Complex Structural Environments Research Articles

Sampling forests with terrestrial laser scanning.

Background and AimsTerrestrial laser scanners (TLSs) have successfully captured various properties of individual trees and have potential to further increase the quality and efficiency of forest surveys. However, TLSs are limited to line of sight observations, and forests are complex structural environments that can occlude TLS beams and thereby cause incomplete TLS samples. We evaluate the prevalence and sources of occlusion that limit line of sight to forest stems for TLS scans, assess the impacts of TLS sample incompleteness, and evaluate sampling strategies and data analysis techniques aimed at improving sample quality and representativeness.MethodsWe use a large number of TLS scans (761), taken across a 255 650-m2 area of forest with detailed field survey data: the Harvard Forest Global Earth Observatory (ForestGEO) (MA, USA). Sets of TLS returns are matched to stem positions in the field surveys to derive TLS-observed stem sets, which are compared with two additional stem sets derived solely from the field survey data: a set of stems within a fixed range from the TLS and a set of stems based on 2-D modelling of line of sight. Stem counts and densities are compared between the stem sets, and four alternative derivations of area to correct stem densities for the effects of occlusion are evaluated. Representation of diameter at breast height and species, drawn from the field survey data, are also compared between the stem sets.Key ResultsOcclusion from non-stem sources was the major influence on TLS line of sight. Transect and point TLS samples demonstrated better representativeness of some stem properties than did plots. Deriving sampled area from TLS scans improved estimates of stem density.ConclusionsTLS sampling efforts should consider alternative sampling strategies and move towards in-progress assessment of sample quality and dynamic adaptation of sampling.

Milliscale Substrate Curvature Promotes Myoblast Self‐Organization and Differentiation

Biological tissues comprise complex structural environments known to influence cell behavior via multiple interdependent sensing and transduction mechanisms. Yet, and despite the predominantly nonplanar geometry of these environments, the impact of tissue-size (milliscale) curvature on cell behavior is largely overlooked or underestimated. This study explores how concave, hemicylinder-shaped surfaces 3-50mm in diameter affect the migration, proliferation, orientation, and differentiation of C2C12 myoblasts. Notably, these milliscale cues significantly affect cell responses compared with planar substrates, with myoblasts grown on surfaces 7.5-15mm in diameter showing prevalent migration and alignment parallel to the curvature axis. Moreover, surfaces within this curvature range promote myoblast differentiation and the formation of denser, more compact tissues comprising highly oriented multinucleated myotubes. Based on the similarity of effects, it is further proposed that myoblast susceptibility to substrate curvature depends on mechanotransduction signaling. This model thus supports the notion that cellular responses to substrate curvature and compliance share the same molecular pathways and that control of cell behavior can be achieved via modulation of either individual parameter or in combination. This correlation is relevant for elucidating how muscle tissue forms and heals, as well as for designing better biomaterials and more appropriate cell-surface interfaces.

Automated detection of koalas using low-level aerial surveillance and machine learning

Effective wildlife management relies on the accurate and precise detection of individual animals. These can be challenging data to collect for many cryptic species, particularly those that live in complex structural environments. This study introduces a new automated method for detection using published object detection algorithms to detect their heat signatures in RPAS-derived thermal imaging. As an initial case study we used this new approach to detect koalas (Phascolarctus cinereus), and validated the approach using ground surveys of tracked radio-collared koalas in Petrie, Queensland. The automated method yielded a higher probability of detection (68–100%), higher precision (43–71%), lower root mean square error (RMSE), and lower mean absolute error (MAE) than manual assessment of the RPAS-derived thermal imagery in a comparable amount of time. This new approach allows for more reliable, less invasive detection of koalas in their natural habitat. This new detection methodology has great potential to inform and improve management decisions for threatened species, and other difficult to survey species.

Mesoscale substrate curvature overrules nanoscale contact guidance to direct bone marrow stromal cell migration.

