The aim of this session is to present the latest research and case studies related to various data analysis and improvement methods and modeling techniques, and demonstrate their applications from the various fields of earth sciences like: hydrology, geology and paleogeomorphology, to geophysics, seismology, environmental and climate change.

The interactions between geo-environmental and anthropic processes are increasing due to the ever-growing population and its related side effects (e.g., urban sprawl, land degradation, natural resource and energy consumption, etc.). Natural hazards, land degradation and environmental pollution are three of the possible “interactions” between geosphere and anthroposphere. In this context, spatial and spatiotemporal data are of crucial importance for the identification, analysis and modelling of the processes of interest in Earth and Soil Sciences. The information content of such geo-environmental data requires advanced mathematical, statistical and geomorphometric methodologies in order to be fully exploited.

The session aims to explore the challenges and potentialities of quantitative spatial data analysis and modelling in the context of Earth and Soil Sciences, with a special focus on geo-environmental challenges. Studies implementing intuitive and applied mathematical/numerical approaches and highlighting their key potentialities and limitations are particularly sought after. A special attention is paid to spatial uncertainty evaluation and its possible reduction, and to alternative techniques of representation of spatial data (e.g., visualization, sonification, haptic devices, etc.).

In the session, two main topics will be covered (although the session is not limited to them!):
1) Analysis of sparse (fragmentary) spatial data for mapping purposes with evaluation of spatial uncertainty: geostatistics, machine learning, statistical learning, etc.
2) Analysis and representation of exhaustive spatial data at different scales and resolutions: geomorphometry, image analysis, machine learning, pattern recognition, etc.

Remote sensing, numerical models, and machine learning have been widely used for investigating environmental risks under climate change. It is known that they tend to do an excellent job in mapping, simulating, and projecting the long-term changes in average conditions. However, damages associated with extreme weathers by droughts, floods, forest fires, heat-related mortality, and crop yield loss are often more devastating than those caused by gradual climate changes. How remote sensing, numerical models, and machine learning can be used for assessing the impacts of extreme weathers on the natural and human systems remains uncertain.
This session aims to summarize current progress in assessing the ability of remote sensing, numerical models, and machine learning for quantifying climate risks in multiple sectors, such as water, agriculture, and human health.
We especially welcome investigations focusing on the inter-comparison of methodologies, as well as multi-sectoral, cross-sectoral, and integrated assessments.

Non-destructive testing (NDT) methods have been increasingly employed in a wide range of engineering and geosciences applications and their stand-alone use has been greatly investigated to date. New theoretical developments, technological advances as well as the progress achieved in surveying, data processing and interpretation have in fact led to a tremendous growth of equipment reliability, allowing outstanding data quality and accuracy.

Nevertheless, the requirements of comprehensive site and material investigations may be complex and time-consuming, involving multiple expertise and many pieces of equipment. The challenge is to step forward and provide an effective integration between data outputs with different physical quantities, scale domains and resolutions. In this regard, enormous development opportunities relating to data fusion, integration and correlation between different NDT methods and theories are to be further investigated.

Within this framework, this Session primarily aims at disseminating contributions from state-of-the-art NDT methods and numerical developments, promoting the integration of existing equipment and the development of new algorithms, surveying techniques, methods and prototypes for effective monitoring and diagnostics. NDT techniques of interest are related – but not limited to – the application of acoustic emission (AE) testing, electromagnetic testing (ET), ground penetrating radar (GPR), geoelectric methods (GM), laser testing methods (LM), magnetic flux leakage (MFL), microwave testing, magnetic particle testing (MT), neutron radiographic testing (NR), radiographic testing (RT), thermal/infrared testing (IRT), ultrasonic testing (UT), seismic methods (SM), vibration analysis (VA), visual and optical testing (VT/OT).

The Session will focus on the application of different NDT methods and theories and will be related – but not limited to – the following investigation areas:
- advanced data fusion;
- advanced interpretation methods;
- design and development of new surveying equipment and prototypes;
- assessment and monitoring methods for material and site investigations;
- comprehensive and inclusive information data systems for the investigation of survey sites and materials;
- numerical simulation and modelling of data outputs with different physical quantities, scale domains and resolutions;
- advances in NDT methods, numerical developments and applications (stand-alone use of existing and state-of-the-art NDTs).

The session gathers geoscientific aspects such as dynamics, reactions, and environmental/health consequences of radioactive materials that are massively released accidentally (e.g., Chernobyl and Fukushima nuclear power plant accidents, wide fires, etc.) and by other human activities (e.g., nuclear tests).

The radioactive materials are known as polluting materials that are hazardous for human society, but are also ideal markers in understanding dynamics and physical/chemical/biological reactions chains in the environment. Thus, the radioactive contamination problem is multi-disciplinary. In fact, this topic involves regional and global transport and local reactions of radioactive materials through atmosphere, soil and water system, ocean, and organic and ecosystem, and its relation with human and non-human biota. The topic also involves hazard prediction and nowcast technology.

