Recent Advances and Challenges of Waveform‐Based Seismic Location Methods at Multiple Scales

Li, Lei; Tan, Jingqiang; Schwarz, Benjamin; Staněk, František; Poiata, Natalia; Shi, Peidong; Diekmann, Leon; Eisner, Leo; Gajewski, D.

doi:10.1029/2019rg000667

Cited by 128 publications

(66 citation statements)

References 287 publications

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“…Alternatively, attributes can be assessed in combination or help to establish cross-plotting maps (e.g. Endres et al, 2008;Lohr et al, 2008;Torrado et al, 2014;Wang et al, 2015). In this framework, coherent diffraction images can be viewed as physics-guided feature maps that naturally complement more conventional attributes commonly used for interpretation.…”

Section: B Schwarz and C M Krawczyk: Coherent Diffraction Imagingmentioning

confidence: 99%

Coherent diffraction imaging for enhanced fault and fracture network characterization

Schwarz

Krawczyk

2020

Solid Earth

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Abstract. Faults and fractures represent unique features of the solid Earth and are especially pervasive in the shallow crust. Aside from directly relating to crustal dynamics and the systematic assessment of associated risk, fault and fracture networks enable the efficient migration of fluids and therefore have a direct impact on concrete topics relevant to society, including climate-change-mitigating measures like CO2 sequestration or geothermal exploration and production. Due to their small-scale complexity, fault zones and fracture networks are typically poorly resolved, and their presence can often only be inferred indirectly in seismic and ground-penetrating radar (GPR) subsurface reconstructions. We suggest a largely data-driven framework for the direct imaging of these features by making use of the faint and still often underexplored diffracted portion of the wave field. Finding inspiration in the fields of optics and visual perception, we introduce two different conceptual pathways for coherent diffraction imaging and discuss respective advantages and disadvantages in different contexts of application. At the heart of both of these strategies lies the assessment of data coherence, for which a range of quantitative measures is introduced. To illustrate the versatility and effectiveness of the approach for high-resolution geophysical imaging, several seismic and GPR field data examples are presented, in which the diffracted wave field sheds new light on crustal features like fluvial channels, erosional surfaces, and intricate fault and fracture networks on land and in the marine environment.

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Section: B Schwarz and C M Krawczyk: Coherent Diffraction Imagingmentioning

confidence: 99%

Coherent diffraction imaging for enhanced fault and fracture network characterization

Schwarz

Krawczyk

2020

Solid Earth

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“…The origin time t 0 of an MS event can be regarded as an overall time-shift of all the recording waveforms. It can be eliminated in the data preprocessing step [11], then the M(x s )S (t − t 0 ) is shortened as S(t) for convenience and Equation (1) can be rewritten as Equation (2). The source-time function estimation strategy will be defined in Section 2.2.…”

Section: Synthetic Waveforms Based On the Reciprocity Theoremmentioning

confidence: 99%

“…For example, Witten and Shragge [9,10] developed a full wavefield-based reverse time imaging method for locating microseismic events. The wavefield reverse-time migration-based location methods are conceptually simple and easy to perform, but the critical imaging conditions for location can be easily affected by noises [11]. As the ultimate means of geophysical inversion, waveform inversion-based location methods takes advantage of the fitting degree between synthetic waveforms and observed waveforms, which fully considers waveform complexity and can comprehensively constrain the source location.…”

Section: Introductionmentioning

confidence: 99%

High-Accuracy Location of Microseismic Events in a Strong Inhomogeneous Mining Environment by Optimized Global Full Waveform Inversion