The intrinsic architecture of biological tissues and of implanted biomaterials provides cells with large-scale geometrical cues. To understand how cells are able to sense and respond to complex structural environments, a deeper insight into the cellular response to multi-scale and conflicting geometrical cues is needed. In this study, we subjected human bone marrow stromal cells (hBMSCs) to mesoscale cylindrical surfaces (diameter 250-5000 µm) and nanoscale collagen fibrils (diameter 100-200 nm) that were aligned perpendicular to the cylinder axis. On flat surfaces and at low substrate curvatures (cylinder diameter d > 1000 µm), cell alignment and migration were governed by the nanoscale collagen fibrils, consistent with the contact guidance effect. With increasing surface curvature (decreasing cylinder diameter, d < 1000 µm), cells increasingly aligned and migrated along the cylinder axis, i.e. the direction of zero curvature. An increase in phosphorylated myosin light chain levels was observed with increasing substrate curvature, suggesting a link between substrate-induced cell bending and the F-actin-myosin machinery. Taken together, this work demonstrates that geometrical cues of up to 10× cell size can play a dominant role in directing hBMSC alignment and migration and that the effect of nanoscale contact guidance can even be overruled by mesoscale curvature guidance.

Interpretative filtering

Industry already acknowledges the power of dip-steered median filters to clean-up seismic data volume in which coherent events are enhanced and random noise is reduced. By combining two powerful technologies, this paper presents a dip-steered geostatistical filtering solution, called interpretative filtering (IF), which gives remarkable results for removing random and organised noise from seismic volumes. It defines a new generation of spatial filters useful for processing and interpretation. The IF solution is based on a 3D non-stationary factorial kriging technique (M-GS technology) driven by a high-resolution interpretation of the seismic volume coming from a Relative Geological Time model. This new technique enables some non-stationary noise to be dealt with. For example acquisition footprints are well known to be non-stationary noises, with varying orientation, width and intensity as a function of the position in the volume. IF opens the way to refine filtering operations of seismic volumes even in complex structural environments. For this paper, the solution is applied to an open-file 3D marine seismic dataset (HCA2000A 3D) covering the Exmouth sub-basin, North West Shelf, Australia. This survey presents a dense fault system, mostly intersecting the Jurassic and the Cretaceous. Results show a great improvement on reducing the acquisition footprint and random noise without compromising the definition of the subtle geological features.

Harmonising diverse 3D geometries in a hard rock environment for pre-stack imaging

Four 3D seismic surveys were acquired by HiSeis Pty Ltd over two years across an existing mining camp. The main objective of these seismic surveys was definition of structures which could assist characterization of mineralised zone that consists mainly of massive sulphides. Initial processing of seismic data started shortly after acquisition in 2011. Preliminary products have shown great promise in resolving complex structural environments and showed potential for direct targeting form seismic data. Our motive for reprocessing the dataset was in integration of all 2011 and 2012 seismic data in an amplitude consistent routine which could bring new value for amplitude based analysis of massive sulphide bodies. Conventional 3D deep-move out corrections (DMO), followed by a post-stack migration algorithm has not been successful in merging diverse datasets. Considerable improvement was achieved through the application of pre-stack time migration (PSTM) algorithm. This allowed us to use unique bin size for all merged seismic surveys. Successful imaging of merged datasets has been challenged. The main reason was that 3D seismic datasets had vastly diverse offset and azimuth distribution. Highly irregular migration fold coverage is an obvious problem which had to be overcome by exclusion of unsuccessful imaged events prior to stacking.

Development of Generic Methodology for Designing a Structural Health Monitoring Installation Based on the Acoustic Emission Technique

The Acoustic Emission (AE) technique can be used to perform damage detection and localisation for structural health monitoring purposes. Implementation in aircraft structures however poses a significant challenge as its performance in terms of damage detection and localisation is not well understood when used with complex structural geometries and variable operational service environments. This paper presents initial developments towards a generic methodology for optimal design of a structural health monitoring installation based on the acoustic emission technique. Performance verification of the AE monitoring process was classified into two stages. The first is a mainly empirical process for quantitatively characterising AE generation from damage using the Probability of Hit (POH) metric developed and presented in this study. The second is a combination of mathematical, numerical and empirical modeling to characterise AE propagation and detection which can also be used to determine optimal system configuration and sensor design. It was found that for Structural Health Monitoring (SHM) techniques to be part of wider structural integrity programmes, there is need for standards that recommend best practices as well as providing specification of acceptable levels of performance in terms of damage detection and location.

