Machine learning in geosciences and remote sensing

Learning incorporates a broad range of complex procedures. Machine learning（ML） is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficultto-program applications, and software applications. It is a collection of a variety of algorithms（e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc.） that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore,nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.

Major contribution to carbon neutrality by China’s geosciences and geological technologies

In the context of global climate change,geosciences provide an important geological solution to achieve the goal of carbon neutrality,China’s geosciences and geological technologies can play an important role in solving the problem of carbon neutrality.This paper discusses the main problems,opportunities,and challenges that can be solved by the participation of geosciences in carbon neutrality,as well as China’s response to them.The main scientific problems involved and the geological work carried out mainly fall into three categories:(1)Carbon emission reduction technology(natural gas hydrate,geothermal,hot dry rock,nuclear energy,hydropower,wind energy,solar energy,hydrogen energy);(2)carbon sequestration technology(carbon capture and storage,underground space utilization);(3)key minerals needed to support carbon neutralization(raw materials for energy transformation,carbon reduction technology).Therefore,geosciences and geological technologies are needed:First,actively participate in the development of green energy such as natural gas,geothermal energy,hydropower,hot dry rock,and key energy minerals,and develop exploration and exploitation technologies such as geothermal energy and natural gas;the second is to do a good job in geological support for new energy site selection,carry out an in-depth study on geotechnical feasibility and mitigation measures,and form the basis of relevant economic decisions to reduce costs and prevent geological disasters;the third is to develop and coordinate relevant departments of geosciences,organize and carry out strategic research on natural resources,carry out theoretical system research on global climate change and other issues under the guidance of earth system science theory,and coordinate frontier scientific information and advanced technological tools of various disciplines.The goal of carbon neutrality provides new opportunities and challenges for geosciences research.In the future,it is necessary to provide theoretical and technical support from various aspects,enhance the ability of climate adaptation,and support the realization of the goal of carbon peaking and carbon neutrality.

Climate change impacts on environmental geosciences: Introduction

Climate change and its impacts have become topical issues of global news,scientific research and conferences.Environmental Geosciences incorporate the various disciplines of geosciences and their multifaceted interactions with life.Research discussions on the interaction of climate change,geosciences and environment may often be blur,and Schmidt-Thoméet al.(2010)stated that“Often past climate changes that can be deduced from geological records may help in understanding the speed of potential climate change effects,i.e.how quickly have sea levels changed,how drastic has nature reacted to ups and downs in temperature,etc.These analyses of past events help in giving outlooks on potential changes in our living environment.It is also of important to understand the magnitude and potential effects of extreme events,such as droughts and floods”.

Progress of machine learning in geosciences:Preface

In the past two decades, artificial intelligence （AI） algorithms have proved to be promising tools for solving several tough scientific problems, As a broad subfield of AI, machine learning is concerned with algorithms and techniques that allow computers to ＂learn＂. The machine learning approach covers main domains such as data mining, difficult-to-program applications, and soft- ware applications. It is a collection of a variety of algorithms that can provide multivariate, nonlinear, nonparametric regression or classification. The remarkable simulation capabilities of the ma- chine learning-based methods have resulted in their extensive ap- plications in science and engineering. Recently, the machine learning techniques have found many applications in the geoscien- ces and remote sensing. More specifically, these techniques are proved to be practical for cases where the system＇s deterministic model is computationally expensive or there is no deterministic model to solve the problem （Lary, 2010）.

Recent progress in submarine geosciences in China

In China submarine geosciences represents a newly established discipline of oceanography, focusing on the oceanic lithosphere, and its interface with the hydrosphere and biosphere. Recently, supported by the National High Technology Research and Development Program and other high-tech development projects, significant progress has been made in the development of advanced technologies and equipment. This en- ables the scientists in China to carry out explorations of the international seabed area in the Pacific Ocean and on the Southwest Indian Ridge. In addition, they have been active in the research activities associated the mid-ocean ridges and western Pacific marginal seas. It is anticipated that this research field will continue to be highly fruitful in the near future.

Special Issue of Mathematical Geosciences for the 33^(rd) IGC

The papers published in this issue are selected from manuscripts submitted by invited authors and most of these papers will be presented at the 33^rd International Geological Congress （33^rd IGC） in Oslo, August 2008. It receives 25 manuscripts and 16 were accepted after going through the journal normal peer reviewing process. The topics of the papers cover various aspects of ＂metallogenic complex processes and mineral resource quantitative assessment＂, one of the strategic research areas of the State Key Laboratory of Geological Processes Resources （GPMR） sponsored by the Science and Technology and the and Mineral Ministry of Ministry of Education of China. Researches in the area are also supported by the National Natural Science Foundation and Ministry of Land and Resources of China as well as by mining companies. Thanks are due to these funding programs and organizations for supporting the research activities of GPMR. Sincere thanks are due to those who have reviewed the manuscripts and provided critical comments and even English editing of some of the papers. We are very appreciated for the assistance of the editorial office especially Professor Wang, the editor-in-chief, Ms. Yuan and many others who have worked hard to make this issue be printed before the event of 33^rd IGC. Special thanks are given to Professor Xie Shuyun, Xu Deyi and Tali Neta and many other members in the Geomatics Research Lab of York University for handling the manuscripts during the reviewing processes.

