Seismological Community Research Articles

Physics-guided symbolic neural network reveals optimal functional forms describing ground motions

This study presents a novel framework for ground motion modelling utilizing Physics-Guided Symbolic Neural Networks (PGSNN). Symbolic neural networks offer a new method for knowledge discovery, providing a unique perspective for automatically uncovering predictive functional forms from data. This approach differs from traditional methods as it does not rely on predefined equations. Instead, it employs symbolic operators to freely combine input parameters in a high-dimensional space. This method addresses the problem of data imbalance by incorporating physical guidance to ensure that the model produces results that are consistent with established physical principles. The resulting equations align with the expectations of the engineering seismology community, particularly within the magnitude-distance ranges, where classical equations are well calibrated. The prediction performance of the PGSNN, evaluated across different intensity measures (PGA, PGV, and PSA), was assessed by calculating the residuals between measured and predicted values and their standard deviations. The predictive capability of this model was verified using new event records. The results indicate that the prediction performance of the PGSNN is comparable to those of traditional methods.

On the role of trans-lithospheric faults in the long-term seismotectonic segmentation of active margins: a case study in the Andes

Abstract. Plate coupling plays a fundamental role in the way in which seismic energy is released during the seismic cycle. This process includes quasi-instantaneous release during megathrust earthquakes and long-term creep. Both mechanisms can coexist in a given subduction margin, defining a seismotectonic segmentation in which seismically active segments are separated by zones where ruptures stop, classified for simplicity as asperities and barrier, respectively. The spatiotemporal stability of this segmentation has been a matter of debate in the seismological community for decades. In this regard, we explore in this paper the potential role of the interaction between geological heterogeneities in the overriding plate and fluids released from the subducting slab towards the subduction channel. As a case study, we take the convergence between the Nazca and South American plates between 18–40° S, given its relatively simple convergence style and the availability of a high-quality instrumental and historical record. We postulate that trans-lithospheric faults striking at a high angle with respect to the trench behave as large fluid sinks that create the appropriate conditions for the development of barriers and promote the growth of highly coupled asperity domains in their periphery. We tested this hypothesis against key short- and long-term observations in the study area (seismological, geodetic, and geological), obtaining consistent results. If the spatial distribution of asperities is controlled by the geology of the overriding plate, seismic risk assessment could be established with better confidence.

Critical Questions About CSEP, in the Spirit of Dave, Yan, and Ilya

Abstract In honor of our dear departed friends Yan Kagan, Dave Jackson, and Ilya Zaliapin, we propose a selection of broad questions regarding earthquake forecasting and especially the Collaboratory for the Study of Earthquake Predictability (CSEP) in particular and give our thoughts on their answers. This article reflects our opinions, not necessarily those of Yan Kagan, Dave Jackson, and Ilya Zaliapin, and not necessarily those of the seismological community at large. Rather than provide definitive answers, we hope to provoke the reader to think further about these important topics. We feel that Dave Jackson in particular might have liked this approach and may have seen this as an appropriate goal.

Back‐Propagating Rupture: Nature, Excitation, and Implications

AbstractRecent observations show that certain rupture phase can propagate backward relative to the earlier one during a single earthquake event. Such back‐propagating rupture (BPR) was not well considered by the conventional earthquake source studies and remains a mystery to the seismological community. Here we present a comprehensive analysis of BPR, by combining theoretical considerations, numerical simulations, and observational evidences. First, we argue that BPR in terms of back‐propagating stress wave is an intrinsic feature during dynamic ruptures; however, its signature can be easily masked by the destructive interference behind the primary rupture front. Then, we propose an idea that perturbation to an otherwise smooth rupture process may make some phases of BPR observable. We test and verify this idea by numerically simulating rupture propagation under a variety of perturbations, including a sudden change of stress, bulk or interfacial property and fault geometry along rupture propagation path. We further cross‐validate the numerical results by available observations from laboratory and natural earthquakes, and confirm that rupture “reflection” at free surface, rupture coalescence and breakage of prominent asperity are very efficient for exciting observable BPR. Based on the simulated and observed results, we classify BPR into two general types: interface wave and high‐order re‐rupture, depending on the stress recovery and drop before and after the arrival of BPR, respectively. Our work clarifies the nature and excitation of BPR, and can help improve the understanding of earthquake physics, the inference of fault property distribution and evolution, and the assessment of earthquake hazard.

