice thickness further enhances our understanding of environmental impacts. Future efforts should focus on refining deep Earth imaging techniques and fostering multidisciplinary collaborations to achieve a more nuanced depiction of the Earth’s interior. Emphasizing interactions between different Earth layers and elucidating the roles of water and light elements in mantle and core processes are also essential. Progress in computational modeling will be essential for simulating complex geological processes, aiding in natural disaster prediction and mitigation, resource management, and advancing our comprehension of Earth’s dynamic history.
Trend in 3D Earth Structure reveals a notable surge in publications and citations from 2018 to 2021, followed by a subsequent decline (Figure 4 (a) ). The primary contributions of this research are aligned with “SDG 13 Climate action”, and “SDG 14 Life below water”(Figure 4 (b) ).
Figure 4. Trends in 3D Earth Structure Over Time (2014-2023): (a) Publications and Cumulative Citations; (b) Contributions to SDGs. |
3.4 Habitable Earth:Carbon Capture and Storage
Carbon Capture and Storage (CCS) technology, designed to mitigate greenhouse gas emissions, captures carbon dioxide (CO2) from industrial and energy-related sources and securely stores it underground, contributing significantly to climate change mitigation efforts and the transition towards carbon neutrality. Recent studies have highlighted a range of challenges and innovative breakthroughs in the field. Economic and societal barriers, such as cost and public acceptance, pose significant obstacles, while technological challenges include the development of efficient capture methods, understanding fluid dynamics in geological formations, and ensuring long-term safety and containment. Noteworthy advancements include the use of machine learning models to predict CO2 trapping efficiency, novel hydrate-based carbon capture techniques, and improved understanding of shale gas recovery processes. Current efforts focus on developing strategic global CCS layouts for cost-effective CO2 management, increasing storage capacities to gigatons levels, and deepening our understanding of climate change interactions with various CO2 sinks. Future directions for CCS involve strengthening global collaboration, particularly in financial and technological domains, with an emphasis on understanding regional climate impacts on carbon sinks, enhancing public acceptance, and addressing ethical considerations. The growing utilization of data-driven approaches and computational modeling to optimize CCS operations, coupled with a focus on environmental and ethical aspects of CCS deployment, underscores the comprehensive approach needed for successful integration into global climate change mitigation strategies.
The number of articles and total citations in this trend have doubled over the past decade (Figure 5 (a) ). This research significantly contributes to “SDG 3 Good Health and Well-being”, “SDG 6 Clean Water and Sanitation”, “SDG 7 Affordable and Clean Energy”, “SDG 11 Sustainable Cities and Communities”, “SDG 12 Responsible Consumption and Production”, “SDG 13 Climate Action”, “SDG 14 Life Below Water” and “SDG 15 Life on Land” (Figure 5 (b) ).
Figure 5. Trends in Carbon Capture and Storage Over Time (2014-2023): (a) Publications and Cumulative Citations; (b) Contributions to SDGs. |
3.5 Big Earth Data:Machine Learning and Big Data Analytics in Geoscience
Machine Learning and Big Data Analytics in Geoscience are revolutionizing our comprehension and management of Earth’s intricate systems by harnessing advanced algorithms and extensive datasets to tackle complex geoscientific challenges. This integration plays a pivotal role in refining predictions and gaining deeper insights into Earth’s processes, essential for effective resource management, environmental conservation, and disaster mitigation.
The field has witnessed remarkable advancements in predictive modeling, particularly in mineral prospecting and groundwater mapping, employing advanced algorithms like Random Forest and Support Vector Machines. These approaches enhance our ability to identify mineral deposits and evaluate groundwater potential with greater accuracy. In disciplines such as seismology and hydrology, the adoption of machine learning techniques, including cutting-edge models like LSTM networks, has revolutionized earthquake detection, seismic tomography, and streamflow forecasting. Hybrid models that blend data-driven analytics with traditional physical modeling are increasingly being recognized as essential for comprehensive predictions.
Progress in climate and weather prediction is notable, with significant strides made in refining bias correction methods for extreme temperature forecasts and implementing innovative approaches like Generative Adversarial Networks for stochastic parameterization. However, the field faces challenges related to data integration and the interpretability of complex models, essential for fully harnessing the potential of machine learning in geoscience.
