ALBERT

Description: Automatic seismic data interpretation is a significant method in the exploration of geophysics. Complexities of the subsurface structures and the subsurface wave propagation media, make the decision-making process difficult in seismic data interpretation. Nevertheless, the extent of related knowledge and using the expert system method in seismic data interpretation can mitigate this problem. An expert system is a knowledge-based system that applies its knowledge in a complex and specific area and acts as an expert end-user consultant. This study investigates the design of an ANFIS expert system for mud diapirs detection with seismic data analysis in Gorgan plain. This method was applied to seismic attributes from a complex geological mud diapir bearing structure from south of the Caspian Sea. The south of the Caspian Sea is one of the richest area as petroleum reserves, and the Gorgan plain has various mud diapirs, which act as indicators of hydrocarbon reservoirs. The expert system design process to identify mud diapirs on seismic sections was modeled in two approaches including manual and automatic seismic data interpretation. In the first approach, the experience of the expert was collected by manual interpretation of training data and used to create a knowledge base and inference of the expert system in the second approach. The validation verified the accuracy of this method with an average accuracy of 90.1% according to using minimum knowledge to develop a knowledge base of the designed ANFIS expert system.

Description: Highlights • Accurate fault model can be built even when sparse drilling wells are available. • The multiresolution fault model provides information of faults with different sizes. • Fault model provides possibility of tectonic and fluid flow analysis simultaneously. • Modelling of faults in different scales, enable more accurate well path design. • The ANN provides optimized parameters for fault detection by ant tracking algorithm. Modelling faults plays a crucial step in the chain of studies through the first phase of the hydrocarbon exploration and its following studies in reservoir engineering, simulation and field development. This study introduces an innovative and automatic integrated approach that combines seismic multi-attributes and well data for faults modelling. The proposed strategy begins with extracting fault-related seismic attributes commonly used for seismic reservoir characterization. Chaos, variance and curvature attributes, typically highlight large-scale faults that shape the structural framework of the study field. In contrast, small-scale faults, influencing subsurface fluid flow in the fractured reservoir, are modeled using the ant-tracking algorithm applied to seismic data. Small-scale and large-scale fault models, then integrated with the conventional fault model to create an integrated discrete fracture network (DFN). This DFN model incorporates information on both large-scale and small-scale faults. The proposed strategy was applied on a geologically complex petroleum field in Iran. The results, validated using Formation Micro Imager (FMI) data, demonstrate accuracy of the integrated DFN model in comparison to conventional approaches on the studied filed, particularly in capturing small-scale faults. Consequently, it can be concluded that the proposed strategy provides a viable alternative for generating accurate DFN model.

Description: When created faithfully from real-world data, Digital 3D representations of objects can be useful for human or computer-assisted analysis. Such models can also serve for generating training data for machine learning approaches in settings where data is difficult to obtain or where too few training data exists, e.g. by providing novel views or images in varying conditions. While the vast amount of visual 3D reconstruction approaches focus on non-physical models, textured object surfaces or shapes, in this contribution we propose a volumetric reconstruction approach that obtains a physical model including the interior of partially translucent objects such as plankton or insects. Our technique photographs the object under different poses in front of a bright white light source and computes absorption and scattering per voxel. It can be interpreted as visual tomography that we solve by inverse raytracing. We additionally suggest a method to convert non-physical NeRF media into a physically-based volumetric grid for initialization and illustrate the usefulness of the approach using two real-world plankton validation sets, the lab-scanned models being finally also relighted and virtually submerged in a scenario with augmented medium and illumination conditions. Please visit the project homepage at www.marine.informatik.uni-kiel.de/go/vito

