Seismic Waveform Research Articles

Waveform Imaging Based on Linear Forward Representations for Scalar Wave Seismic Data

The current reverse-time migration, which is based on wave equations for imaging wavefields, employs an imaging formula derived from Claerbout’s imaging principle. This imaging formula is only valid for plane waves with small incident angles on the perfectly flat reflecting surface. However, the complexity of seismic wave propagation may lead to situations that do not meet this requirement. Therefore, this paper divides the subsurface into local scattering and reflecting bodies. It proposes linear forward representations for scattering and reflection data based on perturbations in the physical parameters and wave impedance, respectively. To further describe the effect on the reflecting body boundary, the local reflection coefficient is defined and the linear forward representation for the reflection data based on it is obtained. After that, the proposed linear forward representations are used as the forward equations for the linear inverse of the seismic data, and the seismic data waveform imaging method is developed based on linear inversion theory. At the same time, the specific waveform imaging calculation formulas for scalar wave scattering data and scalar wave reflection data are provided and validated via numerical experiments. Compared with the current reverse-time migration, waveform migration not only has the correct phase and higher resolution in theory but also does not increase the computational complexity. To some extent, it improves the deficiencies of the current structural imaging and provides a basis for subsurface lithological imaging.

Fine characterization of interbedding sand-mudstone based on waveform indication inversion

Abstract Owing to the increasing challenges associated with coal mine exploration and development, extremely precise surveys with high-resolution images are required to support production. Conventional inversion methods cannot provide sufficiently precise images of the complex lithologies in coal measure strata. Accordingly, this study performed research in Qiyuan mining area, Shanxi Province, China, and predicted the complex lithology on the basis of facies control using waveform indication inversion and waveform indication simulation. Horizontal changes in seismic waveforms were used to reflect lithologic assemblage characteristics for facies-controlled constraints, and the vertical mapping the connection between seismic waveform and logging curves was shown. Moreover, high-resolution inversions of wave impedance and natural gamma parameters were conducted. Combined with lithologic shielding and accurate time–depth conversion, the inversions enabled the precise characterization of the lithological assemblage distribution in the study area. Our results showed that waveform indication inversion could distinguish between coal seams and limestone, whereas waveform indication simulation based on natural gamma could effectively distinguish between sandstone and mudstone. Furthermore, the horizontal resolution was improved and the vertical resolution extended to a thickness of 2–3 m. In addition, the inversion results were highly consistent with drilling results, with an error <0.1 m. Therefore, waveform indication inversion and simulation could be applied to coal mines for safe and efficient production.

Deciphering the low-frequency seismic signals in the Weiyuan Shale gas field: implications for reservoir and structural heterogeneity

SUMMARY Hydraulic fracturing (HF) often stimulates the local earthquake productivity which provides a unique opportunity to characterize the crustal heterogeneities, reservoir properties and fluid injection effects. However, the velocity models acquired solely based on the arrival time records are often undermined due to the seismic network coverage and interpolation techniques. Instead, we adopt the waveform-based approach to apprehend; (1) structural heterogeneities, (2) reservoir distribution and (3) signatures of the injected fluid in the Weiyuan shale gas field. We categorize the waveforms into dominant high and low frequencies based on the qualitative inspection and frequency index analysis of the seismic waveforms. We first inspect the waveform to access the potential controlling mechanisms (source, site and path effects) at both single and multiple stations in different azimuthal orientations. As a result, we find the path effect as a dominant factor to influence the waveform characteristics, for example S-wave amplitude, and frequency. Subsequently, to localize the path effect, we conduct an in-depth examination of events within 10 km of each seismic station and classify the waveform records using their frequency indices. Notably, certain stations record a significant proportion of low-frequency waveforms (LFWs, up to 20 per cent), while others have limited occurrences (∼1 per cent) indicating suspected anomalous zones. Afterward, we identify two suspected anomalous zones based on LFWs intensity and ray tracing map. Both zones are in close proximity to fault zones and preserved reservoirs with no HF activities, where fault damage zones or the fluid-rich reservoir may contribute to our observed LFWs.