et al. 2020

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High-accuracy determination of a microseismic (MS) location is the core task in MS monitoring. In this study, a 3D multi-scale grid Green’s function database, depending on recording wavefield frequency band for the target mining area, is pre-generated based on the reciprocity theorem and 3D spectral element method (SEM). Then, a multi-scale global grid search strategy is performed based on this pre-stored Green’s function database, which can be effectively and hierarchically processed by searching for the spatial location. Numerical wavefield modeling by SEM effectively overcomes difficulties in traditional and simplified ray tracing modeling, such as difficult wavefield amplitude and multi-path modeling in 3D focusing and defusing velocity regions. In addition, as a key step for broadband waveform simulation, the source-time function estimated from a new data-driven singular value decomposition averaged fractional derivative based wavelet function (DD-SVD-FD wavelet) was proposed to generate high-precision synthetic waveforms for better fitting observed broadband waveform than those by simple and traditional source-time function. Combining these sophisticated processing procedures, a new robust grid search and waveform inversion-based location (GSWI location) approach is integrated. In the synthetic test, we discuss and demonstrate the importance of 3D velocity model accuracy to waveform inversion-based location results for a practical MS monitoring configuration. Furthermore, the average location error of the 3D GSWI location for eight real blasting events is only 15.0 m, which is smaller than error from 3D ray tracing-based location (26.2 m) under the same velocity model. These synthetic and field application investigations prove the crucial role of 3D velocity model, finite-frequency travel-time sensitivity kernel characteristics and accurate numerical 3D broadband wavefield modeling for successful MS location in a strong heterogeneous velocity model that are induced by the presence of ore body, host rocks, complex tunnels, and large excavations.

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“…In contrast to Geiger‐type location and detection methods that rely on single‐station phase picking, array‐based detection and location techniques instead exploit the multistation coherence of seismic waveforms or their CFs (for a review, see Li et al, 2020). Such methods are often applicable to a greater variety of seismic source types beyond typical earthquakes.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Location of Seismic Events From Time‐Delay Projection Mapping and Neural Network Classification

Mosher

Audet

2020

JGR Solid Earth

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The past several decades have seen an exponential increase in the volume of available seismic data, and with it has come the need to develop fast, automatic earthquake detection, and location algorithms. Some of the most recent and promising tools come from the field of machine learning. In this study, we combine a recent seismic detection and location method with neural network classification and analyze 4 months of continuous data recorded by a network of 76 stations in northern California. While these approaches have been used separately, our implementation is unique in that it is not constrained by source templates and avoids user-defined detection thresholds. In particular, we partition our data set into 234,240, 3-min long time windows with 75% overlap. For each time window, we create a 3D image that captures information about the coherence of the seismic wavefield. We then devise four features as input and train a neural network classifier to predict which time windows in the data set are likely to contain regional seismic events. These features include the second and fourth Hu image moments computed from 2D cross sections of our 3D images and statistical p values that quantify the probability of observing network-wide power-spectral density values at 0.2 and 0.5 s. Our neural network model predicts that 2,522 time windows contain seismic events, from which we locate 1,192 unique events. Plain Language Summary Whereas seismic data sets were relatively sparse in the past, modern data sets may be as large as several hundred terabytes. Given that researchers are often interested in identifying and studying seismic events (i.e., earthquakes) or their effects, and that these events are hidden within large and noisy data sets, they require tools to automatically and quickly search through large volumes of data to find what they are interested in. In fact, this kind of problem is perfectly suited to machine learning tasks, and this is one reason why machine learning has been so widely adopted in seismology. In this study, we take a reasonably large data set (4 months of data recorded by 76 seismic stations) and use a machine learning algorithm known as a neural network to predict time periods in the data set that are likely to contain earthquakes. Using this approach, we identify 1,192 earthquakes as well as provide location and origin time estimates for them.

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Recent Advances and Challenges of Waveform‐Based Seismic Location Methods at Multiple Scales

Cited by 128 publications

References 287 publications

Coherent diffraction imaging for enhanced fault and fracture network characterization

Coherent diffraction imaging for enhanced fault and fracture network characterization

High-Accuracy Location of Microseismic Events in a Strong Inhomogeneous Mining Environment by Optimized Global Full Waveform Inversion

Automatic Detection and Location of Seismic Events From Time‐Delay Projection Mapping and Neural Network Classification

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