An Introduction to China University of Geosciences(Beijing)

China University of Geosciences（Beijing）（CUGB）,is one of the leading universities listed in"Project 211,"a national program that offers more government financial support for the advancement of 100 top national universities in 21st-century China.CUGB is also supported by the Ministry of Education for its uniquely strong strengths in geological disciplines.CUGB could be traced back to the former Beijing College of Geology,which was founded in 1952 by combining the Departments of Geology from Peking University, Tsinghua University,Tianjin University and Tangshan Railway College.In 1987,the predecessor of CUGB was renamed China University of Geosciences（Beijing）.

China University of Geosciences in Beijing

FOUNDED in 1952, China University of Geosciences in Beijing （CUGB） is a national key university under the direct auspices of the Ministry of Education. Over the past 60 years, it has developed from a geological college tbcused on a single discipline to a multi- disciplinary comprehensive university, offering courses in geology, natural resources, environment and geological engineering.

Investigating dynamic mechanisms of geological phenomena using methodology of computational geosciences:An example of equal-distant mineralization in a fault

This paper presents a research methodology associated with approximately a decade old computa- tional geosciences. To demonstrate how it can be used to investigate the dynamic mechanisms of geological phenomenon, we use as an example the equal-distant distribution of gold deposits in a three-dimensional permeable fault within the Yilgarn Craton, Western Australia. The related numerical results demonstrate that: (1) convective pore-fluid flow in fluid-saturated porous media is the control- ling dynamic mechanism leading to the equal-distant distribution of gold deposits along the fault; (2) the main characteristic of the new methodology is to change the traditionally used empirical, descrip- tive and qualitative methodology into the fundamentally scientific principles based predictive and quantitative methodology. Thus, this new methodology provides a modern scientific research tool for investigating the dynamic mechanisms associated with observed geological phenomena in nature.

Virtual earth cloud: a multi-cloud framework for enabling geosciences digital ecosystems

Humankind is facing unprecedented global environmental and social challenges in terms of food,water and energy security,resilience to natural hazards,etc.To address these challenges,international organizations have defined a list of policy actions to be achieved in a relatively short and medium-term timespan.The development and use of knowledge platforms is key in helping the decision-making process to take significant decisions(providing the best available knowledge)and avoid potentially negative impacts on society and the environment.Such knowledge platforms must build on the recent and next coming digital technologies that have transformed society–including the science and engineering sectors.Big Earth Data(BED)science aims to provide the methodologies and instruments to generate knowledge from numerous,complex,and diverse data sources.BED science requires the development of Geoscience Digital Ecosystems(GEDs),which bank on the combined use of fundamental technology units(i.e.big data,learning-driven artificial intelligence,and network-based computing platform)to enable the development of more detailed knowledge to observe and test planet Earth as a whole.This manuscript contributes to the BED science research domain,by presenting the Virtual Earth Cloud:a multi-cloud framework to support GDE implementation and generate knowledge on environmental and social sustainability.

A unified framework of temporal information expression in geosciences knowledge system

Time is an essential reference system for recording objects,events,and processes in the field of geosciences.There are currently various time references,such as solar calendar,geological time,and regional calendar,to represent the knowledge in different domains and regions,which subsequently entails a time conversion process required to interpret temporal information under different time references.However,the current time conversion method is limited by the application scope of existing time ontologies(e.g.,“Jurassic”is a period in geological ontology,but a point value in calendar ontology)and the reliance on experience in conversion processes.These issues restrict accurate and efficient calculation of temporal information across different time references.To address these issues,this paper proposes a Unified Time Framework(UTF)in the geosciences knowledge system.According to a systematic time element parsing from massive time references,the proposed UTF designs an independent time root node to get rid of irrelevant nodes when accessing different time types and to adapt to the time expression of different geoscience disciplines.Furthermore,this UTF carries out several designs:to ensure the accuracy of time expressions by designing quantitative relationship definitions;to enable time calculations across different time elements by designing unified time nodes and structures,and to link to the required external ontologies by designing adequate interfaces.By comparing the time conversion methods,the experiment proves the UTF greatly supports accurate and efficient calculation of temporal information across different time references in SPARQL queries.Moreover,it shows a higher and more stable performance of temporal information queries than the time conversion method.With the advent of the Big Data era in the geosciences,the UTF can be used more widely to discover new geosciences knowledge across different time references.

Effectiveness of basin morphometry, remote sensing, and applied geosciences on groundwater recharge potential mapping： a comparative study within a small watershed

A multidisciplinary approach using the inte- grated field of geosciences （e.g., geomorphology, geotec- tonics, geophysics, and hydrology） is established to conduct groundwater recharge potential mapping of the Kunur River Basin, India. The relative mean error （RME） calculation of the results of three applied techniques and water table data from twenty-four observation wells in the basin over the 2000-2010 period are presented. Nine sub- basins were identified and ranked for the RME calculation, where the observation wells-based ranking was taken as standard order for comparison. A linear model has been developed using six factors （drainage density, surface slope, ruggedness index, lineament density, Bouguer gravity anomaly, and potential maximum water retention capacity） and a grid-wise weighted index. In a separate comparative approach, the sub-basin and grid-wise analyses have been conducted to identify the suitable spatial unit for watershed level hydrological modeling.

The research on HRM model of geosciences engineering perambulation enterprise

Firstly,this paper defines the definition of geosciences engineering perambulation enterprise,which belongs to the knowledgeable enterprise;then,it summarizes the general HRM model presented by other researchers,based on those models,this paper builds a new HRM model of geosciences engineering perambulation enterprise.