Acoustic monitoring of gas emissions from the seafloor. Part II: a case study from the Sea of Marmara     

Gaye Bayrakci  Carla Scalabrin  Stéphanie Dupré  Isabelle Leblond  Jean-Baptiste Tary  Nadine Lanteri  Jean-Marie Augustin  Laurent Berger  Estelle Cros  André Ogor  Christos Tsabaris  Marc Lescanne  Louis Géli 《Marine Geophysical Researches》2014,35(3):211-229

A rotating, acoustic gas bubble detector, BOB (Bubble OBservatory) module was deployed during two surveys, conducted in 2009 and 2011 respectively, to study the temporal variations of gas emissions from the Marmara seafloor, along the North Anatolian Fault zone. The echosounder mounted on the instrument insonifies an angular sector of 7° during a given duration (of about 1 h). Then it rotates to the next, near-by angular sector and so forth. When the full angular domain is insonified, the “pan and tilt system” rotates back to its initial position, in order to start a new cycle (of about 1 day). The acoustic data reveal that gas emission is not a steady process, with observed temporal variations ranging between a few minutes and 24 h (from one cycle to the other). Echo-integration and inversion performed on the acoustic data as described in the companion paper of Leblond et al. (Mar Geophys Res, 2014), also indicate important variations in, respectively, the target strength and the volumetric flow rates of individual sources. However, the observed temporal variations may not be related to the properties of the gas source only, but reflect possible variations in sea-bottom currents, which could deviate the bubble train towards the neighboring sector. During the 2011 survey, a 4-component ocean bottom seismometer (OBS) was co-located at the seafloor, 59 m away from the BOB module. The acoustic data from our rotating, monitoring system support, but do not provide undisputable evidence to confirm, the hypothesis formulated by Tary et al. (2012), that the short-duration, non-seismic micro-events recorded by the OBS are likely produced by gas-related processes within the near seabed sediments. Hence, the use of a multibeam echosounder, or of several split beam echosounders should be preferred to rotating systems, for future experiments.  相似文献   

Local and controlled prestack depth migration in complex areas     

Isabelle Lecomte 《Geophysical Prospecting》1999,47(6):799-818

Prestack depth migrations based on wavefield extrapolation may be computationally expensive, especially in 3D. They are also very dependent on the acquisition geometry and are not flexible regarding the geometry of the imaging zone. Moreover, they do not deal with all types of wave, considering only primary reflection events through the model. Integral approaches using precalculated Green's functions, such as Kirchhoff migration and Born-based imaging, may overcome these problems. In the present paper, both finite-difference traveltimes and wavefront construction are used to obtain asymptotic Green's functions, and a generalized diffraction tomography is applied as an example of Born-based acoustic imaging. Target-orientated imaging is easy to perform, from any type of survey and subselection of shot/receiver pairs. Multifield imaging is possible using Green's functions that take into account, for instance, reflections at model boundaries. This may help to recover parts of complex structures which would be missing using a paraxial wave equation approach. Finally, a numerical evaluation of the resolution, or point-spread, function at any point of the depth-migrated section provides valuable information, either at the survey planning stage or for the interpretation.  相似文献   

Integrating facies-based Bayesian inversion and supervised machine learning for petro-facies characterization in the Snadd Formation of the Goliat Field,south-western Barents Sea     

Honoré Yenwongfai  Nazmul Haque Mondol  Isabelle Lecomte  Jan Inge Faleide  Johan Leutscher 《Geophysical Prospecting》2019,67(4):1020-1039

