Earthquake early warning systems (EEWSs) often rely on fast determination of earthquake source parameters, namely, location, magnitude, and depth. In areas where the seismic network is coarse, the capability to determine source parameters based on data recorded by a single station is desirable. Moreover, being able to use a single component of the seismic data might increase the robustness of the system to sensor malfunction and might save on sensor cost and computation time. Here, we propose a hybrid deep learning (DL) model to estimate source parameters based on single-component data recorded by a single station at 3 s after the P-wave onset. The model, which we call EEWS-311, uses a convolutional neural network (CNN) and bidirectional long short-term memory. It is trained and tested on recordings of more than 14000 events by a single station of the Japanese Hi-net high-sensitivity short-period seismic network. Compared with source parameters obtained by conventional methods, our model achieves excellent performance (average errors in latitude, longitude, magnitude, and depth equal to 0.05°, 0.1°, 0.14 velocity magnitude (Mv), and 5.68 km, respectively). The results demonstrate the suitability of EEWS-311 for earthquake early warning in areas with sufficient training data.

We present a novel deep-learning method using the ConvMixer network for automatic earthquake location. The proposed ConvMixer network utilizes three-component waveform recordings of single stations for estimating the hypocenter location. The ConvMixer network is a patch-based architecture that combines depthwise and pointwise convolutions to extract the global and local information of the earthquake waveforms. We train and test the proposed method using the Italian seismic dataset (INSTANCE). The ConvMixer network estimates the earthquake hypocenter locations with high accuracy, reaching a mean absolute error (MAE) of 2.71 km for the epicenter distance, and 1.15 km for the depth. In addition, we use the global STanford EArthquake Dataset (STEAD) to further evaluate the performance of the ConvMixer. As a result, the ConvMixer network achieves MAEs of 2.27 km and 1.19 km for the distance and the depth, respectively. The proposed ConvMixer network is compared to the benchmark methods, i.e., ResNet, AlexNet, MobileNet, and Xception, and outperforms all of them.

The need for an earthquake early-warning system (EEWS) is unavoidable in order to save lives. In terms of managing earthquake disasters and achieving effective risk mitigation, the quick identification of the earthquake’s intensity is a valuable factor. In light of this, the on-site intensity measurement can be transmitted over an Internet of Things (IoT) network. In this regard, a machine learning (ML) strategy based on numerous linear and non-linear models is proposed in this study for a quick determination of earthquake intensity after two seconds from the P-wave onset. We call this model an on-site two-second ML model-based earthquake intensity determination (2S-ML-EIOS). The utilized dataset INSTANCE for this model is observed by the number of 386 stations from the Italian national seismic network. Our model has been trained on 50,000 occurrences (150 thousand of 2s-three-component seismic windows). The model has the ability to deal with limited features of the waveform traces leading to reliable estimation of the earthquake intensity. The suggested model has a 98.59% accuracy rate in predicting earthquake intensity. The suggested 2S-ML-EIOS model can be used with a centralized IoT system to promptly send the alarm, and the IoT system will then instruct the affected administration to take the appropriate action. The 2S-ML-EIOS results are contrasted with those from the traditional manual solution approach, which corresponds to the ideal solution mean. Based on the extreme gradient boosting (XGB) model, the 2S-ML-EIOS can achieve the best intensity determination, and this improved performance demonstrates the methodology’s efficacy for EEWS.

Earthquake early-warning system (EEWS) is inevitable for saving human lives. The fast determination of the Earthquake’s (EQ’s) magnitude and its location is significant in disaster management and EQ risk mitigation. These parameters can be conveyed over the Internet-of-Things (IoT) network to alleviate an EQ disaster. In this article, a deep learning model based on integrating autoencoder (AE) and convolutional neural network (CNN) for a swift pinpointing of EQ magnitude and location after 3 s from the onset of the P-wave is proposed. Thus, we name it 3 s AE and CNN (3S-AE-CNN). The employed data set is observed by three stations from the Japanese Hi-net seismic network. We have trained our model on 12200 events (109.80 thousand 3-s-three-component seismic windows). The model facilitates the extraction of waveforms’ significant features leading to robust estimation of the EQ parameters. The proposed model predicts the magnitude and location of EQ with errors in magnitude, latitude, and longitude that reach 0.000028, 0.0000033, and 0.0001, respectively. The EQ’s parameters calculated by the proposed 3S-AE-CNN model are swiftly sent to a centralized IoT system that in turn directs the involved entity to take suitable action. The obtained results of the 3S-AE-CNN are compared to the conventional manual solution method, which represents the optimum solution mean. The 3S-AE-CNN shows an enhanced performance for the magnitude and location determination as compared with the benchmark method, which proves its effectiveness for EEWS.