Surface loading on a self-gravitating, linear viscoelastic Earth: moving beyond Maxwell

SUMMARY Constitutive laws are a necessary ingredient in calculations of glacial isostatic adjustment (GIA) or other surface loading problems (e.g. loading by ocean tides). An idealized constitutive law governed by the Maxwell viscoelastic model is widely used but increasing attention is being directed towards more intricate constitutive laws that, in particular, include transient rheology. In this context, transient rheology collectively refers to dissipative mechanisms activated in addition to creep modelled by the Maxwell viscoelastic model. Consideration of such viscoelastic models in GIA is in its infancy and to encourage their wider use, we present constitutive laws for several experimentally derived transient rheologies and outline a flexible method in which to incorporate them into geophysical problems, such as the viscoelastic deformation of the Earth induced by surface loading. To further motivate this need, we demonstrate, via the Love number collocation method, how predictions of crustal displacement depart significantly between Earth models that adopt only Maxwell viscoelasticity and those with transient rheology. Throughout this paper, we highlight the differences in terminology and emphases between the rock mechanics, seismology and GIA communities, which have perhaps contributed towards the relative scarcity in integrating this broader and more realistic class of constitutive laws within GIA. We focus on transient rheology since the associated deformation has been demonstrated to operate on timescales that range from hours to decades. With ice mass loss enhanced at similar timescales as a consequence of anthropogenically caused climate change, the ability to model GIA with more accurate constitutive laws is an important tool to investigate such problems.

SigRecover: Recovering Signal from Noise in Distributed Acoustic Sensing Data Processing

Abstract Because of the harsh deployment environment of the fibers, distributed acoustic sensing (DAS) data usually suffer from the low signal-to-noise ratio issue. Many methods, whether simple but efficient or sophisticated but effective, have been proposed for dealing with noise and recovering signals from DAS data. However, no matter what methods we apply, we will inevitably damage the signals, more or less, resulting in coherent signal leakage in the removed noise. Here, we present a method (SigRecover) for minimizing signal leakage by recovering useful signals from removed noise and its open-source package (see Data and Resources). We apply a robust dictionary learning framework to retrieve the coherent signals from removed noise that can be captured by a pretrained library of atoms (features). The atoms are obtained by a fast dictionary-learning approach from the initially denoised data. The proposed framework is a self-learning methodology, which does not require additional training datasets and thus is conveniently applicable to any input data. We use three well-processed examples from the literature to demonstrate the generic performance of the proposed method. The idea behind this article is inspired by similar methods widely used in the exploration seismology community for retrieving signal leakage and is promising not only for DAS data processing, but also for all other multichannel seismological datasets.

A Closer Cooperation between Space and Seismology Communities – a Way to Avoid Errors in Hunting for Earthquake Precursors

The space physicists and the earthquake (EQ) prediction community exploit the same instruments – magnetometers, but for different tasks: space physicists try to comprehend the global electrodynamics of near-Earth space on various time scales, whereas the seismic community develops electromagnetic methods of short-term EQ prediction. The lack of deep collaboration between those communities may result sometimes in erroneous conclusions. In this critical review, we demonstrate some incorrect results caused by a neglect of specifics of geomagnetic field evolution during space weather activation. The considered examples comprise: Magnetic storms as a trigger of EQs; ULF waves as a global EQ precursor; Geomagnetic impulses before seismic shocks; Long-period geomagnetic disturbances generated by strong EQs; Discrimination of underground ULF sources by amplitude-phase gradients; Depression of ULF power as a short-term EQ precursor; and Detection of seimogenic emissions by satellites. To verify the reliability of the above widely disseminated results data from available arrays of fluxgate and search-coil magnetometers have been re-analyzed. In all considered events, the “anomalous” geomagnetic field behavior can be explained by global geomagnetic activity, and it is apparently not associated with seismic activity. This critical review does not claim that ULF electromagnetic field cannot be used as a sensitive indicator of the EQ preparation processes, but we suggest that both communities must cooperate their studies more tightly using data exchange, combined usage of magnetometer networks, organization of CDAW for unique events, etc.

