1,720,993 research outputs found
Theoretical approach to estimate the probability of strata convergence: implications for mining induced fractures and potential health risk
No full textEstimating probability of strata convergence in relation to the distribution of stresses around the excavated wall as well as quantifying the time-dependent displacement are of paramount importance. Convergence is defined as the gradual decrease in the interval between to specified rock units or geological horizons as a result of thinning of intervening strata. The heterogeneous distribution of induced stresses with time resulting into simultaneous strata displacement which is extremely difficult to predict under the robust mining environment. As a result of unpredictable stresses, during the time of subsequent excavation around a specified geologic condition, significant amount of gravitational potential energy decreases due to increasing kinematic energy of overburden. Furthermore, the equilibrium of underground mining conditions is disturbed subsequently with time after the initial uncovering of side wall and roof top. This paper proposes an approach to construct a functional equation of convergence purely based on idealized geological conditions. There are several implications which are associated with the prediction of convergence such as reducing health risk of mining workers, contributions of fractures over the dynamic convergence mechanism and so on
URS: An Unsupervised Radargram Segmentation Network Based on Self-Supervised ViT With Contrastive Feature Learning Framework
Get PDFRadar sounders are air and space-borne nadir-looking sensors operating in high-frequency (HF) or very high-frequency (VHF) bands and collect subsurface backscattered returns by transmitting electromagnetic pulses. The backscatter echoes are coherently integrated to generate radargrams for investigating and identifying geophysical characteristics of subsurface targets. While recent efforts have been made to develop supervised or semisupervised deep learning models for segmenting radargrams, obtaining accurate labeled information is often a challenging task. Therefore, it is of paramount importance to develop automatic unsupervised semantic segmentation methods to characterize the subsurface targets without labeled information. Unsupervised segmentation methods learn to discover meaningful semantic contents and decompose them into distinct semantic segments with known ontology. Here, we propose an unsupervised radargram segmentation network that uses a convolution-based expansive network as a proxy decoder and a progressive stepwise reconstruction strategy of the input signal from the latent space to measure the spatial similarity with the input radar sounder signal. After designing a unique training strategy by bootstrapping the randomness inside the minibatch and combining the spatial similarity loss along with the contrastive correlation loss, the proposed architecture outperformed the state of the art in fully unsupervised settings. Experiments were conducted on the multichannel coherent radar depth sounder to test the robustness of the proposed method. We carried out a comparative analysis with the state-of-the-art unsupervised and supervised segmentation methods. MIoU is improved by 23.47%
An FFT-based CNN-Transformer Encoder for Semantic Segmentation of Radar Sounder Signal
No full textRadar Sounders (RSs) are sensors operating in the nadir-looking geometry (with HF or VHF bands) by transmitting modulated electromagnetic (EM) pulses and receiving the backscattering response from different subsurface targets. Recently, convolutional neural network (CNN) architectures were established for characterizing RS signals under the semantic segmentation framework. In this paper, we design a Fast Fourier Transform (FFT) based CNN-Transformer encoder to effectively capture the long-range contexts in the radargram. In our hybrid architecture, CNN models the high-dimensional local spatial contexts, and the Transformer establishes the global spatial contexts between the local spatial ones. To overcome Transformer complex self-attention layers by reducing learnable parameters; - we replace the self-attention mechanism of the Transformer with unparameterized FFT modules as depicted in FNet architecture for Natural Language Processing (NLP). The experimental results on the MCoRDS dataset indicate the capability of the CNN-Transformer encoder along with the unparameterized FFT modules to characterize the radargram with limited accuracy cost and by reducing the time consumption. A comparative analysis is carried out with the state-of-the-art Transformer-based architecture
An Enhanced Unsupervised Feature Learning Framework For Radar Sounder Signal Segmentation
No full textUnsupervised semantic segmentation is the method of discovering meaningful semantic contents within the image domain without using any labelled information. The learned semantic contents are then decomposed into distinct semantic segments with known ontology. The core task of an unsupervised feature learning algorithm is to produce dense features for every pixel with rich semantic content to form distinct clusters with compact information for the downstream task. In this work, we extend the previously developed Self-Supervised Transformer with Energy-based Graph Optimization (STEGO) architecture by integrating a convolution-based Expansive Network in the decoder along with the spatial similarity loss function for radar sounder signal segmentation. Experimental results on the Multi-Channel Coherent Radar Depth Sounder (MCoRDS) data confirm the capability of the proposed unsupervised segmentation method
