Distributed acoustic sensing (DAS) is emerging in seismic monitoring due to its ultra-dense spatial sampling, durability to harsh environments, and sensitivity to weak ground vibration. Compared with traditional nodal geophones that are normally sparsely distributed, DAS offers unprecedented detectability for small-magnitude earthquake events, very subtle reservoir dynamics, and other weak signals among various applications. The appealing detectability of weak signals is compromised by the terabyte-scale daily continuous record that causes prohibitive storage problems. The current solution is to save only the segmented data of interest, e.g., a certain length around a target event. Here, we tackle the urgent storage problem of DAS monitoring by designing a deep-learning (DL) based compression algorithm. The compression algorithm can be split into two major components. The first part is the encoder based on the vision transformer architecture, where the input multi-channel DAS dataset goes through an encoding process to output the key features from the input. The second part is the decoder, where the features are optimally combined to reconstruct the data of the original scale. The optimal network parameters are obtained via an unsupervised training process, aiming at minimizing the difference between the reconstructed and input data. In the proposed DL-based compression algorithm, only the decoder’s weight parameters and extracted features from the input data through the encoder are saved on the disk, which is sufficient to reconstruct a high-fidelity dataset. The proposed compression algorithm can reach around 50 times the compression rate for a gigabyte-scale DAS dataset without unsatisfactory reconstruction performance.

The focal mechanism provides seismological constraints on the geological faults that generate the earthquakes and thus is important for regional seismotectonic research. Focal mechanism calculation based on the P-wave first-motion-polarity is a widely used method, particularly helpful for small to moderate-size earthquakes. However, determining the P-wave first-motion polarity can be challenging and subjective for smaller earthquakes. Here, we propose a deep-learning method (EQpolarity) for determining the P-wave first-motion polarity using the vertical-component seismic waveforms. The proposed deep-learning method was trained using a large-scale dataset from South California and then adapted to the Texas earthquake data via a transfer learning method. The original and secondary models obtained 95.43% and 98.82% accuracy on the Texas database, respectively, indicating the effectiveness of transfer learning. We further apply the deep learning method to thousands of events on the TexNet catalog to determine the focal mechanisms. Most of the focal mechanism solutions align well with the strikes, dips, and rakes of the known faults that were explored previously using full-waveform-based methods. The generation of the large focal mechanism database offers significant insights into the seismotectonic status of West Texas. The open-source package of EQpolarity can be accessed at https://github.com/chenyk1990/eqpolarity.

Predicting the peak ground acceleration from the first few seconds after the P-wave arrival time is crucial in estimating the ground motion intensity of the earthquake. The early estimation of peak ground acceleration supports the earthquake early warning system to generate the warning. Here, we propose to use the vision transformer to predict the peak ground acceleration using 4-sec three-channel single-station seismograms, i.e., 1s prior to the P-wave arrival and 3s subsequent to the arrival. The vision transformer can significantly extract remarkable information from the data resulting in superior prediction performance. The core layer of the vision transformer is the multi-head attention network which highlights the significant features of the input data. We train and evaluate the proposed algorithm using the Italian earthquake waveform data, where the proposed algorithm shows a promising result. The proposed vision transformer network utilizes an augmentation strategy to improve the learning ability of the model. Our proposed method is compared to the benchmark deep learning methods and empirical ground-motion models and outperforms all of them. The proposed algorithm can also predict the peak ground acceleration accurately using only 2-sec data after the P-wave arrival time. The proposed vision transformer architecture can also be integrated into a peak ground acceleration classification framework. Finally, the proposed algorithm is tested using real-time data and shows accurate results, indicating its applicability in real-time monitoring.

Earthquake data recorded in Texas are dramatically different from other places because of the various types of noise caused by oil and gas production or anthropogenic activities. This causes a relatively lower signal-to-noise ratio (SNR) and a strong challenge to leverage a globally trained deep learning model for earthquake detection. To combat the challenging data characteristics when monitoring seismicity using deep learning, we propose to apply transfer learning to a globally optimal phase-picking model using regional earthquake data compiled from Texas. Specifically, we first train an advanced deep learning model based on the compact convolutional transformer (EQCCT) using a global earthquake dataset. Then, we construct individual datasets from each of the main basins in Texas and apply transfer learning to each basin-scale database, intending to obtain optimal picking performance in each basin. As a result, the precision, recall, and$F1$-score significantly increased from the original EQCCT model to the fine-tuned model in the Delaware and Midland basins. The standard deviations of the picking errors of both P- and S-wave phases accordingly decrease significantly. The greatly improved EQCCT models help detect more P- and S-wave arrivals, facilitating a more successful association and location. Transfer learning models using Texas data and Texas basin-based transfer learning models with detailed documentation can be downloaded fromhttps://github.com/omarmohamed15/Picking-Texas/tree/main.

