An earthquake early warning system (EEWS) should be included in smart cities to preserve human lives by providing a reliable and efficient disaster management system. This system can alter how different entities communicate with one another using an Internet of Things (IoT) network where observed data are handled based on machine learning (ML) technology. On one hand, IoT is employed in observing the different measures of EEWS entities. On the other hand, ML can be exploited to analyze these measures to reach the best action to be taken for disaster management and risk mitigation in smart cities. This paper provides a survey on the different aspects required for that EEWS. First, the IoT system is generally discussed to provide the role it can play for EEWS. Second, ML models are classified into linear and non-linear ones. Third, the evaluation metrics of ML models are addressed by focusing on seismology. Fourth, this paper exhibits a taxonomy that includes the emerging ML and IoT efforts for EEWS. Fifth, it proposes a generic EEWS architecture based on IoT and ML. Finally, the paper addresses the application of ML for earthquake parameters’ observations leading to an efficient EEWS.

/pdf/employing-machine-learning-and-iot-for-earthquake-early-221jh0eg.pdf

Employing Machine Learning and IoT for Earthquake Early Warning System in Smart Cities

Earthquake Disaster Assessment (EDA) plays a critical role in earthquake disaster prevention, evacuation, and rescue efforts. Deep learning (DL), which boasts advantages in image processing, signal recognition, and object detection, has facilitated scientific research in EDA. This paper analyses 204 articles through a systematic literature review to investigate the status quo, development, and challenges of DL for EDA. The paper first examines the distribution characteristics and trends of the two categories of EDA assessment objects, including earthquakes and secondary disasters as disaster objects, buildings, infrastructure, and areas as physical objects. Next, this study analyses the application distribution, advantages, and disadvantages of the three types of data (remote sensing data, seismic data, and social media data) mainly involved in these studies. Furthermore, the review identifies the characteristics and application of six commonly used DL models in EDA, including convolutional neural network (CNN), multi-layer perceptron (MLP), recurrent neural network (RNN), generative adversarial network (GAN), transfer learning (TL), and hybrid models. The paper also systematically details the application of DL for EDA at different times (i.e., pre-earthquake stage, during-earthquake stage, post-earthquake stage, and multi-stage). We find that the most extensive research in this field involves using CNNs for image classification to detect and assess building damage resulting from earthquakes. Finally, the paper discusses challenges related to training data and DL models, and identifies opportunities in new data sources, multimodal DL, and new concepts. This review provides valuable references for scholars and practitioners in related fields. 

Deep Learning for Earthquake Disaster Assessment: Objects, Data, Models, Stages, Challenges, and Opportunities

Seismology is among the ancient sciences that concentrate on earthquake disaster management (EQDM), which directly impact human life and infrastructure resilience. Such a pivot has made use of contemporary technologies. Nevertheless, there is a need for more reliable and insightful solutions to tackle the daily challenges and intricacies of the natural sciences that stakeholders must confront. To consolidate the substantial endeavors in this field, we undertake a comprehensive survey of the interconnected contemporary technologies. More particularly, we analyze the data communication networks (DCNs) and the Internet of Things (IoT), which are among the main infrastructures of seismic networks. In accordance, we present conventional and innovative signal-processing techniques in seismology. Then, we shed light on the evolution of EQ sensors including the acoustic sensors based on optical fibers. Furthermore, we address the role of remote sensing (RS), robots, and drones for EQDM. Afterward, we highlight the social media contribution. Subsequently, a comprehensive elucidation of the diverse optimization techniques employed in seismology and for prolonging seismic networks is presented. Besides, the paper analyzes the important functions that artificial intelligence (AI) can fulfill in several areas of seismology. Lastly, we guide stakeholders on how to prevent natural disasters and preserve human lives. 

Leveraging internet of things and emerging technologies for earthquake disaster management: Challenges and future directions

Surface-related multiples are generally removed as noise. To suppress surface-related multiples, we propose an unsupervised deep neural network approach based on ensemble learning (UDNNEL). The unsupervised deep neural network (UDNN) has excellent nonlinear mapping ability, which maps the predicted surface-related multiples to true surface-related multiples, thus completing the separation and estimation of multiples and primaries. UDNN consists of three deep neural networks (DNNs), one input data, six outputs, and six pseudo-labels (PLs). In practical use, input data are the predicted surface-related multiples, PLs consist of full-wavefield data and 0 matrices, and outputs are the desired results of estimated true surface-related multiples and differences between these desired results. Input data, one DNN, and corresponding output data are combined into a single base learner. Each base learner corrects amplitudes and phases of the predicted surface-related multiples and maps predicted multiples to true surface-related multiples under the minimization of the total loss function. The principle ensures that our UDNNEL method does not need true primaries or true multiples as training data and solves the problem of missing training datasets. Ensemble learning combines the advantages of three base learners and integrates the nonlinear optimization capabilities of three DNNs to achieve better multiple suppression effectiveness than a single base learner. Therefore, UDNNEL is better than UDNN based on a single base learner (UDNNSBL). Two synthetic data examples verify that our proposed method has a good surface-related multiple suppression effectiveness. Another field data example demonstrates that our proposed method can efficiently suppress multiples under complex conditions.