Leveraging a Hybrid Quantum-Classical Framework for Subsurface Target Detection in Radar Sounding System: Challenges and Opportunities
No full textIn this article, we explore the potential of quantum machine learning (QML) for subsurface feature extractions from radar sounder (RS) signals. We propose a hybrid quantum-classical (HQC) learning paradigm that leverages parameterized quantum circuits (PQCs) to generate probability amplitudes based on quantum properties such as superposition and entanglement. These amplitudes are synergistically integrated with the classical deep neural networks that are efficient in learning high-dimensional contextual features for downstream prediction tasks. The present research work is structured around two objectives. First, we investigate the role of quantum circuits in the latent space for transferring back-and-forth rich discriminative spatial context from the encoder to the decoder for segmentation. Second, we investigate how the probabilistic amplitudes derived from quantum circuits are significant in integrating into the classical models to provide new insights for RS signals segmentation. The performance of the hybrid architectures has been studied in small-scale settings by simulating the expected behavior of the quantum circuits on a classical machine. The experimental results have demonstrated the viability of QML frameworks on MCoRDS-1 and MCoRDS-3 datasets for RS signal segmentation. Qualitatively, they are capable of delineating the spatial extent of the bedrock from noise. Additionally, we conduct a comparative analysis between the Qiskit Aer Simulator and the IBM FakeBackend Simulator to highlight the computational trade-offs and validate fidelity of two simulators for scalable experimentation. Therefore, our work opens up new avenues of research for future RS data analysis leading to more precise and efficient subsurface target segmentation
A Hybrid Quantum-Classical CNN Architecture for Semantic Segmentation of Radar Sounder Data
No full textThe article presents for the first time a hybrid quantum-classical architecture in the context of subsurface target detection in the radar sounder signal. We enhance the classical convolutional neural network (CNN) based architecture by integrating a quantum layer in the latent space. We investigate two quantum circuits with the classical neural networks by exploiting fundamental properties of quantum mechanics such as entanglement and superposition. The proposed hybrid architecture is used for the downstream task of patch-wise semantic segmentation of radar sounder subsurface images. Experimental results on the MCoRDS and MCoRDS3 datasets demonstrated the capability of the hybrid quantum-classical approach for radar sounder information extraction
A CNN Architecture Tailored For Quantum Feature Map-Based Radar Sounder Signal Segmentation
No full textThis article presents a hybrid quantum-classical framework by incorporating quantum feature maps into a classical Convolutional Neural Network (CNN) architecture for detecting different subsurface targets in radar sounder signals. The quantum feature maps are generated by quantum circuits to utilize spatially-bound input information from the training samples. The associated spectral probabilistic amplitudes of the feature maps are further fed into the classical CNN-based network to classify the subsurface targets in the radargram. Experimental results on the MCoRDS and MCoRDS3 datasets demonstrated the capability of enhancing the classical architecture through quantum feature maps for characterizing radar sounder data
The Potential of Channel Specific Reflectance in Landsat 8 OLI Sensor for Retrieving Coal Fire Affected Pixels
No full textCoal fire is a serious threat in major coal producing countries across the globe and poses significant constraints in mining operations, often leading to environmental degradation. The applications of thermal and shortwave infrared remote sensing play a substantial role in systematically detecting and monitoring the coal fire. Over the last few decades, researchers have extensively examined the importance of spectral radiance for retrieving reliable pixel-integrated temperature threshold to delineate coal fire from its background. However, such an assumption does not necessarily consider the local information, thereby leading to difficulty in isolating the actual coal fire affected pixels. Therefore, we propose to utilise the channel specific reflectance to retrieve the thermally anomalous pixels in coal fire related applications using Landsat 8 OLI data. This paper explores the practicability of incorporating the active fire detection technique using channel specific reflectances based on both fixed and contextual thresholds in the Jharia coalfield, India
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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