Seismological Applications Research Articles

Probabilistic modeling of extreme events involving decaying variables with an application in seismology

Probabilistic modeling of extreme events involving decaying variables with an application in seismology

Change of measure in a Heston–Hawkes stochastic volatility model

Abstract We consider the stochastic volatility model obtained by adding a compound Hawkes process to the volatility of the well-known Heston model. A Hawkes process is a self-exciting counting process with many applications in mathematical finance, insurance, epidemiology, seismology, and other fields. We prove a general result on the existence of a family of equivalent (local) martingale measures. We apply this result to a particular example where the sizes of the jumps are exponentially distributed. Finally, a practical application to efficient computation of exposures is discussed.

Basin‐scale prediction of S‐wave Sonic Logs using Machine Learning techniques from conventional logs

AbstractS‐wave velocity plays a crucial role in various applications but often remains unavailable in vintage wells. To address this practical challenge, we propose a machine learning framework utilizing an enhanced bidirectional long short‐term memory algorithm for estimating S‐wave sonic logs from conventional logs, including P‐wave sonic, gamma ray, total porosity, and bulk density. These input logs are selected based on traditional rock physics models, integrating geological and geophysical relations existing in the data. Our study, encompassing 34 wells across diverse formations in the Delaware Basin, Texas, demonstrates the superiority of machine learning models over traditional methods like Greenberg–Castagna equations, without prior geological and geophysical information. Among these machine learning models, the enhanced bidirectional long short‐term memory model with self‐attention yields the highest performance, achieving an R‐squared value of 0.81. Blind tests on five wells without prior geologic information validate the reliability of our approach. The estimated S‐wave velocity values enable the creation of a basin‐scale S‐wave velocity model through interpolation and extrapolation of these prediction models. Additionally, the bidirectional long short‐term memory model excels not only in predicting S‐wave velocity but also in estimating S‐wave reflectivity for seismic amplitude variation with offset applications in exploration seismology. In conclusion, these S‐wave velocity estimates facilitate the prediction of further elastic properties, aiding in the comprehension of petrophysical and geomechanical property variations within the basin and enhancing earthquake hypocentral depth estimation.

Effect of three-phase-lag thermal and three-phase-lag diffusion models on waves at the boundary of elastic and thermoelastic diffusion medium

In this paper, we discuss the reflection and refraction of an incident P wave or [Formula: see text] wave at the interface of a plane. The plane, which is divided into two halves, is an elastic medium [Formula: see text] having an incident wave and a thermoelastic diffusion medium [Formula: see text] with TPLT (i.e., three-phase-lag thermal) and TPLD (i.e., three-phase-lag diffusion) models. It has been noticed that two waves are reflected and four are refracted in an isotropic thermoelastic diffusion medium. Out of the four refracted waves, three are longitudinal waves: a quasi-longitudinal wave [Formula: see text] a quasi-mass diffusion wave [Formula: see text], a quasi-thermal wave [Formula: see text] and one is a transverse wave [Formula: see text]. If we consider the above waves first, the amplitude and energy ratio are calculated by using the surface boundary conditions and then graphically represented to compare the change in energy and amplitude ratio with the change in incident angle for three particular cases. The conservation of energy is depicted by verifying that all the energy sums up to unity. The considered problem has its application in earthquake engineering, astronautics, rocket engineering, seismology and many more engineering areas.

Pointwise density estimation on metric spaces and applications in seismology

AbstractWe are studying the problem of estimating density in a wide range of metric spaces, including the Euclidean space, the sphere, the ball, and various Riemannian manifolds. Our framework involves a metric space with a doubling measure and a self-adjoint operator, whose heat kernel exhibits Gaussian behaviour. We begin by reviewing the construction of kernel density estimators and the related background information. As a novel result, we present a pointwise kernel density estimation for probability density functions that belong to general Hölder spaces. The study is accompanied by an application in Seismology. Precisely, we analyze a globally-indexed dataset of earthquake occurrence and compare the out-of-sample performance of several approximated kernel density estimators indexed on the sphere.