Quantitative identification and prediction of mixed lithology, Bohai Sea, China

The Paleogene Shahejie Formation in the KL16 oilfield, Bohai bay, is characterized by a thinly interbedded mixed sedimentary system, with complex sedimentary facies, lithologic types and distributions. It is hard for conventional logging methods to identify the lithology therein. In order to solve the difficulty in lithologic identification of mixed sedimentary system, analyses based on graph data base using elemental capture energy spectrum log have been proposed. Due to the different composition for the various minerals, we innovatively established the molar numbers of silicon, calcium, magnesium, and aluminum as characteristic parameters for sandstone, limestone, dolomite, and mudstone, and a graph clustering analysis method was applied to identify lithology. Considering the seismic waveforms corresponding to lithologic impedance of reservoir, three seismic phases were identified by neural network clustering analysis of seismic waveform, and the seismic attributes with high sensitivity to reservoir thickness were then selected to realize the fine description of the mixed carbonate-siliciclastic reservoir. Drilling results confirmed that the sedimentary facies were accurately identified, with reservoir prediction accuracy reaching up to 80%. Under the guidance of reservoir research, the oil-in-place discovered in the oilfield were estimated to be more than 5 million tonnes. This technology provides reference for the exploration and development of oilfields of mixed sedimentary system.

Investigation of Site Response in Xinjiang, Northwestern China, from S–Lg Fourier Amplitude Spectra

ABSTRACT Abundant seismic waveforms have been collected in Xinjiang since 2007, following the deployment of permanent stations by the China National Seismic Network and the Xinjiang Seismic Network. We investigated regional attenuation of the S–Lg phase in the crustal waveguide and site response in Xinjiang using broadband recordings at 44 stations from 118 earthquakes with magnitudes of 4.4–7.0 occurring in Xinjiang and adjacent areas between January 2009 and February 2022. We used linear regression analysis of the Fourier acceleration amplitude spectra in 18 frequency bands from 0.1 to 24.86 Hz to estimate site response terms and distance-dependent anelastic attenuation quality factor Q in the crustal waveguide at regional distances. The regression site terms of stations located in the Tarim and Junggar Basins show significant amplification at low frequencies and strong attenuation at higher frequencies due to the presence of thick sedimentary deposits. We quantified the linear behavior of the logarithms of the high-frequency site terms versus frequency using the zero distance attenuation parameter κ0 (Anderson and Hough, 1984). A positive correlation was observed between κ0 and the thickness of the sedimentary layers in Xinjiang. We observed some consistent patterns in the behavior of the site response terms as a function of sediment thickness over the frequency range of 0.1–24.86 Hz. Linear functions of sediment thickness were used to model the site response in Xinjiang at each of the 18 frequencies. An alternative approach that adopted the κ0 model as a function of sediment thickness can be used to estimate site responses at high frequencies (≥5.66 Hz). The results of this study demonstrate the nature of site effects on earthquake ground motions caused by the thick sedimentary deposits in Xinjiang and provide a preliminary site response model as a function of sediment thickness for this region.

Determination of earthquake focal mechanism via multi-task learning

A multi-task learning-based focal mechanism network (MTFMN) is proposed for calculating parameters of the focal mechanism of earthquakes by regression with incorporating expert prior knowledge. The model automatically learns feature representations of seismic waveforms and transforms the inversion task of the focal mechanism into multi-task learning. Experimental results suggest that MTFMN outperforms traditional methods in the task of earthquake focal mechanism and improves the accuracy of parameter estimation of focal mechanism. In addition, comparative experiments demonstrate MTFMN’s enhanced robustness and generalization capabilities compared to other methods. Our proposed methodology presents a more precise regression approach for focal mechanism inversion, with the potential to provide a better understanding of seismic events.

Seismic Full Waveform Inversion With Shot-Encoding Using an Improved L-BFGS Method

Seismic Full Waveform Inversion With Shot-Encoding Using an Improved L-BFGS Method

Deep Generative Model Conditioned by Phase Picks for Synthesizing Labeled Seismic Waveforms With Limited Data

Deep Generative Model Conditioned by Phase Picks for Synthesizing Labeled Seismic Waveforms With Limited Data

Enhancing Seismic Waveform Inversion Using a Three-Step Strategy With Adversarial Neural Networks and Seismic Envelope

Enhancing Seismic Waveform Inversion Using a Three-Step Strategy With Adversarial Neural Networks and Seismic Envelope