Description: Highlights • New geophysical data and samples redefine submarine volcanism in Sicilian Channel. • Three dominant bands of volcanism are distinguished. • Ancient, eroded structures aligned at 120° are tied to faulted banks in the north. • Younger band of similarly aligned volcanism in the south is linked to grabens. • Youngest structures comprise small, dispersed volcanoes with distinct orientation. Abstract The origin and role of volcanism in continental rifts remains poorly understood in comparison to other volcano-tectonic settings. The Sicilian Channel (central Mediterranean Sea) is largely floored by continental crust and represents an area affected by pronounced crustal extension and strike-slip tectonism. It hosts a variety of volcanic landforms closely associated with faults, which can be used to better understand the nature and distribution of rift-related volcanism. A paucity of appropriate seafloor data in the Sicilian Channel has led to uncertainties regarding the location, volume, sources and timing of submarine volcanism. To improve on this situation, we use newly acquired geophysical data (multibeam echosounder and magnetic data, sub-bottom profiles) and dredged seafloor samples to: (i) re-assess the evidence for submarine volcanism in the Sicilian Channel and define its spatial pattern, (ii) infer the relative age and style of magmatism, and (iii) relate this to the dominant tectonic structures in the region. Quaternary rift-related volcanism has been focused at Pantelleria and Linosa, at the northwest boundaries of their respective NW-SE trending grabens. Subsidiary and older volcanic sites potentially occur at the Linosa III and Pantelleria SE seamounts, collectively representing the only sites of recent volcanism that can be directly related to the main rift process. These long-lived polygenetic volcanic landforms have been shaped by magmatism that is directly correlated with extensional faulting and buried igneous bodies. Older volcanic landforms, sharing a similar scale and alignment, occur to the north at Nameless Bank and Adventure Bank. These deeply eroded volcanoes have likely been inactive since the Pliocene and are probably related to earlier stages of crustal thinning and underlying feeder structures in the northern region of the Sicilian Channel. Along a similar alignment, Pinne Bank, SE Pinne Bank and Cimotoe in the northern Sicilian Channel lack a surface volcanic signature but are associated with intrusive bodies or deeply buried volcanic rock masses. Terrible Bank, in the same region, also shows evidence of ancient, polygenetic magmatism, but was subject to significant erosion and lacks a prominent alignment. The much younger volcanism at Graham Volcanic Field and along the northern Capo-Granitola-Sciacca Fault Zone differs markedly from that observed in the other study areas. Here, the low-volume and scattered volcanic activity is driven by shallow-water mafic magma eruptions, which gave rise to small individual cones. These sites are associated with large fault structures away from the main rift axis and may have a distinct magmatic origin. Dispersed active fluid venting occurs across both ancient and young volcanic sites in the region and is directly associated with shallow magmatic bodies within tectonically-controlled basins. Our study provides the foundation for an updated tectonic and magmatic framework for the Sicilian Channel, and for future detailed chronological and geochemical assessment of the sources and evolution of magmatic processes in the region.

Description: Spatiotemporal observations are data rich and offer insights into links between ecological patterns and underlying processes. We present fine-scale autonomous observations from repeated ferry transects in the Strait of Georgia (British Columbia, Canada) during the 2020 spring bloom period using a FerryBox system (temperature, salinity, chlorophyll a fluorescence) and a digital inline holographic microscope. Despite instrument cleaning interruptions related to COVID-19 restrictions, 3 periods from late winter (February) to springtime (March and April) contained 14 days of high-quality holograms (〉70 000) capturing 〉10 500 identifiable micro- to mesoplankton using automatic object detection. The ferry set-up provided automatic data storage through Ocean Networks Canada, which also automatized data flagging and guaranteed remote access. The highest-quality holograms repeatedly covered the central and eastern Strait and showed aspects of bloom succession. Fast-growing diatoms (Skeletonema sp.) emerged first, followed by a diverse assemblage including Chaetoceros spp., Ditylum spp., and Eucampia spp., and by April, larger centric cells prevailed. The combined approach captured local suppression of chlorophyll a fluorescence and diatom concentrations in Fraser River plume waters during the freshet, suggesting fine-scale spatial patterns in seasonal planktonic community composition. This work is among the first of its kind to autonomously generate in situ imaging and physicochemical data with spatiotemporal resolution.