Seismic petro-facies characterization in low net-to-gross reservoirs with poor reservoir properties such as the Snadd Formation in the Goliat field requires a multidisciplinary approach. This is especially important when the elastic properties of the desired petro-facies significantly overlap. Pore fluid corrected endmember sand and shale depth trends have been used to generate stochastic forward models for different lithology and fluid combinations in order to assess the degree of separation of different petro-facies. Subsequently, a spectral decomposition and blending of selected frequency volumes reveal some seismic fluvial geomorphological features. We then jointly inverted for impedance and facies within a Bayesian framework using facies-dependent rock physics depth trends as input. The results from the inversion are then integrated into a supervised machine learning neural network for effective porosity discrimination. Probability density functions derived from stochastic forward modelling of endmember depth trends show a decreasing seismic fluid discrimination with depth. Spectral decomposition and blending of selected frequencies reveal a dominant NNE trend compared to the regional SE–NW pro-gradational trend, and a local E–W trend potentially related to fault activity at branches of the Troms-Finnmark Fault Complex. The facies-based inversion captures the main reservoir facies within the limits of the seismic bandwidth. Meanwhile the effective porosity predictions from the multilayer feed forward neural network are consistent with the inverted facies model, and can be used to qualitatively highlight the cleanest regions within the inverted facies model. A combination of facies-based inversion and neural network improves the seismic reservoir delineation of the Snadd Formation in the Goliat Field.  相似文献   

Spatial land use regression of nitrogen dioxide over a 5-year interval in Calgary,Canada     

Stefania Bertazzon  Isabelle Couloigner  Fox E. Underwood 《International journal of geographical information science》2019,33(7):1335-1354

We analysed the spatial distribution of nitrogen dioxide over Calgary (Canada) in summer 2010 and winter 2011 and in summer 2015 and winter 2016, and estimated land use regressions for 2015–16 (2010–11 models were estimated previously). As nitrogen dioxide exhibited spatial clustering, we evaluated the following spatial specifications against a linear model: spatially autoregressive (lag), spatially autoregressive (error), and geographically weighted regression. The spatially autoregressive (lag) specification performed best, achieving goodness-of-fit aligned with or greater than values reported in the literature. We compared the 2015–16 spatially autoregressive models with the 2010–11 models and reparametrized them on the 2010–11 and the 2015–16 data. Finally, we identified a single set of predictors to best fit the data. Nitrogen dioxide concentration decreased over the 5 years, retaining consistent spatial and seasonal patterns, with higher concentrations over traffic corridors and industrial areas, and greater variation in summer than winter. The multi-temporal analysis suggested that spatial land use regressions were robust over the time interval, despite moderate land use change. Multi-temporal spatial land use regressions yielded consistent predictors for each season over time, which can aid estimation of air pollution at fine spatial resolution over an extended time period.  相似文献   

Ontologies to interpret remote sensing images: why do we need them?     

Damien Arvor  Mariana Belgiu  Zoe Falomir  Isabelle Mougenot  Laurent Durieux 《地理信息系统科学与遥感》2019,56(6):911-939

The development of new sensors and easier access to remote sensing data are significantly transforming both the theory and practice of remote sensing. Although data-driven approaches based on innovative algorithms and enhanced computing capacities are gaining importance to process big Earth Observation data, the development of knowledge-driven approaches is still considered by the remote sensing community to be one of the most important directions of their research. In this context, the future of remote sensing science should be supported by knowledge representation techniques such as ontologies. However, ontology-based remote sensing applications still have difficulty capturing the attention of remote sensing experts. This is mainly because of the gap between remote sensing experts’ expectations of ontologies and their real possible contribution to remote sensing. This paper provides insights to help reduce this gap. To this end, the conceptual limitations of the knowledge-driven approaches currently used in remote sensing science are clarified first. Then, the different modes of definition of geographic concepts, their duality, vagueness and ambiguity, and the sensory and semantic gaps are discussed in order to explain why ontologies can help address these limitations. In particular, this paper focuses on the capacity of ontologies to represent both symbolic and numeric knowledge, to reason based on cognitive semantics and to share knowledge on the interpretation of remote sensing images. Finally, a few recommendations are provided for remote sensing experts to comprehend the advantages of ontologies in interpreting satellite images.  相似文献