Potential of Earthquake Strong Motion Observation Utilizing a Linear Estimation Method for Phase Cycle Skipping in Distributed Acoustic Sensing

AbstractDistributed acoustic sensing (DAS) using existing optical fiber cables facilitates high‐density seismic observation. However, few studies have examined the reliability of the seismic waveform amplitude recorded by DAS. In this study, a DAS network was connected to optical fiber cables installed over a distance of 75 km along a high‐speed train (Shinkansen) railway in the Kumamoto prefecture, Japan. We successfully observed strong motions of the Mj6.6 earthquake (approximately 150 km from the fiber) on 22 January 2022, in Hyuga‐nada, in addition to several small local earthquakes. The observed strong motions from the Mj6.6 earthquake, using DAS, exhibited cycle skipping (clipping) issues due to dynamic range limitations at numerous channels. To address this, we estimated the shaking map, representing maximum strain distributions for Mj6.6, by replacing the clipped data with information from nearby unclipped channels and scaling their RMS amplitudes based on S‐coda (unclipped). Furthermore, we verified the reliability of the amplitude information obtained from DAS by estimating the distance attenuation of seismic waves while correcting for the differences in the structure type and coupling as much as possible. The distance attenuation property of local earthquakes was consistent with that of the peak ground velocities obtained from seismometers, indicating that DAS data acquired using fibers installed on infrastructure (various structures) can also be utilized to assess the spatial distribution of the relative amplitude values along the fiber. Obtaining high‐density seismic motion distributions is important for earthquake early warning and accurate damage estimation of strong motions.

Inverzija momenta tenzora uklapanjem površinskih talasa za zemljotres M 5.5 u Kraljevu

This paper focuses on the inversion of seismic data from the Kraljevo earthquake to determine the source parameters and provide a comparison with classical methods. Understanding the earthquake source is critical for seismic hazard assessment and contributes to refining existing models. The inversion was performed using the Grond software, using seismic waveform data from the seismic stations located in the range of 900 km from the epicenter to resolve the moment tensor and other key source parameters: depth, magnitude, etc. The results for the moment tensor solution were compared with the one obtained from traditional (P-wave first-motion polarity) method as well as the source parameters were compared with findings from prior studies, offering insights into the robustness of the inversion approach. This comparison helps assess the reliability of different techniques in earthquake source analysis, enhancing our understanding of seismic events and their broader implications for regional tectonics.

Evolution of shallow volcanic seismicity in the hydrothermal system of La Soufrière de Guadeloupe following the April 2018 Mlv 4.1 earthquake

La Soufrière de Guadeloupe volcano is characterized by seismo-volcanic activity dominantly linked to an active hydrothermal system. This microseismicity is shallow, mainly triggered in swarms and characterized by repeating earthquakes. Four recurrent families of Volcano-Tectonic (VT) repeaters have been identified and the main repeater accounts for 80% of detections. Stacking repeating seismic waveforms produces MASTER events with a high Signal-to-Noise Ratio (SNR) and thanks to the deployment of a temporary seismic nodal array, their absolute locations can be better constrained. Because we observe systematic positive residuals of P-wave arrival times at almost all stations, we investigate potential bias in the velocity model in the shallow part of the dome. By increasing the P-wave velocity and decreasing the VP/VS ratio from 1.8 to 1.69, we reduce these residuals and improve the local velocity model. We use this new velocity model to locate each VT event relatively to is own MASTER hypocenter. This procedure offers a new image of the hydrothermal seismic activity that we locate under the acid lake of the summit Tarissan crater, along a sub-vertical conduit. We also define a linear relationship between the logarithm of the peak amplitude of seismic events and their duration to obtain a pseudo local magnitude. The approach enables to accurately and automatically estimate the magnitude during the event detection. We show that after the occurrence in April 2018 of the largest earthquake (Mlv 4.1) since the last phreatic eruption in 1976–1977, the swarm frequency and amplitude increased significantly, and a new family of VT repeater emerges, accounting for up to 14% of detections. We show a quiescence in the main repeaters activity in the month following the April 2018 earthquake, suggesting a significant stress release within the volcanic system, which is likely related to dynamic stress changes rather than static stress variations caused by the earthquake. Moreover, we detect the emergence of a new family of repeaters 3 months after the event, located ∼100 m above the main repeater family, that could be related to a fluid pressure increase induced by the regional hydrological cycle, superimposed to the internal forcing driven by the hydrothermal system. Finally, using a statistical approach, we highlight seasonal periodicities in the number of events and the released seismic moment at La Soufrière de Guadeloupe, with a dominant peak of seismic activity in October–November and a second, less significant, peak of in April.