Description: The Western Tropical Atlantic Ocean (WTAO) is crucial for understanding CO2 dynamics due to inputs from major rivers (Amazon and Orinoco), substantial rainfall from the Intertropical Convergence Zone (ITCZ), and CO2-rich waters from equatorial upwelling. This study, spanning 1998 to 2018, utilized sea surface temperature (SST) and sea surface salinity (SSS) data from the PIRATA buoy at 8°N 38°W to reconstruct the surface marine carbonate system. Empirical models derived TA and DIC from SSS, with subsequent estimation of pH and fCO2 from TA, DIC, SSS, and SST data. Linear trend analysis showed statistically significant temporal trends: DIC and fCO2 increased and pH decreased, although DIC did not show any trend after data was de-seasoned. Rainfall analysis revealed distinct dry (July to December) and wet (January to June) seasons, aligning with lower and higher freshwater influence, respectively. TA, DIC, and pH correlated positively with SSS, exhibiting higher values during the dry season and lower values during the wet season. Conversely, fCO2 correlated positively with SST, showcasing higher values during the wet season and lower values during the dry season. This emphasizes the influential roles of SSS and SST variability in CO2 solubility within the region.

Description: Ocean alkalinity enhancement (OAE) is a negative emissions technology (NET) that shows significant potential for climate change mitigation. By increasing the bicarbonate ion concentration in ocean water, OAE could enhance long-term carbon storage and mitigate ocean acidification. However, the side effects and/or potential co-benefits of OAE on natural planktonic communities remain poorly understood. To address this knowledge gap, a mesocosm experiment was conducted in the oligotrophic waters of Gran Canaria. A CO2-equilibrated total alkalinity (TA) gradient was employed in increments of 300 µmol L−1, ranging from ∼ 2400 to ∼ 4800 µmol L−1. This study represents the first attempt to evaluate the potential impacts of OAE on planktonic communities under natural conditions. The results show that net community production (NCP), gross production (GP), community respiration (CR) rates, and the metabolic balance (GP:CR) did not exhibit a linear response to the whole alkalinity gradient. Instead, significant polynomial and linear regression models were observed for all rates up to ΔTA 1800 µmol L−1, in relation to the dissolved inorganic carbon (DIC) concentrations. Notably, the ΔTA 1500 and 1800 µmol L−1 treatments showed peaks in NCP shifting from a heterotrophic to an autotrophic state, with NCP values of 4 and 8 µmol O2 kg−1 d−1, respectively. These peaks and the optimum curve were also reflected in the nanoplankton abundance, size-fractionated chlorophyll a, and 14C uptake data. Furthermore, abiotic precipitation occurred in the highest treatment after day 21, but no impact on the measured parameters was detected. Overall, a damaging effect of CO2-equilibrated OAE in the range applied here on phytoplankton primary production, community metabolism, and composition could not be inferred. In fact, a potential co-benefit to OAE was observed in the form of the positive curvilinear response to the DIC gradient up to the ΔTA 1800 treatment. Further experimental research at this scale is key to gain a better understanding of the short- and long-term effects of OAE on planktonic communities.

Description: The Gulf of St. Lawrence is increasingly affected by bottom water hypoxia; however, the timescales and pathways of deep water transport remain unclear. Here, we present results from the Deep Tracer Release eXperiment (TReX Deep), during which an inert SF 5 CF 3 tracer was released inshore of Cabot Strait at 279 m depth to investigate deep inflow transport and mixing rates. Dispersion was also assessed via neutrally-buoyant Swish floats. Our findings indicate that the tracer moves inland at 0.5 cm s −1 , with an effective lateral diffusivity of 2 × 10 2 m 2 s −1 over 1 year. Simplified 1D simulations suggest inflow water should reach the estuary head in 1.7 years, with the bulk arriving after 4.7 years. Basin-wide effective vertical diffusivity is around 10 −5 m 2 s −1 over 1 year; however, vertical diffusivity increases near the basin slopes, suggesting that turbulent boundary processes influence mixing. These results are compared to Lagrangian simulations in a regional 3D model to evaluate the capacity to model dispersion in the Gulf.