Deltaic areas are amidst the most favourable territories around the globe. Their strategic location and superior quality of their soils are core factors supporting both human development and the rise of these regions as global economic hubs. At the same time, deltas are extremely vulnerable to multiple threats from both climate change and the rush to urbanisation. These include an increased flood risk combined with the loss of ecological and social–cultural values. To ensure a more sustainable future for urbanising deltas, spatial strategies are needed to strengthen resilience, i.e. help the systems to cope with their vulnerabilities as well as enhance their capacity to overcome natural and anthropogenic threats. In this chapter, we outline the basic concepts and backgrounds of a joint research project with academic and societal partners called adaptive urban transformation. The objective of this research is to develop and test an integrative and multiscale design and planning approach for the adaptive urban transformation of urbanising deltas, in which the Pearl River Delta serves as a case study. In this approach, landscape-based regional design plays a key role in adaptive urban transformation, as well as innovative participation and visualisation techniques. Applications in urban design, planning, and governance in the PRD are also introduced. This chapter is foundational for the rest of the research presented in the chapters in this volume.@en

Adaptive urban transformations employ landscape-based regional design as an integrative and multiscale design and planning approach for sustainable urban development. In this approach, natural and urban dynamics as derived from systems analysis set the pace and nature of adaptation. This chapter presents an initial strategy for adaptive urban transformation in the Pearl River Delta to illustrate the potential of landscape-based regional design as form of territorial governance that takes the natural and urban landscape as the basis to steer urban–rural transformative processes through a combination of sector activities towards more coordinated sustainable outcomes. The strategic spatial plan and subsequent implementation by means of pilot projects will focus on the potential of interlinked economic and ecological development at multiple scales. Together with the assessment of urban landscape growth over time and the evaluation of current spatial development projects in the region, several significant factors of future development have been identified, leading to an initial strategic vision and transformation perspectives for the PRD. This vision is based on the idea that the PRD will develop into China's Silicon Valley, with strongly developed and well-connected urban qualities, robust green–blue frameworks, cultural-historical assets connected to the region and water-sensitive socio-ecological inclusive urbanism.@en

Flood resilience has aroused significant interest in coastal areas dealing with a growing frequency of severe rainstorms caused by climate change and urbanisation. At the core of flood resilience is the development of a resilient green-grey-blue infrastructure system that can resist, absorb, and recover from floods in a timely manner. Current flood resilience research, however, is limited to evaluating single infrastructure systems, failing to examine the dynamic process or find ideal spatial infrastructure designs for decision-makers. This research proposes a scenario-based assessment framework for integrated green-grey-blue infrastructure systems to improve flood resilience during urban design decision-making. Rainfall-runoff, drainage networks, and river system models are interlinked to provide quantitative simulation evaluations of water quantity and urban impact in various spatial organisations of infrastructure design. A dynamic, multi-criteria decision-making process is used to reveal the importance of five temporal indicators and rank design alternatives. In Guangzhou, China, the efficiency of this architecture is demonstrated on Pazhou Island, a typical river network area. Given the limited water and green space available, the results demonstrate that submerged areas exert a greater influence during peak rainfall, and blue infrastructure storage becomes an essential factor following rainfall. Furthermore, from a spatial perspective, the looped network of green-blue infrastructure enhances flood resilience, and downstream waterway connections and green space-aligned waterways boost the water storage capacity of green-grey-blue infrastructure. This paradigm can improve flood resilience in the Greater Bay Area in the future, especially in response to heavy rainstorms and river floods.

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The state-of-the-art joint migration inversion faces the so-called amplitude-versus-offset challenge, due to adopting over-simplified one-way propagation, reflection and transmission operators to avoid over-parameterization in the inversion process. To overcome this challenge, we apply joint migration inversion to horizontally layered media (or 1.5-dimensional media) and parameterize the solution space via density and velocity models. In this scenario, one-way propagation, reflection and transmission operators required by the joint migration inversion process can be analytically and correctly derived from the subsurface models, so the amplitude-versus-offset challenge is successfully overcome. We introduce a new concept, which is named ‘inverse propagation’, into our 1.5-dimensional amplitude-versus-offset joint migration inversion. It can correctly reconstruct subsurface wavefields by using a surface-recorded receiver wavefield with all the influence of transmission, reflection and multiples accounted for. A synthetic example is used to demonstrate the correctness of the inverse propagation. This work is the foundation to further develop the 1.5-dimensional amplitude-versus-offset joint migration inversion technology.

