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Meta-attributes and artificial networking : a new tool for seismic interpretation
\"Overview of meta-attributes and how to design them. Case studies demonstrating the application of meta-attributes. Sample data sets available for hands-on exercises. The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals\"-- Provided by publisher.
Book
Imaging, Modeling and Assimilation in Seismology
This work presents current approaches in geophysical research of earthquakes. A global authorship from top institutions presents case studies to model, measure, and monitor earthquakes. Among others a full-3D waveform tomography method is introduced, as well as propagator methods for modeling and imaging. In particular the earthquake prediction method makes this book a must-read for researchers in the field.
eBook
Computational seismology : a practical introduction
This volume is an introductory text to a range of numerical methods used today to simulate time-dependent processes in Earth science, physics, engineering, and many other fields. The physical problem of elastic wave propagation in 1D serves as a model system with which the various numerical methods are introduced and compared. The theoretical background is presented with substantial graphical material supporting the concepts. The results can be reproduced with the supplementary electronic material provided as Python codes embedded in Jupyter notebooks. The volume starts with a primer on the physics of elastic wave propagation, and a chapter on the fundamentals of parallel programming, computational grids, mesh generation, and hardware models. The core of the volume is the presentation of numerical solutions of the wave equation with six different methods: (1) the finite-difference method; (2) the pseudospectral method (Fourier and Chebyshev); (3) the linear finite-element method; (4) the spectral-element method; (5) the finite-volume method; and (6) the discontinuous Galerkin method. Each chapter contains comprehension questions, and theoretical and programming exercises. The volume closes with a discussion of domains of application and criteria for the choice of a specific numerical method, and the presentation of current challenges.
eBook
Spatiotemporal clustering: a review
An increase in the size of data repositories of spatiotemporal data has opened up new challenges in the fields of spatiotemporal data analysis and data mining. Foremost among them is “spatiotemporal clustering,” a subfield of data mining that is increasingly becoming popular because of its applications in wide-ranging areas such as engineering, surveillance, transportation, environmental and seismology studies, and mobile data analysis. This review paper presents a comprehensive review of spatiotemporal clustering approaches and their applications as well as a brief tutorial on the taxonomy of data types in the spatiotemporal domain and patterns. Additionally, the data pre-processing techniques, access methods, cluster validation, space–time scan statistics, software tools, and datasets used by various spatiotemporal clustering algorithms are highlighted.
Journal Article
SEIS: Insight’s Seismic Experiment for Internal Structure of Mars
By the end of 2018, 42 years after the landing of the two Viking seismometers on Mars, InSight will deploy onto Mars’ surface the SEIS (
S
eismic
E
xperiment for
I
nternal
S
tructure) instrument; a six-axes seismometer equipped with both a long-period three-axes Very Broad Band (VBB) instrument and a three-axes short-period (SP) instrument. These six sensors will cover a broad range of the seismic bandwidth, from 0.01 Hz to 50 Hz, with possible extension to longer periods. Data will be transmitted in the form of three continuous VBB components at 2 sample per second (sps), an estimation of the short period energy content from the SP at 1 sps and a continuous compound VBB/SP vertical axis at 10 sps. The continuous streams will be augmented by requested event data with sample rates from 20 to 100 sps. SEIS will improve upon the existing resolution of Viking’s Mars seismic monitoring by a factor of
∼
2500
at 1 Hz and
∼
200
000
at 0.1 Hz. An additional major improvement is that, contrary to Viking, the seismometers will be deployed via a robotic arm directly onto Mars’ surface and will be protected against temperature and wind by highly efficient thermal and wind shielding. Based on existing knowledge of Mars, it is reasonable to infer a moment magnitude detection threshold of
M
w
∼
3
at
40
∘
epicentral distance and a potential to detect several tens of quakes and about five impacts per year. In this paper, we first describe the science goals of the experiment and the rationale used to define its requirements. We then provide a detailed description of the hardware, from the sensors to the deployment system and associated performance, including transfer functions of the seismic sensors and temperature sensors. We conclude by describing the experiment ground segment, including data processing services, outreach and education networks and provide a description of the format to be used for future data distribution.
Journal Article
Devito (v3.1.0): an embedded domain-specific language for finite differences and geophysical exploration
We introduce Devito, a new domain-specific language for implementing high-performance finite-difference partial differential equation solvers. The motivating application is exploration seismology for which methods such as full-waveform inversion and reverse-time migration are used to invert terabytes of seismic data to create images of the Earth's subsurface. Even using modern supercomputers, it can take weeks to process a single seismic survey and create a useful subsurface image. The computational cost is dominated by the numerical solution of wave equations and their corresponding adjoints. Therefore, a great deal of effort is invested in aggressively optimizing the performance of these wave-equation propagators for different computer architectures. Additionally, the actual set of partial differential equations being solved and their numerical discretization is under constant innovation as increasingly realistic representations of the physics are developed, further ratcheting up the cost of practical solvers. By embedding a domain-specific language within Python and making heavy use of SymPy, a symbolic mathematics library, we make it possible to develop finite-difference simulators quickly using a syntax that strongly resembles the mathematics. The Devito compiler reads this code and applies a wide range of analysis to generate highly optimized and parallel code. This approach can reduce the development time of a verified and optimized solver from months to days.
