Data-enhanced revealing of trends in Geoscience

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 (a) illustrates a steady increase in the number of articles within this trend, accompanied by a rapid surge in cumulative citation counts between 2018 and 2021. This trend significantly contributes to “SDG 9 Industry, Innovation, and Infrastructure”, “SDG 11 Sustainable Cities and Communities”, and “SDG 13 Climate Action” (Figure 2 (b)).

Figure 2.

Trends in Comparative Planetology Over Time (2014-2023): (a) Publications and Cumulative Citations; (b) Contributions to SDGs.

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 (a) demonstrates a rapid increase in both the number of articles and the cumulative citation counts within this trend. This trend plays a crucial role in advancing “SDG 2 Zero Hunger”, “SDG 6 Gender Equality”, “SDG 11 Sustainable Cities and Communities”, and “SDG 13 Climate Action” (Figure 3 (b)).

Figure 3.

Trends in Past, Present and Future Climate Change Over Time (2014-2023): (a) Publications and Cumulative Citations; (b) Contributions to SDGs.

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.

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 costeffective 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.

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.