DAS sensitivity to heterogeneity scales much smaller than the minimum wavelength

Distributed Acoustic Sensing (DAS) is a photonic technology allowing toconvert fiber-optics into long (tens of kilometers) and dense (every few meters) arrays of seismo-acoustic sensors which are basically measuring the strain of the cable all along the cable. The potential of such a distributed measurement is very important and has triggered strong attention in the seismology community for a wide range of applications. In this work, we focus on the interaction of such measurements with heterogeneities of scale much smaller than the wavefield minimum wavelength. With a simple 2-D numerical modeling, we first show that the effect of such small-scale heterogeneities, when located in the vicinity of the instruments, is very different depending on whether we measure particle velocity or strain rate: in the case of velocity, this effect is small but becomes very strong in the case of the strain rate. We then provide a physical explanation of these observations based on the homogenization method showing that indeed, the strain sensitivity to nearby heterogeneities is strong, which is not the case for more traditional velocity measurements. This effect appears as a coupling of the strain components to the DAS measurement. Such effects can be seen as a curse or an advantage depending on the applications.

Elevated collapse risk based on decaying aftershock hazard and damaged building fragilities

This article proposes a framework to support postearthquake building safety and reoccupancy decisions by quantifying the change in building collapse risk following a mainshock earthquake event. This risk may be exacerbated by both an increase in seismic hazard due to aftershock activity and a reduction in building collapse resistance due to structural damage. To address these factors, the framework is based on a hazard that includes (1) both the steady-state and the aftershock occurrence rates, that is, the elevated hazard that accounts for the dependence on the mainshock magnitude and the aftershock rate that decays over time, and (2) revised collapse fragility functions that account for structural damage sustained during the mainshock. The framework is capable of addressing region-specific questions such as (1) What are the mainshock magnitudes for which aftershocks pose a life-safety concern? (2) How long does it take for the elevated risk due to aftershocks to dissipate? and (3) What gaps in current knowledge deserve further attention from the earthquake engineering and seismology communities? The framework addresses these questions for a 20-story building in San Francisco, assuming three different, hypothetical mainshock events of magnitudes 7,7.5, and 8 M W on the San Andreas fault. This is followed by a parametric study that considers a range of buildings and provides a graphical representation of the elevated risk to inform building evaluation (tagging) decisions, based on the intact building’s collapse capacity, the amount of structural damage, and the length of time after the mainshock.

Unsupervised Deep Feature Learning for Icequake Discrimination at Neumayer Station, Antarctica

Abstract Unsupervised machine learning methods are gaining attention in the seismological community as more and larger datasets of continuous waveforms are collected. Recently, contrastive learning for unsupervised feature learning has shown great success in the field of computer vision and other domains, and we aim to transfer these methods to the domain of seismology. Contrastive learning algorithms use data augmentation to implement an instance-level discrimination task: The feature representations of two augmented versions of the same data example are trained to be similar, when at the same time dissimilar to other data examples. In particular, we use the popular contrastive learning method SimCLR. We test data augmentation strategies varying amplitude and frequency of seismological signals, and apply contrastive learning methods to automatically learn features. We use a dataset containing various mostly cryogenic waveforms detected by an STA/LTA short-term average/long-term average algorithm on continuous waveform recordings from the geophysical observatory at Neumayer station, Antarctica. The quality of the features is evaluated on a hand-labeled dataset that includes icequakes, earthquakes, and spikes, and on a larger unlabeled dataset using a classical clustering method, k-means. Results show that the approach separates the different hand-labeled groups with an accuracy of up to 88% and separates meaningful groups within the unlabeled data. Thus, we provide an effective tool for the unsupervised exploration of large seismological datasets and the automated compilation of event catalogs.