A Seismic Monitoring Tool for Tidal-Forced Aquifer Level Changes in the Río de la Plata Coastal Plain, Argentina

Ambient seismic noise has gained extensive applications in seismology and plays a pivotal role in environmental seismic studies. This study focuses on the Río de la Plata Coastal Plain, employing the horizontal-to-vertical spectral ratio (HVSR) method on ambient seismic noise records to analyze subsurface dynamics. The region’s hydrogeology is complex, featuring partially interconnected coastal aquifers. The HVSR analysis reveals two peaks, with P0 associated with the sediment-basement interface and P1 linked to a shallower stratigraphic discontinuity. Temporal analysis of P1 highlights cyclical patterns correlated with estuarine levels, suggesting a relationship between variations in seismic velocities and tidal dynamics. Comparisons with aquifer data support the hypothesis that tidal variations influence subsurface mechanical properties, impacting the HVSR function. The study hints at the potential of ambient seismic noise analysis as a non-invasive and cost-effective method for studying coastal aquifers and understanding groundwater dynamics. Ongoing research aims to further explore these relationships for enhanced groundwater resource management.

TXED: The Texas Earthquake Dataset for AI

Abstract Machine-learning (ML) seismology relies on large datasets with high-fidelity labels from humans to train generalized models. Among the seismological applications of ML, earthquake detection, and P- and S-wave arrival picking are the most widely studied, with capabilities that can exceed humans. Here, we present a regional artificial intelligence (AI) earthquake dataset (TXED) compiled for the state of Texas. The TXED dataset is composed of earthquake signals with manually picked P- and S-wave arrival times and manually picked noise waveforms corresponding to more than 20,000 earthquake events spanning from the beginning of the Texas seismological network (TexNet) (1 January 2017) to date. These data are a supplement to the existing worldwide open-access seismological AI datasets and represent the signal and noise characteristics of Texas. Direct applications of the TXED datasets include improving the performance of a global picking model in Texas by transfer learning using the new dataset. This dataset will also serve as a benchmark dataset for fundamental AI research like designing seismology-oriented deep-learning architectures. We plan to continue to expand the TXED dataset as more observations are made by TexNet analysts.

Fractional Criticality Theory and Its Application in Seismology

To understand how the temporal non-locality («memory») properties of a process affect its critical regimes, the power-law compound and time-fractional Poisson process is presented as a universal hereditary model of criticality. Seismicity is considered as an application of the theory of criticality. On the basis of the proposed hereditarian criticality model, the critical regimes of seismicity are investigated. It is shown that the seismic process has the property of «memory» (non-locality over time) and statistical time-dependence of events. With a decrease in the fractional exponent of the Poisson process, the relaxation slows down, which can be associated with the hardening of the medium and the accumulation of elastic energy. Delayed relaxation is accompanied by an abnormal increase in fluctuations, which is caused by the non-local correlations of random events over time. According to the found criticality indices, the seismic process is in subcritical regimes for the zero and first moments and in supercritical regimes for the second statistical moment of events’ reoccurrence frequencies distribution. The supercritical regimes indicate the instability of the deformation changes that can go into a non-stationary regime of a seismic process.

An advanced median filter for improving the signal-to-noise ratio of seismological datasets

Strong noise usually disturbs the recorded seismic waves, resulting in low-quality seismic data sometimes with an extremely low signal-to-noise ratio (S/N), which negatively impacts the subsequent seismological processes, e.g., imaging, inversion, and interpretation. Suppressing undesirable noise is a meaningful procedure to increase the S/N of seismological datasets. This issue can be partially addressed using the recently proposed structure-oriented space-varying median filter (SOSVMF) by filtering out the unwanted noise from noise-corrupted signals. However, the SOSVMF approach has limitations when the raw data is corrupted by various sources of noise which is more common in practice. To conquer the difficulties in improving the S/N in the face of diverse types of strong noise, we introduce MATamf, an open-source MATLAB code package based on a novel advanced median filter (AMF). The proposed AMF workflow takes advantage of multiple denoising operators, i.e., the bandpass (BP) filter, SOSVMF, dip filter in the frequency–wavenumber (FK) domain, Curvelet, and local orthogonalization (LO), all in the same framework to simultaneously attenuate all types of noise and improve S/N. To demonstrate the usefulness of the MATamf package and its advantage over conventional denoising methods, we introduce the principles behind the proposed AMF framework and present results from a variety of seismological problems, including seismic reflection, distributed acoustic sensing (DAS), as well as global seismological tasks of receiver function and SS-precursors imaging. All results show that the proposed AMF workflow can be conveniently utilized to improve the S/N of a wide spectrum of seismological applications.