Target-oriented beamlet migration based on Gabor-Daubechies frame decomposition

We develop beamlet propagation and imaging using Gabor-Daubechies (G-D) frame decomposition based on local perturbation theory and apply it to target-oriented prestack depth migration. The method is formulated with local background velocities and local perturbations in wavefield extrapolation. The localized propagators and phase-correction operators are obtained analytically or semianalytically by one-way operator decomposition and screen approximation in the beamlet and space-beamlet mixed domain. Beamlet wavefields have superior localization properties in both local space and direction (wave-number) over Gaussian beams in the sense that localizations are not limited within short propagation distances in either homogeneous or heterogeneous media. Comparisons of the prestack depth-migrated images for the 2D SEG-EAGE salt model and the Marmousi model indicate that, for seismic-wave propagation and imaging in complex structural environments, the G-D beamlet propagator has higher accuracy and better wide-angle properties than do global propagators. Target-oriented prestack G-D beamlet migration is performed by means of local-angle-domain imaging and controlled superposition of common-dip-angle images based on the local directivity features of the target structures. This considers the spatial and direction localizations of beamlets. As a numerical example, we process the 2D SEG-EAGE salt-model prestack data. The results show that the proposed migration method has considerable advantages in suppressing noise and enhancing structural features. Image quality for subsalt structures, especially for steep faults, is improved through structure-based superposition of common-angle images. This demonstrates the potential and capability of beamlet migration in target-oriented seismic imaging.

Spectral parametrization of wave scattering from a rigid aperture coupled enclosure with interior loading

Wave interaction with complex structural environments can be analyzed by regarding the overall response as the response from coupled subsystems. The decomposition into subsystems is wavenumber-scale dependent, and the “best” classification involves weak coupling between subsystems but strong coupling of wave types within a subsystem. How to effect the classification is illustrated here for a cylindrical thin smoothly convex rigid shell, which grants access to an enclosed cylindrical convex rigid interior load via a narrow-slit aperture. The geometry suggests a parametrization of exterior wave phenomena by geometrically reflected, slit diffracted, and surface guided creeping waves, and of interior wave phenomena by guided local modes in the nonuniform waveguide formed by the region between the shell and interior boundary. The slit produces coupling among the exterior wave types and among the interior wave types, as well as between the exterior and interior. These couplings cause spatial wavenumber conversions from trapped, to radiating, etc. A system of state vectors, propagation matrices, and coupling matrices renders the overall description self-consistent, and permits the development of reduced forms by loading a significant (for example, resonant) subsystem with nonresonant remainders. The dynamics of the parametrization is illustrated by considering the numerical example of plane wave scattering as a function of aspect and frequency when the interior and exterior boundaries are eccentric circular cylinders. [Work supported by ONR and David Taylor NRDC.]

Edge-Detection Processing in Finding Subtle Oil Traps in Complex Stratigraphic and Structural Environments: ABSTRACT

New algorithms aimed at seismic signal processing show promise in detecting subtle hydrocarbon traps in offshore margin environments. Stratigraphic and structural traps associated with diapirism in particular are targets that can be discerned by edge-detection processing methods. A real-time, high-speed, parallel pipelined processing system has been used to perform two- and three-dimensional migration and edge detection. The system uses real-time interactive graphics and inputs from a high-resolution three-dimensional seismic array that digitizes signals over a dynamic range of 120 db. The data input is converted to IEEE 32-bit floating point prior to processing. The relative amplitude, velocity, and energy spectrum of each data point can be determined over a broad section of data. The authors believe that geophysical data processing of this type is essential if detailed geologic features are to be clearly understood using seismic reflection remote sensing. Subtle hydrocarbon deposits will be continually overlooked until a detailed approach such as this is employed. They believe the research at hand will begin a new era in reflection data acquisition, processing, and interpretation to look specifically for minor accumulations of petroleum in complex stratigraphic and structural environments.