ALBERT

Description: The IRGC risk governance framework refers to an integrated concept on how to deal with a variety of physical, environmental and social risks characterized by complex, uncertain and ambiguous impacts. The framework has been informed by interdisciplinary research drawing from sociological and psychological research, including investigations about regulatory styles, institutional regimes and public participation. The notion of governance pertains to the many ways in which all relevant actors, individuals and institutions, public and private, deal with interventions that impact nature and human societies. It includes formal institutions and regimes as well as informal arrangements for including stakeholders and the public at large.

Description: Risk and uncertainty communication of often directed towards addressing the relevance and meaning of probability distributions over negative outcomes and statistical confidence intervals. As much as a professional handling of complex mathematical operations is needed, this will cover only a fraction of what uncertainty communication needs to addres. In those cases where estimates of uncertainty come close to educated guesses, simple beliefs or even total ignorance, other important cues may be more important than formal expertise in risk assessments. These include trust in the risk management agencies or other influential actors, personal experience, indigenous or community-based knowledge, common sense and argumentative or persuasive power. Once the full range of uncertainty is in the focus of risk managemem and communication institutions, an interdisciplinary, inclusive and demand-driven communication strategy needs to be developed. As recommended in the paper by Lofstedt and Bouder, such a targeted approach to risk and uncertainty communication requires interdisciplinary expertise, competence in the social and natural sciences, and practical experience in designing public discourses.

Description: In recent years, bottom–up civil society initiatives have advanced urban transformation processes in Berlin. Following previous research suggesting that bottom–up participation could have a positive impact on community resilience (CR), we analyse the impact of engagement on Berlin–based civil society initiatives. Whilst a positive effect on resilience can be found, we identify governance processes that would be necessary to enable the full potential of bottom–up participation for CR. Resilience, understood as the capacity of a community to thrive in times of change and uncertainty, is becoming increasingly important for the functioning of (urban) communities; hence, finding ways of strengthening it is deemed necessary.

Description: In the light of the major global crises such as pandemics, war, climate change and inequality, scientific research faces new challenges and targets. Scientific studies are supposed to provide background knowledge, to facilitate the desired transformation towards a sustainable future and to offer assistance for resolving complex problems that accompany societies in transition. Concepts such as transformative, transdisciplinary or co-creative research elucidate the direction in which scientific research finds its new role(s). Based on the discussion of these concepts and their different roots, the entry analyzes a new modular concept for a transdisciplinary scientific approach combining and integrating classic curiosity-driven research with goal oriented (advocacy) knowledge and catalytic, process-oriented expertise. In particular it emphasizes the role and function of curiosity-driven research for a comprehensive design of transdisciplinarity and transformative research. There is no doubt that society needs better transformative knowledge to facilitate actions towards sustainability but such knowledge rests on evidence-based insights from disciplinary and interdisciplinary research efforts. The entry provides an orientation of how this classic research outcomes can effectively be integrated into a transdisciplinary context.

Description: Cities and other human settlements are major contributors to climate change and are highly vulnerable to its impacts. They are also uniquely positioned to reduce greenhouse gas emissions and lead adaptation efforts. These compound challenges and opportunities require a comprehensive perspective on the public policy of human settlements. Drawing on core literature that has driven debate around cities and climate over recent decades, we put forward a set of boundary objects that can be applied to connect the knowledge of epistemic communities and support an integrated urbanism. We then use these boundary objects to develop the Goals-Intervention-Stakeholder-Enablers (GISE) framework for a public policy of human settlements that is both place-specific and provides insights and tools useful for climate action in cities and other human settlements worldwide. Using examples from Berlin, we apply this framework to show that climate mitigation and adaptation, public health, and well-being goals are closely linked and mutually supportive when a comprehensive approach to urban public policy is applied. © The Author(s) 2024.

Description: Automatic seismic data interpretation is a significant method in the exploration of geophysics. Complexities of the subsurface structures and the subsurface wave propagation media, make the decision-making process difficult in seismic data interpretation. Nevertheless, the extent of related knowledge and using the expert system method in seismic data interpretation can mitigate this problem. An expert system is a knowledge-based system that applies its knowledge in a complex and specific area and acts as an expert end-user consultant. This study investigates the design of an ANFIS expert system for mud diapirs detection with seismic data analysis in Gorgan plain. This method was applied to seismic attributes from a complex geological mud diapir bearing structure from south of the Caspian Sea. The south of the Caspian Sea is one of the richest area as petroleum reserves, and the Gorgan plain has various mud diapirs, which act as indicators of hydrocarbon reservoirs. The expert system design process to identify mud diapirs on seismic sections was modeled in two approaches including manual and automatic seismic data interpretation. In the first approach, the experience of the expert was collected by manual interpretation of training data and used to create a knowledge base and inference of the expert system in the second approach. The validation verified the accuracy of this method with an average accuracy of 90.1% according to using minimum knowledge to develop a knowledge base of the designed ANFIS expert system.

Description: Highlights • Accurate fault model can be built even when sparse drilling wells are available. • The multiresolution fault model provides information of faults with different sizes. • Fault model provides possibility of tectonic and fluid flow analysis simultaneously. • Modelling of faults in different scales, enable more accurate well path design. • The ANN provides optimized parameters for fault detection by ant tracking algorithm. Modelling faults plays a crucial step in the chain of studies through the first phase of the hydrocarbon exploration and its following studies in reservoir engineering, simulation and field development. This study introduces an innovative and automatic integrated approach that combines seismic multi-attributes and well data for faults modelling. The proposed strategy begins with extracting fault-related seismic attributes commonly used for seismic reservoir characterization. Chaos, variance and curvature attributes, typically highlight large-scale faults that shape the structural framework of the study field. In contrast, small-scale faults, influencing subsurface fluid flow in the fractured reservoir, are modeled using the ant-tracking algorithm applied to seismic data. Small-scale and large-scale fault models, then integrated with the conventional fault model to create an integrated discrete fracture network (DFN). This DFN model incorporates information on both large-scale and small-scale faults. The proposed strategy was applied on a geologically complex petroleum field in Iran. The results, validated using Formation Micro Imager (FMI) data, demonstrate accuracy of the integrated DFN model in comparison to conventional approaches on the studied filed, particularly in capturing small-scale faults. Consequently, it can be concluded that the proposed strategy provides a viable alternative for generating accurate DFN model.

Description: When created faithfully from real-world data, Digital 3D representations of objects can be useful for human or computer-assisted analysis. Such models can also serve for generating training data for machine learning approaches in settings where data is difficult to obtain or where too few training data exists, e.g. by providing novel views or images in varying conditions. While the vast amount of visual 3D reconstruction approaches focus on non-physical models, textured object surfaces or shapes, in this contribution we propose a volumetric reconstruction approach that obtains a physical model including the interior of partially translucent objects such as plankton or insects. Our technique photographs the object under different poses in front of a bright white light source and computes absorption and scattering per voxel. It can be interpreted as visual tomography that we solve by inverse raytracing. We additionally suggest a method to convert non-physical NeRF media into a physically-based volumetric grid for initialization and illustrate the usefulness of the approach using two real-world plankton validation sets, the lab-scanned models being finally also relighted and virtually submerged in a scenario with augmented medium and illumination conditions. Please visit the project homepage at www.marine.informatik.uni-kiel.de/go/vito