The Global DAS Month of February 2023

Abstract During February 2023, a total of 32 individual distributed acoustic sensing (DAS) systems acted jointly as a global seismic monitoring network. The aim of this Global DAS Month campaign was to coordinate a diverse network of organizations, instruments, and file formats to gain knowledge and move toward the next generation of earthquake monitoring networks. During this campaign, 156 earthquakes of magnitude 5 or larger were reported by the U.S. Geological Survey and contributors shared data for 60 min after each event’s origin time. Participating systems represent a variety of manufacturers, a range of recording parameters, and varying cable emplacement settings (e.g., shallow burial, borehole, subaqueous, and dark fiber). Monitored cable lengths vary between 152 and 120,129 m, with channel spacing between 1 and 49 m. The data has a total size of 6.8 TB, and are available for free download. Organizing and executing the Global DAS Month has produced a unique dataset for further exploration and highlighted areas of further development for the seismological community to address.

Seismology in the cloud: guidance for the individual researcher

The commercial cloud offers on-demand computational resources that could be revolutionary for the seismological community, especially as seismic datasets continue to grow. However, there are few educational examples for cloud use that target individual seismological researchers. Here, we present a reproducible earthquake detection and association workflow that runs on Microsoft Azure. The Python-based workflow runs on continuous time-series data using both template matching and machine learning. We provide tutorials for constructing cloud resources (both storage and computing) through a desktop portal and deploying the code both locally and remotely on the cloud resources. We report on scaling of compute times and costs to show that CPU-only processing is generally inexpensive, and is faster and simpler than using GPUs. When the workflow is applied to one year of continuous data from a mid-ocean ridge, the resulting earthquake catalogs suggest that template matching and machine learning are complementary methods whose relative performance is dependent on site-specific tectonic characteristics. Overall, we find that the commercial cloud presents a steep learning curve but is cost-effective. This report is intended as an informative starting point for any researcher considering migrating their own processing to the commercial cloud.

A WebGPU-based acoustic wave simulator for ultrasound NDT

Ultrasound NDT is a standard industrial technique for health monitoring and flaw identification in several structures, such as subsea oil pipelines. Its primary purpose is to form a representative image of the internal structure of the inspected object, which provides, in many cases, early warning of structure discontinuities, making it possible to reduce repair costs and mitigate risks. One of the imaging methods that has been receiving increasing attention in the ultrasound and seismology communities is the Full Waveform Inversion (FWI), which estimates the velocity model for the inspection area by confronting the acquired information obtained by the NDT system with theoretical simulated data. Since FWI algorithms rely on successive ultrasonic wave simulations, it is essential to ensure that each simulation is optimized and contributes little to the total running time. Acoustic wave simulations can be optimized using a graphical processing unit (GPU), which provides hardware acceleration through parallelism, where each point of the simulated area can be computed simultaneously. Among all available GPU APIs, WebGPU is the next-generation standard graphics Web API that exposes modern computer graphics capabilities by giving the user low-level, general-purpose access to the GPUs. This API is designed to efficiently map to native GPU APIs, which enables applications to run on different GPUs, making implementations more scalable and interchangeable. Motivated by this flexibility, we explore the acceleration capabilities of the WebGPU API by implementing a WebGPU-based acoustic wave simulator for ultrasound NDT.