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Simulation-Based Multi-Objective Optimization (SBMOO) methods are being increasingly used in conceptual architectural design. They mostly focus on the solving, rather than the re-formulation, of a Multi-Objective Optimization (MOO) problem. However, Optimization Problem Re-Formulation (Re-OPF) is necessary for treating ill-defined conceptual architectural design as an iterative exploration process. The paper proposes an innovative SBMOO method which builds in a dynamic and interactive Re-OPF phase. This Re-OPF phase, as the main novelty of the proposed method, aims at achieving a realistic MOO model (i.e., a parametric geometry-simulation model which includes important objectives, constraints, and design variables). The proposed method is applied to the conceptual design of a top-daylighting system, focusing on divergent concept generation. The integration of software tools Grasshopper and modeFRONTIER is adopted to support this application. The main finding from this application is that the proposed method can help to achieve quantitatively better and qualitatively more diverse Pareto solutions.

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Preparing cities for sea-level rise is one of the critical challenges of the twenty-first century. Extreme weather events, natural hazards, and the failure of climate mitigation and adaptation are substantial risks. These risks are especially significant in fast-urbanizing deltas, such as the Pearl River Delta in China, because the conflict between urbanization and flooding caused by climate change will be more significant in the future. This paper elaborates on an approach that employs a future land-use simulation (FLUS) model for scenario-based 100-year coastal flood risk assessment. Storylines of future scenarios from the Intergovernmental Panel on Climate Change (IPCC), called the representative concentration pathways (RCPs) 2.6 and 8.5, are utilized in the present study. The Guangzhou Metropolitan Area (GMA) is used as a case study to explore the probable implications of future land-use changes due to the ongoing urbanization process in the region in relation to projected environmental changes (sea-level rise, storm surge, and land subsidence). The results indicate that there will be a significant increase in flooded urban areas in the future. The simulations show that, as compared to 2015, the built-up area in the GMA will increase by 246.57 km² in 2030 and 513.03 km² in 2050. As compared to 2015, the flooding of built-up areas in 2030 and 2050 will respectively increase by about 31.32 km² and 48.49 km² under the RCP 8.5 scenario. It is also found that, as the main driving factor, urbanization will increase the flooding of built-up areas in Guangzhou in 2030 and 2050 by about 1.9 km² and 5.9 km², respectively, under the RCP 2.6 scenario as compared to 2015. Additionally, due to environmental changes, the flooding of built-up areas in Guangzhou will increase by about 24.2 km² and 26.8 km², respectively, under the RCP 8.5 scenario by 2030 and 2050 as compared to 2015. This increasing flood risk information determined by the simulation provides insight into the spatial distribution of future flood-prone urban areas to facilitate the development and prioritization of flood mitigation measures at the most critical locations in the region.

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Joint migration inversion is a recently proposed technology, accommodating velocity model building and seismic migration in one integrated process. Different from the widely accepted full waveform inversion technology, it uses imaging parameters, i.e. velocities and reflectivities of the subsurface, to parameterize its solution space. The unique feature of this new technology is its explicit capability to exploit multiples in its inversion scheme, which are treated as noise by most current technologies. In this paper, we comprehensively evaluate the state-of-the-art joint migration inversion technology from various angles: we first benchmark its performance, on both velocity model building and seismic imaging, against that of the well-accepted workflow comprising full waveform inversion and reverse-time migration using a fully controlled 2D realistic synthetic dataset. Next, we demonstrate its application on a 2D field dataset. Last, we use another 2D synthetic dataset to clearly illustrate the challenges the current joint migration inversion technology is facing. With this paper, we transparently reveal the pros of cons of the current joint migration inversion, and we will also point out the imminent research directions joint migration inversion technology should focus on in the next phase for it to be more widely accepted by the geophysics community.