Looking ahead, the trajectory of Machine Learning and Big Data Analytics in Geoscience involves deeper integration with physical modeling across various domains. Overcoming challenges related to data heterogeneity and enhancing model interpretability are crucial goals. Expanding applications in mineral processing, urban flood risk assessment, and refining parameter learning techniques for improved model efficiency and generalizability represent key areas poised for significant advancement.
In conclusion, the ongoing evolution of machine learning and big data analytics holds immense potential to transform our capacity to address the multifaceted challenges in geoscience. Through innovative solutions and enhanced stewardship of Earth’s resources, these technologies promise to shape a more sustainable and resilient future.
With breakthroughs in the field of machine learning, its application in Earth sciences has been steadily increasing, reflected in a dual rise in both the number of publications and their impact (Figure 6 (a) ). This trend has contributed significantly to “SDG 3 Good Health and Well-being”, “SDG 4 Quality Education” and “SDG 8 Decent Work and Economic Growth” (Figure 6 (b) ).
Figure 6. Trends in Machine Learning and Big Data Analytics in Geoscience Over Time (2014-2023): (a) Publications and Cumulative Citations; (b) Contributions to SDGs. |
4 Discussion and conclusion
The list of trends was curated through a combination of expert knowledge, insights from literature, and co-citation methods. This approach aimed to achieve a moderate level of granularity in defining and characterizing the identified trends, providing a comprehensive overview of the evolving landscape in Geosciences.
These 30 topics reflect the latest trends and advancements in Geosciences and have the potential to address real-world problems that are closely related to society, science, and technology. What’s more, they are potential to provide valuable insights into major research breakthroughs, research methods, and solutions to significant scientific problems in Geosciences.
Funding information
This study was provided by the Deep-time Digital Earth (DDE) Big Science Program.
Acknowledgements
The authors extend their gratitude to the members of the DDE Science Committee, the DDE Executive Committee, and the experts from the working and task groups for their invaluable contributions and insights.
Author contributions
Yu Zhao (zhaoyu.cugb@gmail.com): Conceptualization (Lead), Data curation (Lead), Formal analysis (Equal), Investigation (Lead), Methodology (Lead), Validation (Equal), Writing-original draft (Lead), Writing-review & editing (Equal);
Meng Wang (yeswangmeng@gmail.com): Conceptualization (Lead), Data curation (Equal), Formal analysis (Equal), Investigation (Equal), Methodology (Equal), Validation (Lead), Writing-original draft (Equal), Writing-review & editing (Equal);
Jiaxin Ding (jiaxinding@sjtu.edu.cn): Conceptualization (Lead), Data curation (Lead), Formal analysis (Lead), Investigation (Equal), Methodology (Equal), Validation (Equal), Writing-original draft (Equal), Writing-review & editing (Equal);
Jiexing Qi (qi_jiexing@sjtu.edu.cn): Conceptualization (Supporting), Data curation (Equal), Formal analysis (Equal), Investigation (Equal), Methodology (Supporting), Validation (Supporting), Writing-original draft (Equal), Writing-review & editing (Equal);
Lyuwen Wu (wlw2016@sjtu.edu.cn): Conceptualization (Supporting), Data curation (Equal), Formal analysis (Equal), Investigation (Equal), Methodology (Supporting), Validation (Supporting), Writing-original draft (Equal), Writing-review & editing (Equal);
Sibo Zhang (zhang_sibo@sjtu.edu.cn): Conceptualization (Supporting), Data curation (Equal), Formal analysis (Equal), Investigation (Equal), Methodology (Supporting), Validation (Supporting), Writing-original draft (Equal), Writing-review & editing (Equal);
Luoyi Fu (yiluofu@sjtu.edu.cn): Conceptualization (Lead), Data curation (Lead), Formal analysis (Equal), Investigation (Lead), Methodology (Lead), Validation (Equal), Writing-original draft (Lead), Writing-review & editing (Equal);
Xinbing Wang (xwang8@sjtu.edu.cn): Conceptualization (Lead), Data curation (Lead), Formal analysis (Lead), Investigation (Equal), Methodology (Equal), Validation (Equal), Writing-original draft (Equal), Writing-review & editing (Equal);
Li Cheng (lcheng@cugb.edu.cn): Conceptualization (Lead), Data curation (Lead), Formal analysis (Equal), Investigation (Lead), Methodology (Lead), Validation (Equal), Writing-original draft (Lead), Writing-review & editing (Equal).