An Unsupervised Deep Neural Network Approach Based on Ensemble Learning to Suppress Seismic Surface-Related Multiples

Conventional deep learning methods have shown promising results in the medical domain when trained on accurate ground truth data. Pragmatically, due to constraints like lack of time or annotator inexperience, the ground truth data obtained from clinical environments may not always be impeccably accurate. In this paper, we investigate whether the presence of noise in ground truth data can be mitigated. We propose an innovative and efficient approach that addresses the challenge posed by noise in segmentation labels. Our method consists of four key components within a deep learning framework. First, we introduce a Vision Transformer-based modified encoder combined with a convolution-based decoder for the segmentation network, capitalizing on the recent success of self-attention mechanisms. Second, we consider a public CT spine segmentation dataset and devise a preprocessing step to generate (and even exaggerate) noisy labels, simulating real-world clinical situations. Third, to counteract the influence of noisy labels, we incorporate an adaptive denoising learning strategy (ADL) into the network training. Finally, we demonstrate through experimental results that the proposed method achieves noise-robust performance, outperforming existing baseline segmentation methods across multiple evaluation metrics.

Dealing with Unreliable Annotations: A Noise-Robust Network for Semantic Segmentation through A Transformer-Improved Encoder and Convolution Decoder

The inherent challenge of 3-D seismic noise attenuation is determining how to uncover high-dimensional concise structures that only exist in true signals to eliminate random noise. The prevailing deep learning (DL) denoising methods have achieved promising performance in revealing the compact structures underlying contaminated seismic data. However, as clean ground truth seismic data are generally unavailable in real-world settings, most existing matrix-based DL denoising schemes fail to automatically describe this type of high-dimensional structure in an unsupervised manner, potentially rendering them unable to effectively perform 3D seismic data denoising tasks. To tackle this challenge, this article presents a tensor convolutional neural network (TCNN)-based data denoising scheme using Stein’s unbiased risk estimate (SURE) (called SURE-TCNN) to learn intrinsic high-dimensional structures without ground truth seismic data. Considering that SURE provides an almost unbiased estimate of the mean squared error (MSE), SURE-TCNN has the potential to provide similar results to those of the supervised MSE-based TCNN with ground truth data. For ease of implementation, the properties of a transform-based tensor-tensor product (t-product) are followed to establish a solid theoretical connection between the SURE-TCNN tensor and matrix. Derived from this connection, the SURE-TCNN weight parameters are determined by implementing a matrix-based SURE-CNN on each frontal slice in the time-frequency domain (e.g., the wavelet domain). Synthetic and field data examples demonstrate the superior performances of the proposed model against three state-of-the-art (SOTA) methods.

Ground Truth-Free 3-D Seismic Random Noise Attenuation via Deep Tensor Convolutional Neural Networks in the Time-Frequency Domain

Due to the complex properties of hyperspectral images (HSI), such as spatial-spectral structure, high dimension, and great spectral variability, HSI clustering is a challenging operation. In this paper, we propose a novel deep convolutional asymmetric autoencoder-based spatial-spectral clustering network (DCAAES2C-Net) which employs a convolutional autoencoder (CAE) and an asymmetric autoencoder to investigate spatial-spectral information. First, we use a CAE to extract spatial-spectral features. Then, we introduce an asymmetric autoencoder between the encoder and decoder of CAE to suppress some non-material-related spatial information in latten feature like shading and texture. By using a collaborative strategy to train the proposed networks, we obtain the representation features in a low dimension. Furthermore, we improve the 
 
 k
 
 -means algorithm by using the concept of over-clustering to handle fuzzy representation which is difficult to distinguish the cluster, and utilize it to obtain the final HSI clustering result. The results of the experiments demonstrated that the proposed methodology outperforms other methods on the frequently used hyperspectral image dataset.

/pdf/deep-convolutional-asymmetric-autoencoder-based-spatial-2jbvsd8p.pdf

Deep Convolutional Asymmetric Autoencoder-Based Spatial-Spectral Clustering Network for Hyperspectral Image

Region of interest (ROI) segmentation is a key step in computer-aided diagnosis (CAD). With the problems of blurred tissue edges and imprecise boundaries of ROI in medical images, it is hard to extract satisfactory ROIs from medical images. In order to overcome the shortcomings in segmentation from the V-Net model or the level set method (LSM), we propose in this paper a new image segmentation method, the VLSM-Net model, combining these two methods. Specifically, we first use the V-Net model to segment the ROIs, and set the segmentation result as the initial contour. It is then fed through the hybrid LSM for further fine segmentation. That is, the complete segmentation of the V-Net model can be obtained by successively combining the V-Net model and the hybrid LSM. The experimental results conducted in the public datasets LiTS and LUNA show that, compared with the V-Net model or LSM alone, our VLSM-Net model greatly improves the sensitivity, precision and dice coefficient values (DCV) in 3D image segmentation, thus validating our model’s effectiveness.

https://www.mdpi.com/2076-3417/13/7/4384/pdf?version=1680163558

VLSM-Net: A Fusion Architecture for CT Image Segmentation

A robust hierarchical domain constraint (HDC) strategy is proposed for the multiscale contrast objects reconstruction in this letter. The Monte Carlo method and point evaluation are adopted and modified for the classification of background and scatterers cells, the parameters of the background cells are set as the same as the actual background and removed from the inversion. The major work of the HDC strategy is to divide the inversion process into several stages, and the classification of cells is evaluated separately at each stage, making the classification reversible. And the HDC can effectively classify the inversion domain of low-contrast objects without negligence in the multiscale contrast objects case, as well as the continuously varying inhomogeneous objects’ low-contrast parts. Another advantage of HDC is the threshold determining the classification of cells can be flexibly chosen. This robust constraint of the inversion domain compresses the solution space so that the ill-posedness of the inverse problem is mitigated, and better accuracy thus is achieved. In this letter, the HDC strategy is applied to the variational born iterative method (VBIM), numerical results validate the accuracy, robustness, and antinoise ability of the proposed VBIM-HDC.

A Robust Hierarchical Domain Constraint Strategy for Multiscale Contrast Objects Reconstruction

Zoledronate inhibits the differentiation potential of adipose-derived stem cells into osteoblasts in repairing jaw necrosis.

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