Journal Article
SegPhase: development of arrival time picking models for Japan’s seismic network using the hierarchical vision transformer
Seismic phase picking is a fundamental task in seismology that is crucial for event detection and earthquake cataloging; however, manual analysis is impractical given the scale of modern seismic networks. We present SegPhase, a novel seismic arrival time picking model designed to efficiently process large-scale seismic data recorded by dense seismic networks in Japan. In contrast to conventional convolution-based models, SegPhase employs a hierarchical vision transformer structure that utilizes multi-head self-attention to dynamically focus on important waveform features, such as P- and S-wave onsets, noise, and coda waves. Compared to PhaseNet, the most widely used deep learning model, SegPhase improved arrival time match rates by ~ 11% and detected ~ 15% more events in continuous waveform tests, particularly enhancing the detection of small-magnitude events. Benchmark evaluations demonstrated that SegPhase achieved high classification performance in identifying P- and S-waves. We also examined the threshold of the output probability values when applying SegPhase to continuous waveforms for which the optimal threshold was unknown. By lowering the threshold to 0.1, we observed an increase in the number of detected events without noticeable changes in the hypocenter location error and observed–calculated discrepancies. This was achieved by more effectively utilizing high-probability picks, which further improved phase association. Based on these results, we recommend a threshold of 0.1 to enhance event detection while maintaining accurate arrival times. Our findings demonstrate that SegPhase enables robust arrival picking across diverse datasets and supports high-resolution seismic monitoring.
Graphical Abstract
Journal Article
Opportune detections of global P-wave propagation from microseisms interferometry
Global seismological observations are sensitive to oceanic forcings, namely microseisms. In addition to dominant surface waves, these sources generate body waves that travel through the deep structures of our planet. Despite these sources’ inherent complexity, interferometric methods allow isolating coherent waves for imaging applications. For a given station pair, only specific microseism events contribute to the illumination of a specific target. We propose an opportune workflow based on ocean sea state models to extract robust interferences. This approach is illustrated with a strong microseism source in the North Atlantic Ocean, occurring around December 9, 2014.
Journal Article
A new characteristic function to enhance earthquake detection abilities on Distributed Acoustic Sensing data, DAS
The deployment of Distributed Acoustic Sensing (DAS) for seismic monitoring has significantly increased in recent years due to its numerous advantages over conventional seismic sensors. DAS has the potential to play a crucial complementary role along with classical seismic networks, particularly in logistic challenging areas such as offshore and volcanic environments. However, DAS data are inherently noisier than seismometer data, primarily due to fiber coupling issues and optical noise associated with the instrument. As a result, effective denoising and signal enhancement techniques are essential to fully exploit the advantages of DAS data. Recent efforts in improving the quality of DAS data have primarily focused on denoising algorithms (mostly deep learning-based) aimed at reducing coherent and background noise. However, improvements in terms of signal-to-noise ratio can also be achieved through the application of characteristic functions to raw or pre-processed data. To date, the application of these methods to DAS data have been largely unexplored, with the exception of few standard algorithms. In this study, we investigate the signal enhancement capability on DAS data of a new characteristic function based on the hyperbolic cosine. More specifically, we assess the performance of this function in improving the signal-to-noise ratio and compare the results against a set of more standard characteristic functions. Our analysis follows a two-step approach. First, we quantify the signal enhancement achieved through the application of the different characteristic functions by computing the signal-to-noise ratio of the preprocessed DAS data. In the second step, we evaluate their capability to enhance signal coherence across all fiber channels. This is achieved through the application of a coherence-based detector, which provides an estimate of the coherence as a function of time. Following a standardized denoising procedure, we systematically evaluate the impact of each characteristic function in increasing both the signal-to-noise ratio and coherence of DAS data. We conduct our analysis both on synthetic data and on a real dataset of 947 events recorded at the Frontier Observatory for Research in Geothermal Energy (FORGE) site, in Utah, USA.
Graphical Abstract
Journal Article
Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning
The continuously growing amount of seismic data collected worldwide is outpacing our abilities for analysis, since to date, such datasets have been analyzed in a human-expert-intensive, supervised fashion. Moreover, analyses that are conducted can be strongly biased by the standard models employed by seismologists. In response to both of these challenges, we develop a new unsupervised machine learning framework for detecting and clustering seismic signals in continuous seismic records. Our approach combines a deep scattering network and a Gaussian mixture model to cluster seismic signal segments and detect novel structures. To illustrate the power of the framework, we analyze seismic data acquired during the June 2017 Nuugaatsiaq, Greenland landslide. We demonstrate the blind detection and recovery of the repeating precursory seismicity that was recorded before the main landslide rupture, which suggests that our approach could lead to more informative forecasting of the seismic activity in seismogenic areas.
The authors here tackle the problem that too much seismic data is acquired worldwide to be evaluated in a timely fashion. Seydoux and colleagues develop a machine learning framework that can detect and cluster seismic signals in continuous seismic records.
Journal Article