Passive seismic source location imaging is important to various scientific and engineering research topics spanning from unconventional reservoir development in exploration seismology to seismic hazard prevention in the earthquake seismology community. The emerging machine-learning (ML) techniques enable the location of passive seismic sources with unprecedented efficiency and accuracy. Most of the state-of-the-art ML methods are based on waveforms, as required by the most popular convolutional neural network (CNN) architecture, which is prone to the sensitivity of velocity models. Here, we present a traveltime-based ML method, RFloc3D, to locate passive seismic sources from manually or automatically picked P- and S-wave arrivals. The proposed method is similar to traditional traveltime-based location methods, where the inverse mapping from arrival times to the passive source location is obtained by inverting a nonlinear inverse problem, but differs in leveraging the random forest (RF) method to learn the inverse mapping relation from numerous eikonal-based forward simulations. Details and analyses of the proposed RFloc3D method are illustrated based on a microseismic monitoring setup. Numerical and real data examples show that the proposed method is capable of real-time location. The inclusion of S-wave arrivals, most importantly, the differential time between P- and S-wave arrivals, helps significantly to reduce the depth error (e.g., decreasing the mean absolute error (MAE) to a half) of the located sources.

We propose to implement a compact convolutional transformer (CCT) for picking the earthquake phase arrivals (EQCCT). The proposed method consists of two branches, with each of them responsible for picking the arrival times of the P- or S-wave phases. We use the STEAD dataset to train and validate the proposed EQCCT algorithm. We split the STEAD dataset into 85% for training, 5% for validation, and 10% for testing To facilitate the training process, we implement several data augmentation strategies to the training set by adding Gaussian noise, randomly shifting the waveforms, adding a second earthquake to the input window, and dropping one or two channels from the seismogram in the STEAD dataset. As a result, the EQCCT model outperforms both EQTransformer and PhaseNet, the two most popular deep-learning-based phase-picking methods. Considering the true positive criterion as the picked phases arriving within 0.5 s of the reference times, the EQCCT achieves the lowest mean absolute error (MAE) compared to the EQTransformer and PhaseNet methods for the STEAD, Japanese, Instance and Texas datasets. Our EQCCT network also demonstrates superior performance in other metrics such as precision, recall, and F1 score. We apply the pre-trained model to three independent datasets (not included in the training set), i.e., the Japanese, Texas, and Instance datasets, and achieve higher picking accuracy than the EQTransformer and the PhaseNet in terms of various statistical metrics, demonstrating a stronger robustness and generalization ability of the EQCCT. The real-time application of EQCCT in the Texas Seismological Network (TexNet) further demonstrates its production-ready performance in terms of detection and phase-picking accuracy.

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.

We propose to use unsupervised deep learning (DL) and attention networks to mute the unwanted components of the single-channel earthquake data. The proposed algorithm is an unsupervised technique that does not require any prior information about the input data, i.e., no need for the labeled data. The imaginary and real parts of the short-time frequency transform (STFT) are divided into several overlapped patches to be the input of the proposed DL network, while the output target is the absolute value of the STFT. The proposed DL network utilizes a customized loss function to reconstruct the signal mask, where the STFT components related to the seismic noise are muted. An adaptive thresholding technique is utilized to obtain the binary mask, which is multiplied by the real and imaginary parts of the input seismic data. The binary mask has zero values for the samples corresponding to the unwanted components and ones for the seismic signal components. Then, inverse STFT is used to reconstruct the denoised signal. The proposed algorithm is evaluated using samples from the STanford EArthquake Dataset (STEAD) and the results are compared to the benchmark denoising method, i.e., DeepDenoiser. As a result, the proposed algorithm shows a robust denoising performance and outperforms the DeepDenoiser method by 1.95 dB in terms of signal-to-noise ratio.