Guus Berkhout

Connecting technological capabilities with market needs using a cyclic innovation model

One of the key limitations of current models of innovation is that they still represent variations on the familiar pipeline architecture. In addition, they are not embedded in the strategic issues of company boards and therefore remain isolated entities. Equally, the activity of entrepreneurship, while long recognized as a key factor in firm innovation management, is not captured and is inadvertently understated or only implied at best. We find that there is incongruence between current theoretical models and innovation practice. We offer a socio-technical framework that replaces the family of linear concepts by a cyclic alternative. It combines hard and soft sciences, bridges research and development and marketing communities and helps firms and policy makers to better understand the iterative nature of the innovation process.

Parameterization of seismic data using gridpoint responses

Seismic reflection wavefields can be described by three fundamental steps: downward propagation, reflection and upward propagation (WRW). Hence, complexity of the wavefields is caused by the combination of propagation and reflection properties. Many algorithms for preprocessing and imaging rely on some parameterization of the wavefields, using mathematically convenient basis functions, such as plane waves, parabolas, wavelets and curvelets. However, none of these are very adequate for describing complex wavefields without a large amount of basis function parameters. In this paper it is proposed to use a more physically-based parameterization in terms of gridpoint responses (GPRs). In our parameter estimation process, the wavefield of each gridpoint source is propagated towards the source and receiver coordinates of the acquisition surface in order to match the measured data. If introduced properly, the propagation operators need only to be approximate (the background velocity model) without losing the ability to fully describe the data. An important aspect of this double (‘bi-focal’) transform is the fact that no imaging condition is applied. Furthermore, this parameterization of seismic data can be easily extended to include surface-related multiples as well.

Multi‐scattering illumination in blended acquisition design

In traditional seismic surveys the firing time between shots is such that the records do not interfere in time. However, in the concept of blending the records do overlap, allowing much denser and wider geometries in an economic way. The blending parameters are the locations of the involved sources and their time delays (or a more complex code). A denser shot sampling and a wider aperture make that each subsurface gridpoint is illuminated from a larger number of angles and will therefore improve the image quality in terms of resolution and signal-to-noise ratio. In a next step, the illumination can be further improved by utilizing the surface-related multiples. This means that multiples can be exploited to improve the incident wavefield, e.g., by filling angle gaps in the illumination, or extending the range of angles. In this way the energy contained in the multiples now contributes to the image, rather than making it noisy. To study the illumination roperties of blended primaries and multiples, the incident wavefield at subsurface gridpoints is assessed. The incident wavefield at a specific gridpoint is a dispersed time series with a ‘complex code’, even with very simple blending parameters like time delays. This paper introduces a framework that quantitavely connects the blended source configuration at the surface with the properties of the incident wavefield in the subsurface. The spectral properties of the ‘complex code’ in the subsurface form the basis for our acquisition geometry analysis and design. In addition, the contribution of surface-multiples as illuminating wavefields is included in the design.

Blended Acquisition with Optimized Dispersed Source Arrays

Until now, blended source arrays are configured with equal source units: ‘homogeneous blending’. We propose to extend the blending concept to inhomogeneous blending, meaning that a blended source array consists of different, narrowband source units with different central frequencies.

Double Illumination In Blended Acquisition

In traditional seismic surveys, interference between shot records is undesired. Therefore, the temporal interval and/or the distance between shots is chosen sufficiently large. However, in the concept of blended acquisition (also called simultaneous acquisition) the records do overlap, allowing denser source sampling and wider azimuths in an economic way. Denser source sampling and wider azimuths make that each subsurface gridpoint is illuminated from a larger number of angles and will, therefore, improve the image quality in terms of signal-to-noise ratio and spatial resolution. We show that even with very simple blending parameters like time delays the incident wavefield at a specific subsurface gridpoint represents a dispersed time series with a ‘complex code’. For shot record migration purposes, this time series must have a stable inverse. The illumination can be further improved by utilizing the surface-related multiples: ‘double illumination’. These multiples fill angle gaps in the illumination and/or extend the range of angles. Note that the energy contained in the multiples now contributes to the image, rather than decreasing its quality. One remarkable consequence of this property is that detector geometries play an important role in strengthening incident wavefields. We show how to quantify the contribution of the blended surface multiples to the illuminating wavefield for a blended source configuration. Results confirm that the combination of blending and multiple scattering increases the illumination quality in an economically attractive way and, therefore, will improve the quality of shot record migration results.

Chapter 1 Innovation in a Historical Perspective

In order to understand todays innovation models, we need to look at the historical development of these models. This chapter describes the succession of the R&D management generations and discusses the innovation models in each generation (Section 2). The shortcomings of these models and the requirements for improved versions are summarized in Section 3. In Section 4, we will explain why new models of innovation should be circular and multi-layered.

Connecting technological capabilities with market needs using a cyclic innovation model: Connecting technological capabilities with market needs using CIM

One of the key limitations of current models of innovation is that they still represent variations on the familiar pipeline architecture. In addition, they are not embedded in the strategic issues of company boards and therefore remain isolated entities. Equally, the activity of entrepreneurship, while long recognized as a key factor in firm innovation management, is not captured and is inadvertently understated or only implied at best. We find that there is incongruence between current theoretical models and innovation practice. We offer a socio-technical framework that replaces the family of linear concepts by a cyclic alternative. It combines hard and soft sciences, bridges research and development and marketing communities and helps firms and policy makers to better understand the iterative nature of the innovation process.

Connecting technological capabilities with market needs with a cyclic model of innovation

One of the key limitations of current models of innovation is that they still represent variations on the familiar pipeline architecture. In addition, they are not embedded in the strategic issues of company boards and therefore remain isolated entities. Equally, the activity of entrepreneurship, while long recognized as a key factor in firm innovation management, is not captured and is inadvertently understated or only implied at best. We find that there is incongruence between current theoretical models and innovation practice. We offer a socio-technical framework that replaces the family of linear concepts by a cyclic alternative. It combines hard and soft sciences, bridges research and development and marketing communities and helps firms and policy makers to better understand the iterative nature of the innovation process.

Chapter 8 CIM and Thixomolding

Thixomolding® refers to a new technology to mold a magnesium alloy in elaborate forms. The actors that introduced this technology in the Netherlands first operated on a regional level. With the support of the Cyclic Innovation Model (CIM), the innovation system was able to evolve by developing new innovations, although initially Class 1 and 2 type of innovations. In the future, the Thixomolding® innovation system will compete on a European scale, and it is expected that products will be developed for many different industries.

Chapter 4 Innovation Takes Time: The Role of Futures Research in CIM

The duration of an innovation process, from new idea to new business, may take many years. This makes it necessary to incorporate a vision of the future. The Cyclic Innovation Model (CIM) shows that aspects such as multiplicity (looking at multi-fold futures) and multidimensionality (looking at different aspects of the future) should be taken into account. Looking at the different actors involved in CIM, the future should be researched with an open mind (meaning that the transition path to the future should be kept wide open) and different time horizons should be taken into account.