We develop a random forest (RF) model for rapid earthquake location with an aim to assist earthquake early warning (EEW) systems in fast decision making. This system exploits P-wave arrival times at the first five stations recording an earthquake and computes their respective arrival time differences relative to a reference station (i.e., the first recording station). These differential P-wave arrival times and station locations are classified in the RF model to estimate the epicentral location. We train and test the proposed algorithm with an earthquake catalog from Japan. The RF model predicts the earthquake locations with high accuracy, achieving a mean absolute error (MAE) of 2.88 km. As importantly, the proposed RF model can learn from a limited amount of data (i.e., 10% of the dataset) and much fewer (i.e., three) recording stations and still achieve satisfactory results (MAE < 5 km). The algorithm is accurate, generalizable, and rapidly responding, thereby offering a powerful new tool for fast and reliable source-location prediction in EEW.

Discrimination between earthquakes and quarry blasts is crucial for precise seismic analysis, e.g., seismic hazard mitigation, earthquake cataloging, etc. However, the discrimination process is challenging due to the similarity of waveforms between the local earthquakes and quarry blasts. We propose to use the scalogram and the capsule neural network to distinguish between earthquakes and quarry blasts. First, we obtain the scalogram for 60s 3-channel waveforms, where we extract 10s before and 50s after the first arrival time of the seismic event. Secondly, we utilize the capsule neural network to extract the important information from the input scalogram which leads to robust classification performance. The proposed capsule neural network consists of the convolutional layer, primary capsule layer, and digit caps layer. The convolutional layer extracts the important information from the input data, and the primary capsule layer extracts the spatial relationship between different feature maps. Thirdly, we use the dynamic routing process to connect the primary capsule to the digit caps layer. We train and test the proposed capsule network using a small and unbalanced dataset which is recorded by the Egyptian Seismic Network (ENSN) in the Red Sea and the surrounded area in Egypt. Accordingly, the proposed method achieves a test accuracy of 96.08%. The proposed method is compared to the benchmark methods, i.e., convolutional neural network (CNN), AlexNet, VGG, and ResNet networks, and demonstrated to outperform all of the competing methods. Finally, we apply the proposed method to classify real-time seismic events and obtain promising results.

We propose a novel deep learning method using an attention-based fully convolutional dense network (FCDNet) for automatic earthquake detection. The FCDNet consists of encoder-decoder parts with skip connections, where each encode-decoder block contains a block of densely connected layers to enhance the feature learning capability. The spatial attention mechanism is added within the FCDNet to assign greater attention to useful features and hence improve the accuracy of earthquake detection. The time-frequency representations of three-component seismograms produced by the Stockwell transform are used for better extracting the hidden data features. The attention-based FCDNet extracts the time-frequency features needed for distinguishing the seismic signal from the background noise. We evaluate the performance of the proposed method using a Mediterranean dataset. The attention-based FCDNet is trained using 90% of the Mediterranean dataset and tested using the remaining 10%. Accordingly, the training and testing accuracies are 97.71% and 97.02%, respectively. The intersection over union (IoU), precision, recall, and F1-score of the attention-based FCDNet are 93.80%, 99.72%, 99.55%, and 99.64%, respectively. Moreover, to evaluate the generalization ability of the trained model, we utilize 100,000 seismic waveforms recorded in different seismic regions from the global STanford EArthquake Dataset (STEAD) dataset for testing, which shows robust performance. We also apply the attention-based FCDNet to the Japanese seismic data and compare the performance to the CRED and SCALODEEP methods. The attention-based FCDNet outperforms the benchmark methods and achieves a higher detection accuracy of 99.46%. The attention-based FCDNet is additionally evaluated using one-day continuous seismic data recording a seismic swarm that occurred in the Helike region. As a result, the attention-based FCDNet recognizes 135 earthquakes and raises 15 false alarms with a detection accuracy of 90.06%.

Magnitude determination of earthquakes is a mandatory step before an earthquake early warning (EEW) system sends an alarm. Beneficiary users of EEW systems dependon how far they are located from such strong events. Therefore,determining the locations of these shakes is an important is sue for the tranquility of citizens as well. In light of that, this article proposes a magnitude, location, depth, and origin timecategorization using earthquake Ml magnitudes between 2 and 9.The dataset used is the fore and aftershocks of the great Tohokuearthquake of March 11,2011, recorded by three stations fromthe Japanese Hi-net seismic network. The proposed algorithmdepends on a convolutional neural network (CNN) which hasthe ability to extract significant features from waveforms thatenabled the classifier to reach a robust performance in the required earthquake parameters. The classification accuracies ofthe suggested approach for magnitude, origin time, depth, andlocation are 93.67%,89.55%,92.54%,and 89.50%, respectively.