GEO Research Publications

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    Stratigraphy and eruptive history of Gedemsa caldera volcano, Central Main Ethiopian Rift
    (Elsevier B.V., 2024-02) Bedassa, Gemechu; Ayalew, Dereje; Getaneh, Worash; Fontijn, Karen; Emishaw, Luelseged; Melaku, Abate A.; Tadesse, Amdemichael Z.; Demissie, Zelalem S.; Swindle, Andrew L.; Chamberlain, Katy J.
    Gedemsa caldera is a peralkaline volcanic depression located in the Central sector of the Main Ethiopian Rift. An integrated volcanological study of stratigraphic sections was carried out in order to constrain the eruptive history of Gedemsa caldera volcano (GCV), Ethiopia. Textural analyses on plagioclase crystals together with field observations shed light on magma chamber processes feeding the volcanic complex. A multi-vent eruption is ascribed to the pre-caldera volcanic products, as can be seen from the varying caldera wall sequences comprising lavas and pyroclastic deposits. At least three major caldera-forming eruptions are identified producing: 1) a lower ignimbrite, 2) extensive pumice fall and pyroclastic density current deposits, and 3) an upper ignimbrite exposed in different areas of the caldera, indicating a sector collapse. We identified 20 individual pyroclastic deposits found outside and within the caldera that erupted after the climactic caldera collapse. The most recent volcanic activity at Gedemsa is characterized by rhyolitic lava domes and pyroclastic deposits from eruptions through numerous vents aligned with WNW-ESE pre-existing cross-cutting structures. Macrocryst disequilibrium textures such as fine-scale oscillatory zoning (FOZ), sieve textures, glomerocryst, and synneusis indicate that magma reservoir is characterized by repeated magma injection, convection flows, and mixing.
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    Subsurface structural control of geothermal resources in a magmatic rift: Gravity and magnetic study of the Tulu Moye geothermal prospect, Main Ethiopian Rift
    (Frontiers Media SA, 2023-07) Nigussie, Wubamlak; Alemu, Abera; Mickus, Kevin; Keir, Derek; Demissie, Zelalem S.; Muhabaw, Yoseph; Muluneh, Ameha A.; Corti, Giacomo; Yehualaw, Esubalew
    Since the Quaternary, extension and magmatism in the Main Ethiopian Rift (MER) have been mainly focused into narrow magmatic segments that have numerous volcanic centers and caldera collapses that offer favorable conditions for the occurrence of geothermal resources. However, the subsurface structure of the volcanic systems (0-10 km) and their link to the distribution of shallow geothermal resources remain unclear. To investigate the role of subsurface structures on the occurrence of these resources, we conducted gravity and magnetic studies combined with geological constraints within the Tulu Moye Geothermal Prospect (TMGP), one of the current geothermal prospects in the central MER associated with caldera collapses. Gravity data from the Global Gravity Model plus (GGMplus 2013) and ground magnetic data transformed into residual and derivative maps reveal that shallow magmatic intrusions occur under the volcanic centers (Tulu Moye, Bora, and Bericha). Our interpretation along with recent magnetotelluric model suggests that only the intrusion beneath Tulu Moye is currently magmatically active and includes partial melt, consistent with it being a primary heat source for the geothermal system. A new caldera formation model is proposed where the TMGP hosts an older large caldera (about 25 km diameter) within which there are several smaller nested caldera systems associated with the Bora, Bericha, and Tulu Moye volcanoes. Along with existing geologic, seismic, and magnetotelluric studies, our gravity and magnetic analysis indicate the interaction between NNE-SSW (rift-parallel) and NW-SE (cross-rift) trending faults, along with shallow magmatic intrusions and caldera systems, suggesting that such a large geothermal system is possible under these conditions.