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Indoor multi-functional sports arenas are a complex building type. Integration of the (multi-) functional space and of the large-span structure of the roof mainly determines the overall geometry of the building, and is one of the most challenging phases of the design. Several interdisciplinary numeric assessments and numerous solutions with diverse geometries (rather than just several specific types) should be considered to make informed design decisions. To support the design exploration in the early design stage for multi-functional arenas, this paper proposes a design process that is composed of a flexible parametric model, a framework of interdisciplinary assessment criteria, and multi-objective optimization (MOO) with post-process tools. The parametric model is defined based on the basic spatial composition of arenas and is flexible to provide a broader design space, including diverse solutions with three frequently-used structural types. The framework of assessment criteria includes indicators of viewing quality for spectators, acoustics, and structures, which can evaluate the design in different aspects. Based on certain assessment criteria, the MOO can be used to search for good designs in the broader space, and the post-process tools facilitate the designer to analyse the results. Two typical arenas (the Barclay Centre and the O2 Arena) are selected as real case studies to demonstrate the proposed process and assess the capacity. Results of the case studies validate the efficacy of the process and the necessity of the broader design space to include diverse solutions with multiple structural types.

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Indoor sports arenas are a kind of important public buildings, which require iconic architectural forms and well performing structures for the long-span roofs. Hence, during the early stage of an arena design, it is crucial for designers to define a proper building form based on integrated design exploration of both geometric typology and structural performance. To support such design exploration, this paper proposes a method based on SOM (self-organizing map)-clustering and structural performance simulation as well as SAG (Sports Arena Generator: a specific parametric model for arenas proposed by the authors). This method can support designers to explore designs according to both geometries and performance and also illustrate the relationships between geometric typology and specific performance values, which is crucial for both architectural design and the research about building performance. A hypothetic arena is used as an example to demonstrate and validate the method.@en

Joint migration inversion (JMI) is a recently developed technology that aims at incorporating the seismic velocity model update and acoustic migration, including all multiple scatterings, into one closed-loop process. Full waveform inversion (FWI) is a commonly accepted technology for velocity model building, and reverse-time migration (RTM) is the main method adopted for depth imaging. In this paper we first use a 2D realistic deep water model to benchmark JMI against a workflow of FWI combined with RTM. With some insights on JMI gained from this comparison study, we point out that a good niche for JMI is to provide a high quality initial velocity model. We further demonstrate the performance of JMI on a 2D deep water field dataset with an initial velocity model derived from migration velocity analysis (MVA).

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Although joint migration inversion has been proposed for several years, a thorough derivation and description of the involved gradients was not published. In this paper, we derive the gradient of both the angle-independent reflectivity and the velocity in a framework of acoustic angle-independent joint migration inversion. With some further approximations taken, the conclusions shown in previous publications can also be reached from our new derivation.

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In this paper, we propose a novel three-dimensional receiver deghosting algorithm that is capable of deghosting both horizontal and slanted streamer data in a theoretically consistent manner. Our algorithm honours wave propagation phenomena in a true three-dimensional sense and frames the three-dimensional receiver deghosting problem as a Lasso problem. The ultimate goal is to minimise the mismatch between the actual measurements and the simulated wavefield with an L1 constraint applied in
the extended Radon space to handle the underdetermined nature of this problem. We successfully demonstrate our algorithm on a modified three-dimensional EAGE/SEG
Overthrust model and a Red Sea marine dataset.@en

The Rayleigh integral solution of the acoustic Helmholtz equation in a homogeneous medium can only be applied when the integral surface is a planar surface, while in reality almost all surfaces where pressure waves are measured exhibit some curvature. In this paper we derive a theoretically rigorous way of building propagation operators for pressure waves on an arbitrarily curved surface. Our theory is still based upon the Rayleigh integral, but it resorts to matrix inversion to overcome the limitations faced by the Rayleigh integral. Three examples are used to demonstrate the correctness of our theory – propagation of pressure waves acquired on an arbitrarily curved surface to a planar surface, on an arbitrarily curved surface to another arbitrarily curved surface, and on a spherical cap to a planar surface, and results agree well with the analytical solutions. The generalization of our method for particle velocities and the calculation cost of our method are also discussed.@en