By combining 34 years (> halftime of Cesium 137) monitoring data after the Chernobyl Accident in 1986, 9 years dense measurement data by the most advanced instrumentation after the Fukushima Accident in 2011, and other events, we can improve our knowledgebase on the environmental behavior of radioactive materials and its environmental/biological impact. This should lead to improved monitoring systems in the future including emergency response systems, acute sampling/measurement methodology, and remediation schemes for any future nuclear accidents.

The following specific topics have traditionally been discussed:
(a) Atmospheric Science (emissions, transport, deposition, pollution);
(b) Hydrology (transport in surface and ground water system, soil-water interactions);
(c) Oceanology (transport, bio-system interaction);
(d) Soil System (transport, chemical interaction, transfer to organic system);
(e) Forestry;
(f) Natural Hazards (warning systems, health risk assessments, geophysical variability, countermeasure);
(g) Measurement Techniques (instrumentation, multipoint data measurements);
(h) Ecosystems (migration/decay of radionuclides).

The session consists of updated observations, new theoretical developments including simulations, and improved methods or tools which could improve observation and prediction capabilities during eventual future nuclear emergencies. New evaluations of existing tools, past nuclear contamination events and other data sets also welcome.

Public information:
Here is instruction of a live chat,
(1) Convener’s summary at the beginning of Chat 10:45-11:00
(2) We then go each presentation for 5 minutes including discussion.
(3) Each presenter posts their own "a few sentence summary within 80 words" in total, and the discussion. Omit any greeting to save time.
(4) To save time, we even offer to post your summary when we introduce your talk if you send me before hand
Live chat schedule
10:45 Convener summary
— we present one highlight slide from each presentation and give audience to search for presentation to deeply look into.
11:00 10066 Mykola Talerko et al
11:05 15257 Joffrey Dumont Le Brazidec et al
11:10 233 Sheng Fang et al
11:15 5844 Elena Korobova et al
11:20 2252 Misa Yasumiishi et al
11:25 13220 Yuichi Onda et al (solicited/Highlights)
11:30 13965 Fumiaki Makino et al
11:35 12301 Michio Aoyama et al
11:40 22136 Yasuhito Igarashi et al
11:45 12465 Hikaru Iida et al
11:50 19250 Mark Zheleznyak et al
11:55 12477 Yoshifumi Wakiyama et al
12:00 3175 Michio Aoyama et al (solicited)
12:05 11813 Yayoi Inomata and Michio Aoyama
12:10 12627 Daisuke Tsumune et al
12:15 21319 Susumu Yamada (Masahiko Machida) et al
12:20 6987 Hikaru Miura et al
12:25 Closing remark

The session gathers geoscientific aspects such as dynamics, reactions, and environmental/health consequences of radioactive materials that are massively released accidentally (e.g., Chernobyl and Fukushima nuclear power plant accidents, wide fires, etc.) and by other human activities (e.g., nuclear tests).

In addition to hazardous aspect for human society, the radioactive materials are used as ideal markers in understanding dynamics and physical/chemical/biological reactions chains in the environment. This multi-disciplinary session gathers all these aspect.

Data science, analytics and visualization technologies and methods emerge as significant capabilities for extracting insight from the ever growing volume and complexity of scientific data. The rapid advancement of these capabilities no doubt helps address a number of challenges and present new opportunities in improving Earth and Space science data usability. This session will highlight and discuss the novelty and strength of these emerging fields and technologies of these components, and their trends. We invite papers and presentations to examine and share the experience of:
- What benefits they offer to Earth and Space Science
- What science research challenges they address
- How they help transform science data into information and knowledge
- In what ways they can advance scientific research
- What lessons were learned in the development and infusion of these methods and technologies

Smart monitoring and observation systems for natural hazards, including satellites, seismometers, global networks, unmanned vehicles (e.g., UAV), and other linked devices, have become increasingly abundant. With these data, we observe the restless nature of our Earth and work towards improving our understanding of natural hazard processes such as landslides, debris flows, earthquakes, floods, storms, and tsunamis. The abundance of diverse measurements that we have now accumulated presents an opportunity for earth scientists to employ statistically driven approaches that speed up data processing, improve model forecasts, and give insights into the underlying physical processes. Such big-data approaches are supported by the wider scientific, computational, and statistical research communities who are constantly developing data science and machine learning techniques and software. Hence, data science and machine learning methods are rapidly impacting the fields of natural hazards and seismology. In this session, we will see research from natural hazards and seismology for processes over a broad range of time and spatial scales.

Dr. Pui Anantrasirichai of the University of Bristol, UK will give the invited presentation:
Application of Deep Learning to Detect Ground Deformation in InSAR Data