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    The significance of volcanic segments and rifts in faults characterization within the Amagmatic graben of the Afar Depression, Ethiopia
    (Elsevier Ltd, 2023-09) Demissie, Zelalem S.; Bedassa, Gemechu; Rattani, Ajita; Nigussie, Wubamlak; Kebede, Hailemichael; Muhabaw, Yoseph; Haridasan, Smitha
    The Dobi graben is a northwestern trending, continental rift situated in the East Central Block (ECB) of the Afar Depression (AD), Ethiopia. Ongoing extensional rifting in the graben is evident from the swarm of intermediate magnitude earthquakes (5.7 < Ms < 6.3) in 1989. The graben's extension occurs on steeply dipping faults, where the maximum displacement and traced fault length spans four orders of magnitude. Using a 30 m resolution Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), we conducted a fault population analysis in the Dobi graben. We traced 953 faults and conducted a size-frequency distribution analysis has been conducted for various spatially heterogeneous structural zones. Our results show that the frequency size distribution revealed a negative exponential fitting trend, indicating strong strain distribution within the Imbrication, the Fault Termination zone, and the active axial graben floor. However, a power law size distribution dominates most first-order border faults, suggesting that the strain is localized mainly at the graben flanks. The fault displacement-length profiles demonstrate that approximately 48% of the total fault traced lengths exhibit increasing slip rates towards the southeast, while about 40% display increasing slip rates towards the northwest. These suggest that ?88% of the lateral propagation of the 953 faults in the Dobi graben is governed by the regional differential strain transfer of the Red Sea Rift (RSR) and Gulf of Aden Rift (GAR) to the central Afar. Most of the hmax/L aspect ratio of the Dobi graben's faults fits in Category II, which exhibits a constant hmax/L ratio, meaning that the hmax/L ratio increases with fault growth, presumably due to the graben's is in an active tectonic region. This implies that these faults are becoming more efficient at accommodating deformation as they grow. Additionally, the normalized average maximum displacement over maximum length (Dmax/Lmax) ratio for most faults is 0.03, which is in accord with the constant displacement length fault growth model. © 2023 Elsevier Ltd
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    Multispectral deep learning models for wildfire detection
    (CEUR-WS, 2022-07-06) Haridasan, Smitha; Rattani, Ajita; Demissie, Zelalem; Dutta, Atri
    Aided by wind, all it takes is one ember and few minutes to create wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision based techniques have been proposed for early detection of forest fire using video surveillance. Several computer vision based methods have been proposed to predict and detect forest fires at various spectrum, namely, RGB, HSV and YCbCr. The aim of this paper is to propose multi-spectral deep learning model that combine information from different spectrum at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available dataset is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 1.9% and 14.88% in test and challenge set over those based on single spectrum for fire detection even in challenging environments.
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    Multimodal combination of text and image tweets for disaster response assessment
    (CEUR-WS, 2022-07-06) Kotha, Saideshwar; Haridasan, Smitha; Rattani, Ajita; Bowen, Aaron; Rimmington, Glyn M.; Dutta, Atri
    Social media platforms are a vital source of information in times of natural and man-made disasters. People use social media to report updates about injured or dead people, infrastructure damage, missing or found people among other information. Studies show that social media data, if processed timely and effectively, could provide important insight to humanitarian organizations to plan relief activities. However, real-time analysis of social media data using machine learning algorithms poses multiple challenges and requires processing large amounts of labeled data. Multi-modal Twitter Datasets from Natural Disasters (CrisisMMD) is one of the dataset that provide annotations as well as textual and image data to help researchers develop a crisis response system. In this paper, we analyzed multi-modal data from CrisisMMD, related to seven major natural calamities like earthquakes, floods, hurricanes, wildfires, etc., and proposed an effective fusion-based decision making technique to classify social media data into Informative and Non-informative categories. The Informative tweets are then classified into various humanitarian categories such as rescue volunteering or donation efforts, not-humanitarian, infrastructure and utility damage, affected individuals, and other relevant information. The proposed multi-modal fusion methodology outperforms the text tweets-based baseline by 6.98% in the Informative category and 11.2% in the Humanitarian category, while it outperforms image tweets-based baselines by 4.5% in the Informative category and 6.39% in the humanitarian category