The Santos basin in Brazil suffers from strong internal multiples that overlap with primaries from the pre-salt reservoirs. We propose an adaptive double-focusing method for the removal of these multiples to obtain a correct image of the target area. The proposed method applies a form of source-receiver Marchenko redatuming to the reflection response. The Marchenko method is used to achieve single-focusing, after which we convolve the retrieved downgoing focusing function and the upgoing Green’s function to create double-focusing. This results in a base image that contains both primaries and internal multiples, and two models that predict the strongest internal multiples. Next, adaptive subtraction in the curvelet domain is used to remove these multiples from the base image. Some multiple interactions between the target area and the overburden remain, but we gain a robust method that is capable of dealing with a sparse acquisition geometry and imperfections in the (pre-processed) data. Also, this method is straightforward to implement and can be parallelized over pairs of focal points. These properties make adaptive double-focusing particularly suitable for the application to large volumes of field data. Tests on 2D field data and 3D field data show that the proposed method correctly predicts and removes the strongest internal multiples from the overburden, resulting in a clear improvement of the geological interpretability in the target area.@en

The multi-functional space of sports arena is highly related to the long-span structure. To support the integration of these two aspects, design optimization combining parametric modeling, performance simulations, and searching algorithm can be used. However, optimization is powerful in dealing with quantitative performance, but for some soft requirements on buildings, design exploration of geometries based on the judgments of architects is still necessary. Self-organizing map (SOM), as a model-based clustering algorithm, can be used to support this kind of explorations on geometric typology. Nevertheless, it is difficult to ensure the accuracy of clustering, especially for complex parametric models. To support the design exploration on geometry (besides the exploration on quantitative performance supported by optimization) during the conceptual design of sports arenas, this paper proposed a process based on a versatile and flexible parametric model for sports arenas and self-organizing map (SOM). Within this process, to increase the accuracy of SOM clustering, a pre-processing step for the parameters of design alternatives is also proposed. A design of a hypothetic sports arena is used as a case to demonstrate and verify the process.@en

The comparison of various competing design concepts during conceptual architectural design is commonly needed for achieving a good final concept. For this, computational design exploration is a key approach. Unfortunately, most of existing research tends to skip this crucial process, and purely focuses on the late-stage design optimization based on a single concept that, they assume, has been good enough or accepted already. This paper focuses on information or knowledge extracted from a multi-objective design exploration for the formulation of a good geometrical building design concept. To better support the exploration process, a new integration plug-in is developed to integrate parametric modelling software and process integration and optimization software. Through a case study that investigates the daylight and energy performances of a large indoor space, this paper 1) tackles the importance of design exploration on the formulation of a good design concept; 2) presents and shows the usability of the new integration plug-in for supporting the exploration process.@en

Near-surface problem is a common challenge faced by land seismic data processing, where often, due to near-surface anomalies, events of interest are obscured. One method to handle this challenge is near-surface layer replacement, which is a wavefield reconstruction process based on downward wavefield extrapolation with the near-surface velocity model and upward wavefield extrapolation with a replacement velocity model. This requires, in theory, that the original wavefield should be densely sampled. In reality, data acquisition is always sparse due to economic reasons, and as a result in the near-surface layer replacement data interpolation should be resorted to. For datasets with near-surface challenges, because of the complex event behaviour, a suitable interpolation scheme by itself is a challenging problem, and this, in turn, makes it difficult to carry out the near-surface layer replacement. In this research note, we first point out that the final objective of the near-surface layer replacement is not to obtain a newly reconstructed wavefield but to obtain a better final image. Next, based upon this finding, we propose a new thinking, interpolation-free near-surface layer replacement, which can handle complex datasets without any interpolation. Data volume expansion is the key idea, and with its help, the interpolation-free near-surface layer replacement is capable of preserving the valuable information of areas of interest in the original dataset. Two datasets, i.e., a two-dimensional synthetic dataset and a three-dimensional field dataset, are used to demonstrate this idea. One conclusion that can be drawn is that an attempt to interpolate data before layer replacement may deteriorate the final image after layer replacement, whereas interpolation-free near-surface layer replacement preserves all image details in the subsurface.

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