Seismic waves from earthquakes and other sources are used to infer the structure and properties of Earth’s interior. The availability of large-scale seismic datasets and the suitability of deep-learning techniques for seismic data processing have pushed deep learning to the forefront of fundamental, long-standing research investigations in seismology. However, some aspects of applying deep learning to seismology are likely to prove instructive for the geosciences, and perhaps other research areas more broadly. Deep learning is a powerful approach, but there are subtleties and nuances in its application. We present a systematic overview of trends, challenges, and opportunities in applications of deep-learning methods in seismology. Description Large-scale learning The large amount and availability of datasets in seismology creates a great opportunity to apply machine learning and artificial intelligence to data processing. Mousavi and Beroza provide a comprehensive review of the deep-learning techniques being applied to seismic datasets, covering approaches, limitations, and opportunities. The trends in data processing and analysis can be instructive for geoscience and other research areas more broadly. —BG The ways in which deep learning can help process and analyze large seismological datasets are reviewed. BACKGROUND Seismology is the study of seismic waves to understand their origin—most obviously, sudden fault slip in earthquakes, but also explosions, volcanic eruptions, glaciers, landslides, ocean waves, vehicular traffic, aircraft, trains, wind, air guns, and thunderstorms, for example. Seismology uses those same waves to infer the structure and properties of planetary interiors. Because sources can generate waves at any time, seismic ground motion is recorded continuously, at typical sampling rates of 100 points per second, for three components of motion, and on arrays that can include thousands of sensors. Although seismology is clearly a data-rich science, it often is a data-driven science as well, with new phenomena and unexpected behavior discovered with regularity. And for at least some tasks, the careful and painstaking work of seismic analysts over decades and around the world has also made seismology a data label–rich science. This facet makes it fertile ground for deep learning, which has entered almost every subfield of seismology and outperforms classical approaches, often dramatically, for many seismological tasks. ADVANCES Seismic wave identification and onset-time, first-break determination for seismic P and S waves within continuous seismic data are foundational to seismology and are particularly well suited to deep learning because of the availability of massive, labeled datasets. It has received particularly close attention, and that has led, for example, to the development of deep learning–based earthquake catalogs that can feature more than an order of magnitude more events than are present in conventional catalogs. Deep learning has shown the ability to outperform classical approaches for other important seismological tasks as well, including the discrimination of earthquakes from explosions and other sources, separation of seismic signals from background noise, seismic image processing and interpretation, and Earth model inversion. OUTLOOK The development of increasingly cost-effective sensors and emerging ground-motion sensing technologies, such as fiber optic cable and accelerometers in smart devices, portend a continuing acceleration of seismological data volumes, so that deep learning is likely to become essential to seismology’s future. Deep learning’s nonlinear mapping ability, sequential data modeling, automatic feature extraction, dimensionality reduction, and reparameterization are all advantageous for processing high-dimensional seismic data, particularly because those data are noisy and, from the point of view of mathematical inference, incomplete. Deep learning for scientific discovery and direct extraction of insight into seismological processes is clearly just getting started. Aspects of seismology pose interesting additional challenges for deep learning. Many of the most important problems in earthquake seismology—such as earthquake forecasting, ground motion prediction, and rapid earthquake alerting—concern large and damaging earthquakes that are (fortunately) rare. That rarity poses a fundamental challenge for the data-hungry methods of deep learning: How can we train reliable models, and how do we validate them well enough to rely on them when data are scarce and opportunities to test models are infrequent? Further, how can we operationalize deep-learning techniques in such a situation, when the mechanisms by which they make predictions from data may not be easily explained, and the consequences of incorrect models are high? Incorporating domain knowledge through physics-based and explainable deep learning and setting up standard benchmarking and evaluation protocols will help ensure progress, as is the nascent emergence of a seismological data science ecosystem. More generally, a combination of data science literacy for geoscientists as well as recruiting data science expertise will help to ensure that deep-learning seismology reaches its full potential. Deep-learning processing of seismic data and incorporation of domain knowledge can lead to new capabilities and new insights across seismology. A. MASTIN/SCIENCE, TOP RIGHT: CARA HARWOOD/UNIVERSITY OF CALIFORNIA-DAVIS/CC BY-NC-SA 3.0 MIDDLE RIGHT: JOHAN SWANEPOEL/SCIENCE SOURCE