A deep-learning-based model for quality assessment of earthquake-induced ground-motion records

High-quality earthquake ground-motion records are required for various applications in engineering and seismology; however, quality assessment of ground-motion records is time-consuming if done manually and poorly handled by automation with conventional mathematical functions. Machine learning is well suited to this problem, and a supervised deep-learning-based model was developed to estimate the quality of all types of ground-motion records through training on 1096 example records from earthquakes in New Zealand, which is an active tectonic environment with crustal and subduction earthquakes. The model estimates a quality and minimum usable frequency for each record component and can handle one-, two-, or three-component records. The estimations were found to match manually labeled test data well, and the model was able to accurately replicate manual quality classifications from other published studies based on the requirements of three different engineering applications. The component-level quality and minimum usable frequency estimations provide flexibility to assess record quality based on diverse requirements and make the model useful for a range of potential applications. We apply the model to enable automated record classification for 43,398 ground motions from GeoNet as part of the development of a new curated ground-motion database for New Zealand.

DRR: An open-source multi-platform package for the damped rank-reduction method and its applications in seismology

We present an open-source multi-platform package for the damped rank-reduction (DRR) method, that has been widely used in various problems in seismology. The DRR method is an optimally improved rank-reduction (RR) method by damping the singular values using an analytically derived threshold. The DRR method can separate the useful signal and unwanted noise in multi-channel seismic data better than the standard RR method, resulting in spatially more coherent seismic events without damaging the critical subtleties. There have been some advanced methodological improvements or ingenious implementations of the DRR method in the past decade, as well as dozens of new applications in different areas of seismology, most of which will be included in the presented package. The DRR package is composed of two parts: MATdrr and Pydrr, corresponding to the Matlab and Python versions, respectively. The current DRR package includes localized DRR, optimally damped rank-reduction (ODRR), off-the-grid reconstruction, de-aliased reconstruction, and many reproducible examples in different areas of seismology. The DRR package will be continuously developed to include more functionalities in the future.

Dynamic curvature sensing using time expanded ΦOTDR.

Shape sensing can be accomplished using optical fiber sensors through different interrogation principles such as fiber Bragg gratings, optical frequency-domain reflectometry (OFDR), or optical time-domain reflectometry (OTDR). These techniques are either not entirely distributed, have poor performance in dynamic sensing, or are only valid for few-meter-long fibers. Here, we present a system able to perform distributed curvature sensing with a range of 125 m, 10-cm resolution, and a sampling rate of 50 Hz. This is done by interrogating three cores of a multi-core fiber (MCF) with the novel, to the best of our knowledge, time-expanded phase-sensitive (TE-Φ)OTDR technique. This system fills a performance gap in fiber shape sensors, opening the door to applications in civil engineering, medicine, or seismology.

Dual-polarization interferometric fiber optic gyroscope with Shupe effect compensation

Dual-polarization (DP) interferometric fiber optic gyroscope (IFOG) has been studied for over a decade, which has important applications in inertial navigation and rotational seismology. Currently, the Shupe effect is an important factor degrading the accuracy of the DP IFOG. Here, a dual-quadrupolar winding pattern is proposed to compensate for the Shupe effect. The finite-element simulation experiments verify that the phase shifts induced by the Shupe effect in two polarizations are almost reversed and well compensated. Static laboratory test results show that, in this configuration, the angle random walk is reduced 10-fold to 7.1×10−5 °/h, while the bias instability is reduced ninefold to 1.7×10−4 °/h. Moreover, a 3-h temperature variation experiment is carried out. The results show that, compared with the original output, the net rotation rate errors originating from the Shupe effect are effectively compensated and the temperature drift is reduced by 13 times. To summarize, the dual-quadrupolar-wound method is a valid approach to compensate for the Shupe effect, and the temperature adaptability of this DP configuration is greatly enhanced.