Data‐driven 1D wave propagation for site response analysis

AbstractPrediction of ground motion triggered by earthquakes is a prime concern for both the seismology community and geotechnical earthquake engineering one. The subfield occupied with such a problem is termed site response analysis (SRA), its one‐dimensional flavor (1D‐SRA) being particularly popular given its simplicity. Despite the simple geometrical setting, a paramount challenge remains when it comes to numerically consider intense shaking in the soft upper soil strata of the crust: how to mathematically model the high‐strain, dissipative, potentially rate‐dependent soil behavior. Both heuristics models and phenomenological constitutive laws have been developed to meet the challenge, neither of them being exempt of either numerical limitations or physical inadequacies or both. We propose herein to bring the novel data‐driven paradigm to bear, thus giving away with the need to construct constitutive behavior models altogether. Data‐driven computational mechanics (DDCM) is a novel paradigm in solid mechanics that is gaining popularity; in particular, the multiscale version of it relies on studying the response of the microstructure (in the case of soil, representative volumes containing grains) to populate a dataset that is later used to inform the response at the macroscale. This article presents the first application of multiscale DDCM to 1D‐SRA: first, we demonstrate its capacity to handle wave propagation problems using discrete datasets, obtained via sampling grain ensembles using the discrete element method (DEM), in lieu of a constitutive law and then we apply it specifically to analyze the propagation of harmonic waves in a soft soil deposit that overlies rigid bedrock. The soil in this application displays elastic yet non‐linear response that is depth‐dependent due to evolving overburden pressure. We validate the implementation via comparison to regular finite elements analyses (FEA), discuss the benefits of using DD, and demonstrate that traditional amplification functions are recovered when using the DDCM. Finally, we discuss a number of future work opportunities that lie ahead of this proof‐of‐concept, chiefly in terms of site‐specific studies and incorporating more complex realistic traits of soil behavior. This project was developed using open‐source software and the relevant code is freely made available to other researchers.

Against Båth’s Law: When Aftershocks Became Mainshocks—Implications for Earthquake Forecasting Communication

Abstract The Båth’s law is an empirical seismological relation between the magnitudes of the mainshock and the largest aftershock of a seismic sequence. This empirical law, differently from other seismological laws, could be valid only when the seismic sequence is ended. Indeed, during the sequence, we do not know if the strongest event has already happened or not, and then inferring something about the magnitude of the largest aftershock is hazardous. In this opinion article, we discuss some issues related to the Båth’s law: its validity on a global catalog, the use of the terms “mainshock” and “aftershock” in the seismological community and in the public, and their implications in earthquake forecasting communications. We show the uselessness of Båth’s law in earthquake forecasting and that the words “mainshock” and “aftershock” have different interpretations for the public and for seismologists. We argue that their use during an ongoing seismic sequence, without a proper explanation of their meaning, could be confounding, in particular for the Italian language. Calling all events just “earthquakes” could be a simple but effective solution.

Inconsistent Citation of the Global Seismographic Network in Scientific Publications

Abstract The highly used Global Seismographic Network (GSN) is a pillar of the seismological research community and contributes to numerous groundbreaking publications. Despite its wide recognition, this survey found that the GSN is not consistently acknowledged in scientific literature and is underrepresented by roughly a factor of 3 in citation searches. Publication tracking is a key metric that factors into operational decisions and funding support for the network; thus, consistent and proper citation of the GSN is important. This study not only serves as a reminder for researchers using GSN observations to cite the network’s digital object identifiers (DOIs) but also promotes a community-wide conversation among researchers, journal editors, network operators, and other stakeholders regarding more standardized policies and review processes to ensure seismic networks are properly and consistently recognized for their contributions to research.