Deep-learning seismology

This review provides a comprehensive overview of the state-of-the-art methods of graph-based networks from a deep learning perspective. Graph networks provide a generalized form to exploit non-euclidean space data. A graph can be visualized as an aggregation of nodes and edges without having any order. Data-driven architecture tends to follow a fixed neural network trying to find the pattern in feature space. These strategies have successfully been applied to many applications for euclidean space data. Since graph data in a non-euclidean space does not follow any kind of order, these solutions can be applied to exploit the node relationships. Graph Neural Networks (GNNs) solve this problem by exploiting the relationships among graph data. Recent developments in computational hardware and optimization allow graph networks possible to learn the complex graph relationships. Graph networks are therefore being actively used to solve many problems including protein interface, classification, and learning representations of fingerprints. To encapsulate the importance of graph models, in this paper, we formulate a systematic categorization of GNN models according to their applications from theory to real-life problems and provide a direction of the future scope for the applications of graph models as well as highlight the limitations of existing graph networks.

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Graph Neural Network: A Comprehensive Review on Non-Euclidean Space

Seismic exploration is a kind of exploration method for oil and gas resources. However, the disturbance of numerous random noise will decrease the quality and signal-to-noise ratio (SNR) of real seismic records, which brings difficulties to the following works of processing and interpretation. The seismic records of desert region pose a particular problem because of the strong energy noise and the spectrum overlapping between effective signals and random noise. Recent research works demonstrate that a convolutional neural network (CNN) can increase the SNR of seismic records. The optimization of denoising methods based on CNN is principally driven by the loss functions that largely focus on minimizing the mean-squared reconstruction error between denoising records and theoretical pure records. The denoising results estimated by the CNN model are often lacking the perfection of the signal structure. Therefore, when processing seismic records with low SNR, the denoising results often have a lack of effective signal in some traces, which leads to the poor continuity of events. In order to solve this problem, we adopt the strategy of generative adversarial network (GAN) to construct a GAN for denoising. It is divided into two parts: the generator (the denoising network based on CNN) is used to remove noise, while the discriminator is used to guide the generator to restore the structure information of effective signals. The generator and discriminator enhance the performance of each other through adversarial training, and the generator after adversarial training can greatly recover events and suppress random noise in synthetic and real desert seismic data.

Generative Adversarial Network for Desert Seismic Data Denoising

Recently, distributed optical fiber acoustic sensing (DAS) is regarded as a transformative technology in seismic exploration. However, both various complex background noise and weak desired signals significantly limit its practical application. To explore an effective denoising method for the vertical seismic profile (VSP) record received by DAS, we propose an improved residual encoder–decoder deep neural network (RED-Net) enhanced by deep iterative memory block (DMB) and channel aggregation block (CAB), called residual channel aggregation encoder–decoder network (RCEN). Here, DMB uses the weight accumulation theory to improve the feature extraction ability and achieve accurate noise elimination. Meanwhile, CAB, using the multi-channel analysis architecture, enhances the weak signal retention performance. In addition, we leverage both the synthetic data obtained by forward modeling and real DAS noise data to construct a sufficient training dataset with high authenticity, thereby meeting the requirement of network training. Both the synthetic and field DAS-VSP data processing results demonstrate the advantage of RCEN compared with competing algorithms, including singular value decomposition (SVD), conventional RED-Net, and feed-forward denoising convolutional neural network (DnCNN). 

RCEN: A Deep-Learning-Based Background Noise Suppression Method for DAS-VSP Records

Seismic random noise reduction is an indispensable step in seismic data processing. Due to complex geological condition and acquisition environment, random noise in the desert seismic data has spatiotemporally variant noise levels and weak similarity to the signals, which severely obscures the seismic signals and increases the difficulty to extract the reflected seismic signals. This letter focuses on suppressing the desert random noise based on a convolutional neural network (CNN) and proposes a branch construction-based denoising network (BCDNet). The BCDNet contains a denoising main network and a branched network added to the downsampled layer of the main network. With the branched network, the global context feature of the seismic data is obtained early in the network to guide the denoising task of the subsequent main network, which allows a flexible denoising for the desert random noise. Moreover, the downsampled layer is able to enlarge the receptive field of the network without increasing the network depth, thus leading to the better retention of the structural features in the seismic records. The extensive experiments and the field desert data application confirm that our BCDNet not only has a significant denoising capacity to desert seismic data but also is competitive in training time and memory cost.

A Branch Construction-Based CNN Denoiser for Desert Seismic Data

The seismic data with high quality are the essential foundation of imaging and interpretation. However, the real seismic data are inevitably contaminated by noise, which affects the subsequent processing and interpretation of seismic data. In desert seismic data, the energy of noise is stronger. Also, the frequency-band overlap between noise and effective signals is more serious. Recently, some methods based on supervised learning can suppress the desert seismic noise to some extent. Generally, supervised learning-based methods use synthetic noisy data and paired pure data as training sets to train model. However, the difference between synthetic noisy data of training and real seismic data of testing leads to the degradation of the model, and the denoising results often have many false seismic events when dealing with field seismic data. To solve the above problem, we introduce Cycle-generative adversarial networks (GANs) into the denoising of desert seismic records. Cycle-GAN is an unsupervised learning-based method. It can learn the domain mapping from noisy data domain to effective signal data domain through unpaired data training. So we use unpaired real desert common-shot-point data and synthetic pure data to train Cycle-GAN, so as to effectively improve the denoising ability of the method for real seismic data. Finally, the denoising of desert seismic data is realized. The experiment shows that the Cycle-GAN with unpaired data training can effectively suppress desert seismic noise and retain the effective signal amplitude. Also, the denoising result has less false seismic reflection.

The Denoising of Desert Seismic Data Based on Cycle-GAN With Unpaired Data Training

The convolutional neural network (CNN) has achieved excellent performance in many fields, which has attracted much attention. CNN is a kind of feedforward neural network with convolution computation and depth structure. In this letter, aiming at the intense interference of seismic exploration noise in the desert of China, a desert seismic noise reduction system based on deep residual encoder–decoder network is proposed. In order to extract the characteristics and variation law of desert seismic noise, a noise set containing a large number of desert seismic noise is utilized for training the network so that the network forms the end-to-end mapping between the noisy records and the noise. Consequently, the effective signals are obtained by subtracting noise from the noisy records so as to achieve a satisfactory denoising performance. Compared with the traditional random noise suppression methods, the advantages of the proposed method are fully demonstrated in the processing of the synthetic records and the field records. Especially when the signal-to-noise ratio (SNR) is very low, this proposed method can still have a very good denoising effect.

Deep Residual Encoder–Decoder Networks for Desert Seismic Noise Suppression

Distributed fiber-optical acoustic sensing (DAS) is a new and booming technology in seismic exploration. DAS technology has been gradually applied to the exploration of vertical seismic profile (VSP) due to its strong resistance to high temperature and pressure, high sensitivity, high precision (trace interval can be accurate to about 1 m), and so on. However, real DAS-VSP data are always contaminated by both random and coherent noises, which greatly affects the quality of DAS-VSP data. In order to suppress the background noise and increase the signal-to-noise ratio (SNR), a convolutional neural network (CNN) based on leaky rectifier linear unit (ReLU) and forward modeling is proposed and named L-FM-CNN. In terms of network architecture, Leaky ReLU is adopted as the activation function of CNN, which can enhance the recovery ability of trained CNN denoising model to the weak effective signals. As for the training data set, we construct a high-authenticity theoretical pure seismic data set for DAS-VSP data through the complexity of forward models and the diversification of physical parameters. In addition, we propose a new mean square error (MSE) loss function combined with an energy ratio matrix (ERM). The ERM can adjust the SNR between the signal patch and noise patch during the network training and thus increase the robustness of trained CNN denoising model for the DAS-VSP data with different SNRs, especially the DAS-VSP data with extremely low SNR. Both synthetic and real experiments prove the effectiveness of the proposed L-FM-CNN.

Random and Coherent Noise Suppression in DAS-VSP Data by Using a Supervised Deep Learning Method

Desert seismic data pose a particular problem due to its low signal-to-noise ratio and severe spectral overlapping. Most of the existing denoising methods have negative suppression effect on the de...

Hongzhou Wang

Papers

Generative Adversarial Network for Desert Seismic Data Denoising

The Denoising of Desert Seismic Data Based on Cycle-GAN With Unpaired Data Training

Deep Residual Encoder–Decoder Networks for Desert Seismic Noise Suppression

Random and Coherent Noise Suppression in DAS-VSP Data by Using a Supervised Deep Learning Method

Desert mixed seismic noise suppression by using multiple forward models and a supervised deep-learning method