Advanced Signal Processing in Distributed Acoustic Sensors Based on Submarine Cables for Seismology Applications

Photonic integrated sensing and communication technology based on optical fibers has recently attracted great research attention. In particular, distributed acoustic sensing (DAS) with telecommunication fibers is an emerging technology in the research areas of geology and seismology. With the massive deployment of telecommunication submarine cables, it provides an effective and low-cost approach for studying various oceanic geological and seismic activities. Though there have been some demonstrations of using DAS with submarine cables for the oceanic and geological analysis, no systematic analysis of the method has been presented, especially the signal processing techniques employed for the study. In this work, we discuss the signal processing procedure adopted to carry out seismic analysis using data obtained with the underwater DAS in detail, including the preprocessing of raw DAS data, the seismic recognition after feature extraction and event classification, and the seismic analysis of localization and magnitude estimations. Based on these signal processing methods, we successfully detect and analyze different seismic activities with our underwater DAS testbed, which utilizes the telecommunication submarine cable between two islands in the Pearl River estuary area of South China Sea.

Processing Ambient Noise Data Using Phase Cross‐Correlation and Application Toward Understanding Spatiotemporal Environmental Effects

AbstractTypical use of ambient noise interferometry focuses on longer period (>1 s) waves for exploration of subsurface structure and other applications, while very shallow structure and some environmental seismology applications may benefit from use of shorter period (<1 s) waves. We explore the potential for short‐period ambient noise interferometry to determine shallow seismic velocity structures by comparing two methodologies, the conventional amplitude‐based cross‐correlation and linear stacking (TCC‐Lin) and a more recently developed phase cross‐correlation and time‐frequency phase‐weighted‐stacking (PCC‐PWS) method with both synthetic and real data collected in a heterogeneous karst aquifer system. Our results suggest that the PCC‐PWS method is more effective in extracting short‐period wave velocities than the TCC‐Lin method, especially when using data collected in regions containing complex shallow structures such as the karst aquifer system investigated here. In addition to the different methodologies for computing the cross correlation functions, we also examine the relative importance of signal‐to‐noise ratio and number of wavelengths propagating between station pairs to determine data/solution quality. We find that the lower number of wavelengths of 3 has the greatest impact on the network‐averaged group velocity curve. Lastly, we test the sensitivity of the number of stacks used to create the final empirical Green's function, and find that the PCC‐PWS method required about half the number of cross‐correlation functions to develop reliable velocity curves compared to the TCC‐Lin method. This is an important advantage of the PCC‐PWS method when available data collection time is limited.

Applications and limitations of distributed acoustic sensing in shallow seismic surveys and monitoring

Distributed acoustic sensing (DAS) is an emerging technology gaining acceptance in a variety of seismological applications. We systematically analyze the adaptability and usefulness of horizontal DAS deployments for near-surface geophysical applications, such as surface wave inversion, diving wave tomography, and passive subsurface source location. We find that, in accordance with previous studies, DAS data are generally similar to traditional sensors and can be successfully used independently. Nonetheless, DAS data suffer from inherent limitations due to the design of the optical measurement system. Among others, we identify the gauge length, measurement directivity, and saturation at near offsets as the primary limiting factors. When operating in low-velocity environments and a standard 10 m gauge length, surface wave analysis may be constrained to the usage of low frequencies due to the wavenumber filtering effect of the gauge length. The measurement directivity generally prohibits applications that are based on upgoing P-wave energy, such as near-offset diving wave tomography. In addition, saturation at near offsets prevents reliable diving wave traveltime picking. As a consequence of these limitations, the ultrashallow resolution achievable with DAS data is poor. Source directivity also strongly limits passive source location resolution. Whereas some of the limitations can be alleviated through an optimal choice of optical parameters at the interrogator level, we mostly rely on adapted field acquisition and data processing workflows that use the undisturbed portion of the recorded signal. Eventually, the spatiotemporal resolution and relative ease of long-term deployments turn DAS into a worthwhile option for monitoring and time-lapse scenarios, or when operating in urban environments over existing infrastructure.