TIMCPOT: Tcl/Tk Interface for Measuring Crustal Phase-Onset Time

Abstract In recent years, the seismological community has seen an amazing increase in the amount of seismic arrival time picks with the help of artificial intelligence. But, the accuracy of this data remains a challenge to seismologists. In comparison with automatic picks, manual picks have sufficient reliability and accuracy because manual picks are often used not only as a benchmark to evaluate automatic picking algorithms, but also as training samples to train artificial intelligence models. However, measurement through manual labor is a laborious and time-consuming task. To alleviate the manual labor involved in measuring phase arrival time, we developed a graphical interface named Tcl/Tk Interface for Measuring Crustal Phase-Onset Time (TIMCPOT). The TIMCPOT is mainly equipped with two modules: one is designed for measuring seismic arrival time, and the other is for relocating hypocenter. TIMCPOT simplifies user interaction with seismic data, thus reducing most manual labor. TIMCPOT provides several graphical workflows to carry out detection, identification, revision, and comparison operations on measurement work. The measurement procedure can be operated either manually or by autopicker. For the sake of allowing the user to interact easily with seismic data, TIMCPOT is designed to be executed in a semiautomatic model during several necessary procedures such as visually inspecting results and manually modifying picks. By applying a near real-time displaying technique, TIMCPOT can create a travel time curve figure and comparison chart for manual picks in a timely fashion, which provides a rapid way to estimate data quality. The measurement efficiency and travel time data quality have been significantly improved using this graphical interface. An explicit demonstration was shown to validate the powerful performance mentioned earlier. The primary advantage of TIMCPOT is that it reduces manual labor while retaining valuable manual experience on measuring phase picks. Of course, TIMCPOT has several limitations. However, most limitations will be improved in future development.

William “Willie” Hung Kang Lee (1940–2022)

Obituary| January 12, 2023 William “Willie” Hung Kang Lee (1940–2022) Bill Ellsworth; Bill Ellsworth 1U.S. Geological Survey (retired), Stanford University, Stanford, California, U.S.A. https://orcid.org/0000-0001-8378-4979 Search for other works by this author on: GSW Google Scholar Steve Kirby Steve Kirby * 2U.S. Geological Survey, California, U.S.A. *Corresponding author: stevenlyle@icloud.com Search for other works by this author on: GSW Google Scholar Seismological Research Letters (2023) https://doi.org/10.1785/0220220389 Article history first online: 12 Jan 2023 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Bill Ellsworth, Steve Kirby; William “Willie” Hung Kang Lee (1940–2022). Seismological Research Letters 2023; doi: https://doi.org/10.1785/0220220389 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search The seismological community has lost one of its leaders and pioneers who led the creation of modern seismic networks while maintaining a sharp focus on the importance and value of the early instrumental and historical record. William “Willie” Hung Kang Lee was also a pioneer in such global seismology fields as seismic tomography. The Earth science literature abounds with his papers on diverse subjects under his frequently used moniker, W. H. K. Lee. Willie was born in wartime Guangxie, southern China on October 6, 1940, the sixth of seven children. His family fled to Macao in 1950 and then to... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

RFloc3D: A Machine-Learning Method for 3-D Microseismic Source Location Using P- and S-Wave Arrivals

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.

Ab initio calculations of third-order elastic coefficients

Third-order elasticity (TOE) theory predicts strain-induced changes in second-order elastic coefficients (SOECs) and can model elastic wave propagation in stressed media. Although third-order elastic tensors have been determined based on first principles in previous studies, their current definition is based on an expansion of thermodynamic energy in terms of the Lagrangian strain near the natural, or zero pressure, reference state. This definition is inconvenient for predictions of SOECs under significant initial stresses. Therefore, when TOE theory is necessary to study the strain dependence of elasticity, the seismological community has resorted to an empirical version of the theory. This study reviews the thermodynamic definition of the third-order elastic tensor and proposes using an ``effective'' third-order elastic tensor. An explicit expression for the effective third-order elastic tensor is given and verified. We extend the ab initio approach to calculate third-order elastic tensors under finite pressure and apply it to two cubic systems, namely, NaCl and MgO. As applications and validations, we evaluate (a) strain-induced changes in SOECs and (b) pressure derivatives of SOECs based on ab initio calculations. Good agreement between third-order elasticity-based predictions and numerically calculated values confirms the validity of our theory.