Efficient spatial kelp biomass estimations using acoustic methods

Kelp forests are the largest vegetated marine ecosystem on earth, but vast areas of their distribution remain unmapped and unmonitored. Efficient and cost-effective methods for measuring the standing biomass of these ecosystems are urgently needed for coastal mapping, ocean accounting and sustainable management of wild harvest. Here we show how widely available acoustic equipment on vessels can be used to perform robust and large-scale (kilometer) quantifications of kelp biomass which can be used in assessments and monitoring programs. We demonstrate how to interpret echograms from acoustic systems into point estimates of standing biomass in order to create spatial maps of biomass distribution. We also explore what environmental conditions are suitable for acoustic measures. This has direct application for blue carbon accounting, coastal monitoring, management of wild seaweed harvest and the protection and conservation of marine habitats supporting high biodiversity.publishedVersio

Cruise report 2022106 - MAREANO methods cruise: AUV testing (Munin +)

This cruise report relates to the Mareano methods testing cruise undertaken in June 2022 which was mainly focussed on the use of the AUV Munin+ in habitat mapping, and the testing of a new shipborne ADCP. This report both records the activities of the cruise and provides some initial evalutaiton of these equiment and the data they collect.Cruise report 2022106 - MAREANO methods cruise: AUV testing (Munin +)publishedVersio

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets

Geomechanical modelling of subsidence and induced seismicity in a gas reservoir

Reservoir compaction and associated surface subsidence, fault reactivation and induced earthquakes are observed in many petroleum fields worldwide. A better understanding of the geomechanical behaviour of reservoir rocks and neighbouring rock bodies is therefore becoming increasingly important within the petroleum industry. Several monitoring techniques for these phenomena exist, but methods of modelling reservoir geomechanical behaviour are hindered by clear limitations. This study discusses different suspected mechanisms of induced seismicity related to oil and gas production and their significance in varying reservoir environments. In support of this discussion, relevant background theory is presented together with a case study of induced seismicity in the Groningen Gas Field in the northern Netherlands. The aim of this thesis is to use a Modified Discrete Element Method proposed by (H. T. Alassi, 2008) to model the geomechanical behaviour in a depleting gas reservoir. Multiple scenarios have been modelled to investigate the significance of the suspected underlying mechanisms of seismicity and subsidence observed in the Groningen Field. It was found that depletion of a reservoir has the potential to induce rock failure on faults inside and in contact with the depleted zone as well as causing significant surface subsidence. It is also emphasized that improvements of the method and further research is needed to fully understand the significance of the underlying mechanisms

Self-similarity of intrasalt thrust faults: Lessons from offshore Levant Basin and the Dead Sea

The Levant Basin in the eastern Mediterranean Sea developed during rifting episodes occurring from Permian to the Early Jurassic (Netzeband et al. 2006; Gardosh et al. 2010), and has been a deep-water basin with a passive continental margin at least since the Cretaceous (Gardosh et al. 2008). Thick sequences of halite and interbedded shales (stringers) were deposited during the infamous Messinian Salinity Crisis (5.96-5.33 Ma). Salt-rich passive continental margins facilitate complex deformation of both the mobile salt and the surrounding rock mass (Allen et al. 2016, Cartwright et al. 2012). The weak salt will mobilize as a response to differential loading (gravity spreading) or tilting of the basin (gravity gliding), and intricate strains within the package may be observed due to the difference in AI between the halite and the stringers. This study compares large-scaled intrasalt thrust systems interpreted on high quality 3D seismic data from offshore Israel (Kartveit et al. under review) with recently published outcrop analogies in gravity-driven mass transport systems near the Dead Sea (Alsop et al. 2017a, Alsop et al. 2017b) in order to evaluate the multi-phase deformation history in the basin

Multiphase structural evolution and geodynamic implications of Messinian salt-related structures, Levant Basin, offshore Israel

Speculations surround salt deformation in the Mediterranean Basins, both related to the deformation history and the triggers for halokinesis since the onset of the Messinian Salinity Crisis (MSC). This work presents a detailed description of the mechanisms driving internal and external deformation of a salt giant from the Levant Basin, offshore Israel. The intrasalt siliciclastic layers generate good internal reflectivity within the Messinian evaporites, allowing a thorough elucidation of the complex evolution and nature of syn- and post-Messinian structures. We have identified three distinct phases of deformation in the deep basin, based on the orientation, timing and geometry of their related structures: The first phase is characterized by small-scaled, gravity-driven, contractional faults and folds oriented N-S that have been overprinted by a second syn-Messinian, NW-SE trending, deformation phase affecting the clastic bundles. This latter deformation phase is the cause of truncation of the intrasalt stringers on the intra-Messinian erosional surface (IMTS). The third deformation phase occurred in the Pleistocene and affected all strata from the Messinian salt to the seabed. This deformational phase produced thrust, strike-slip- and normal faults, but the dominant orientation of the thrust faults and folds is NNW-SSE. Our study demonstrates that the first deformation phase was caused by regional uplift along the Levant margin during the Messinian, the second is a response to basin subsidence toward the Cyprus Arc, also syn-Messinian, and the third phase is likely related to the reorganization of the African-Eurasian plate boundary and activity along the Dead Sea Transform after the MSC

Machine learning in marine ecology: an overview of techniques and applications

International audienceMachine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets