Seismic hazard evaluation by employing microtremor measurements for Abu Simbel area, Aswan, Egypt

Elbshbeshi, Abdelhamid; Gomaa, Ahmed; Mohamed, A.M.A.; Othman, Amal; Ghazala, Hosni

doi:10.1016/j.jafrearsci.2022.104734

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…Seismic event susceptibility index is accomplished from the average of the vulnerability of seismic for each station. Then distribution map of vulnerability of seismic is shown in Figure 7 (Elbshbeshi et al, 2022).…”

Section: Vulnerability Index Of Seismic Resultsmentioning

confidence: 99%

Analysis of the Effect of Local Magnitude on Peak Ground Acceleration and Seismic Vulnerability Index for Geothermal Field Monitoring Using Microearthquake

Utama,

Indriani,

Garini

2023

jppipa, pendidikan ipa, fisika, biologi, kimia

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Intensive exploitation of geothermal injection and production can trigger microearthquakes which it signals come from dynamic fractures. The purpose of this study is to decide the impact of local magnitude on mitigation in geothermal fields based on soil acceleration and vulnerability of seismic in geothermal fields. This study uses seismic wave recording data and the geology of the research area. It is focable on calculating local magnitude, ground acceleration and seismic susceptibility index to earthquakes based on ground acceleration and seismic susceptibility index in geothermal fields. The maximum amplitude value represent that the medium classification class (3<A0<6) is associated with a moderate degree of deformation. Natural frequency value are found with a moderate classification (4<f0<10) around the area of injection wells and production wells, indicating that the research area has a moderate level of soil hardness structure. The peak ground acceleration in the study area is classified as moderate (0.25<PGA<0.7) which means that the area has a moderate level of risk. Vulnerability of seismic in the study area is included in the low classification (Kg<10). The local magnitude impact on soil acceleration and vulnerability of seismic in this study has a moderate risk and can be categorised as safe. In the future, this research serves as a basis for proper decision-making in geothermal energy operations, monitoring, and infrastructure development

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Section: Vulnerability Index Of Seismic Resultsmentioning

confidence: 99%

Analysis of the Effect of Local Magnitude on Peak Ground Acceleration and Seismic Vulnerability Index for Geothermal Field Monitoring Using Microearthquake

Utama,

Indriani,

Garini

2023

jppipa, pendidikan ipa, fisika, biologi, kimia

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“…Traffic moving object detection in real-world recorded videos is used to recognize the changing or moving areas in the camera view [1][2][3]. It is an essential task in computer vision and plays a vital role in various applications, including intelligent transportation systems (ITSs), fault detection, area monitoring, motion capture, disaster management, scene analysis, and visual surveillance [4][5][6][7][8]. Several image processing algorithms have been developed, in the literature, to detect a particular object or event in the video scenes.…”

Section: Introductionmentioning

confidence: 99%

Novel Deep Learning Domain Adaptation Approach for Object Detection Using Semi-Self Building Dataset and Modified YOLOv4

Gomaa,

Abdalrazik

2024

WEVJ

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Moving object detection is a vital research area that plays an essential role in intelligent transportation systems (ITSs) and various applications in computer vision. Recently, researchers have utilized convolutional neural networks (CNNs) to develop new techniques in object detection and recognition. However, with the increasing number of machine learning strategies used for object detection, there has been a growing need for large datasets with accurate ground truth used for the training, usually demanding their manual labeling. Moreover, most of these deep strategies are supervised and only applicable for specific scenes with large computational resources needed. Alternatively, other object detection techniques such as classical background subtraction need low computational resources and can be used with general scenes. In this paper, we propose a new a reliable semi-automatic method that combines a modified version of the detection-based CNN You Only Look Once V4 (YOLOv4) technique and background subtraction technique to perform an unsupervised object detection for surveillance videos. In this proposed strategy, background subtraction-based low-rank decomposition is applied firstly to extract the moving objects. Then, a clustering method is adopted to refine the background subtraction (BS) result. Finally, the refined results are used to fine-tune the modified YOLO v4 before using it in the detection and classification of objects. The main contribution of this work is a new detection framework that overcomes manual labeling and creates an automatic labeler that can replace manual labeling using motion information to supply labeled training data (background and foreground) directly from the detection video. Extensive experiments using real-world object monitoring benchmarks indicate that the suggested framework obtains a considerable increase in mAP compared to state-of-the-art results on both the CDnet 2014 and UA-DETRAC datasets.

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“…Based on these three signal components, dominant frequency (Fo) and amplification factor (Ao), values are obtained, and both values are used to calculate the seismic vulnerability index (Kg) used to identify soil that is vulnerable to movement. Microtremors, known as ambient noise or vibrations originating from within the ground with specific amplitudes, have been useful in determining soil layers' fundamental frequency and amplitude [7]. Liu et al (2023) used the microtremor method to measure the soil vulnerability index on a potential landslide road section.…”

Section: Introductionmentioning

confidence: 99%

Vulnerability Index Assessment for Mapping Ground Movements Using the Microtremor Method as Geological Hazard Mitigation

Susilo,

Zulaikah,

Pohan

et al. 2024

Civ Eng J

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Various geological disasters, such as landslides and ground movements, occur annually in Srimulyo Village, Malang District, with varying levels of damage. Ground movements can affect structures built above, causing sinking, cracking, and collapse. Research into landslides and ground movements triggered by vibrations is generally conducted using the microtremor method, which has proven effective. This study uses the microtremor method to map the soil condition that is potentially prone to movement or landslides based on the observed soil vulnerability index. Data was collected using a TDL 303s Digital Portable Seismograph instrument; the measurement points were established in the form of a grid distributed across the research area, with a recording duration of approximately 45 minutes at each point. The analysis technique utilizes the Horizontal Vertical Spectrum Ratio (HVSR) based on the Fast Fourier Transform (FFT) principle. The study’s results found that the research location’s seismic vulnerability index varies between 6.5 and 16.5. Areas with high seismic vulnerability index values, specifically those with Kg>11.5, are scattered on the west, south, and southeast sides of the research location. Based on field observations, these areas are dominated by relatively thick sediment layers, leading to lower dominant frequency values and higher amplification values; consequently, the seismic vulnerability index in the southern region is also high. Doi: 10.28991/CEJ-2024-010-05-017 Full Text: PDF

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Seismic hazard evaluation by employing microtremor measurements for Abu Simbel area, Aswan, Egypt

Cited by 8 publications

References 14 publications

Analysis of the Effect of Local Magnitude on Peak Ground Acceleration and Seismic Vulnerability Index for Geothermal Field Monitoring Using Microearthquake

Analysis of the Effect of Local Magnitude on Peak Ground Acceleration and Seismic Vulnerability Index for Geothermal Field Monitoring Using Microearthquake

Novel Deep Learning Domain Adaptation Approach for Object Detection Using Semi-Self Building Dataset and Modified YOLOv4

Vulnerability Index Assessment for Mapping Ground Movements Using the Microtremor Method as Geological Hazard Mitigation

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