Principles and Applications of Seismic Monitoring Based on Submarine Optical Cable

Submarine optical cables, utilized as fiber-optic sensors for seismic monitoring, are gaining increasing interest because of their advantages of extending the detection coverage, improving the detection quality, and enhancing long-term stability. The fiber-optic seismic monitoring sensors are mainly composed of the optical interferometer, fiber Bragg grating, optical polarimeter, and distributed acoustic sensing, respectively. This paper reviews the principles of the four optical seismic sensors, as well as their applications of submarine seismology over submarine optical cables. The advantages and disadvantages are discussed, and the current technical requirements are concluded, respectively. This review can provide a reference for studying submarine cable-based seismic monitoring.

Improved seismic envelope full-waveform inversion

The envelope full-waveform-inversion method has the potential to greatly improve the estimation of long-wavelength components of subsurface seismic velocity, and simultaneously avoid or reduce inversion cycle skipping, by fitting the envelopes of observed and modeled seismic waveform data, rather than fitting the waveforms directly. However, some issues such as instability of the adjoint source and gradient term make it challenging to use in practice for many seismology applications. We examine the envelope inversion (EI) methods with different envelope function exponent p values from an adjoint source analysis perspective and reveal the adverse effects of the adjoint source weighting term on the data residuals, which degrade or mute important inversion gradient contributions, especially from reflected or scattered phases in the seismic data. We develop the theory and implementation for an improved EI (IEI) method by adding an upshift constant (zero-frequency direct current [DC] bias term) to the seismic waveform data before calculating the envelope functions, which can help solve the instability issue of the adjoint source of the EI with power value p = 1. Our approach is different than the common but simple addition of a “water-level” damping constant in the denominator of the data residual-weighting term, which can ignore important information contained in the data residuals. Our IEI method becomes a mixed envelope-waveform objective function, which introduces a new adjoint source weighting term that can preserve all of the information contained in the data residuals, by boosting the low-amplitude phases of the seismic waveform, while simultaneously preserving the correct envelope (energy shape) of the high-amplitude phases. We test the performance of all three methods in detail using the highly realistic SEAM4D model and data and find the advantages of our new IEI method compared with conventional EI and full-waveform inversion methods.

Introduction to the Digitization of Seismic Data: A User’s Guide

Abstract Modern seismic data are collected, distributed, and analyzed using digital formats, and this has become a standard for the field. Although most modern seismometers still make use of analog electronic circuits, their data are converted from an analog voltage output to time-tagged counts by way of digitization. Although much of the digitization process is not complicated to conceptualize, there is a fair bit of jargon in digitizer specifications, and a few pitfalls that can arise in the processes of recording and analyzing ground-motion data. In this article, we review some of the fundamental physical properties of data acquisition systems and the basic steps in digitizing data from an analog instrument (specifically a seismometer). We then briefly discuss the digitization process and some of the key properties needed to make these data useful for seismological applications. Finally, we discuss some of the filtering processes that naturally arise from digitization and how it can affect the processing workflow. The end goal is to provide a user guide that will enable seismologists to have a working knowledge of the digitization process. We focus on aspects central to seismological applications and have tried to avoid getting bogged down in signal processing formalism.

Curated Pacific Northwest AI-ready Seismic Dataset

The curation of seismic datasets is the cornerstone of seismological research and the starting point of machine-learning applications in seismology. We present a 21-year-long AI-ready dataset of diverse seismic event parameters, instrumentation metadata, and waveforms, as curated by the Pacific Northwest Seismic Network and ourselves. The dataset contains about 190,000 three-component (3C) waveform traces from more than 65,000 earthquake and explosion events, and about 9,200 waveforms from 5,600 exotic events. The magnitude of the events ranges from 0 to 6.4, while the biggest one is 20 December 2022 M6.4 Ferndale Earthquake. We include waveforms from high-gain (EH, BH, and HH channels) and strong-motion (EN channels) seismometers and resample to 100 Hz. We describe the earthquake catalog and the temporal evolution of the data attributes (e.g., event magnitude type, channel type, waveform polarity, and signal-tonoise ratio, phase picks) as the network earthquake monitoring system evolved through time. We propose this AI-ready dataset as a new open-source benchmark dataset.