Self-supervised learning waveform inversion for seismic forward prospecting in tunnels: A case study in Pearl River Delta Water Resources Allocation Project in China

Tunnel and underground engineering construction often encounter unfavorable geology, leading to disasters such as water and mud inrushes and landslides. To prevent geologic hazards, it is important to look ahead and predict the location and distribution of adverse geology ahead of the tunnel face. This process is known as seismic forward prospecting in tunnels, and it typically requires an accurate calculation of velocity. Seismic waveform inversion methods based on deep learning have demonstrated potential in estimating velocity from synthetic seismic data. However, the superiority of these methods over traditional ones on field data is still an area of active research. Here, the Pearl River Delta Water Resources Allocation Project in China is used as an example to develop a self-supervised learning waveform inversion method for building a reliable velocity distribution in front of the tunnel. By introducing the background velocity as large-scale information and implementing multiscale loss functions, the previous self-supervised learning inversion method on synthetic data is improved. In addition, the corresponding network-based workflow for field data is developed. To demonstrate the effectiveness of our method, a comparison is conducted with practical tunneling exposure, for which the low-velocity zone corresponds with the fault-fractured zones and the water-flowing zones. This indicates that the results obtained from our method can be used as geologic guidance for safe tunneling practices. In the end, the applicability and disadvantages of our deep-learning inversion method for seismic forward prospecting in tunnels are discussed.

Towards fast focal mechanism inversion of shallow crustal earthquakes in the Chinese mainland

We have developed an automatic regional focal mechanism inversion system based on the Earthquake Rapid Report (ERR) system and the real-time three-component seismic waveform stream of 1 000 broadband seismic stations provided by the China Earthquake Networks Center (CENC). The system can rapidly provide a double couple solution and centroid depth within 5–15 ​min after receiving earthquake information from the ERR system. The data processing is triggered by earthquake information obtained from the ERR system. The system is capable of determining the focal mechanism of all shallow-depth earthquakes in the Chinese mainland with a magnitude of 5.5–6.5. It utilizes waveform data recorded by seismic stations located within 500 ​km from the epicenter, enabling the reporting of a focal mechanism solution within 5–15 ​min of an earthquake occurrence. Additionally, the system can assign a corresponding grade (A B C) to the focal mechanism solution. We processed a total of 301 earthquakes that occurred from 2021 to June 2022, and after the quality control, 166 of them were selected. These selected solutions were manually checked, and 160 of them were compiled in a focal mechanism catalog. This catalog can be conveniently downloaded online via the Internet. The automatic focal mechanism solution of earthquakes in eastern China exhibits a good agreement with that provided by the Global Centroid Moment Tensor (GCMT), when available. The average Kagan angle between this catalog and GCMT is 22°, and the average difference in MW is 0.17. Furthermore, compared with GCMT, the minimum magnitude of our catalog has been reduced from approximately 5.0 to 4.0. The correlation between the centroid depth and crustal thickness in the Chinese mainland confirms the distribution of the centroid depth.

Ground-Motion Variability for Ruptures on Rough Faults

ABSTRACT Fault roughness influences earthquake rupture dynamics, seismic energy radiation, and, hence, resulting ground motion and its variability. Using 3D dynamic rupture simulations considering a range of rough-fault realizations, we investigate the effects of rupture complexity caused by fault roughness on ground-motion variability, that is, the variability of peak ground acceleration (PGA) and velocity (PGV) as a function of distance. In our analysis, we vary hypocenter locations (leading to unilateral and bilateral ruptures) and fault roughness amplitude to generate a set of magnitude M ≈ 7 strike-slip dynamic rupture simulations. Synthetic seismic waveforms computed on a dense set of surface sites (maximum resolved frequency 5.75 Hz) form our database for detailed statistical analyses. For unilateral ruptures, our simulations reveal that ground-shaking variability (in terms of PGA and PGV) remains nearly constant with increasing distance from the fault. In contrast, bilateral ruptures lead to slowly decreasing ground-motion variability with increasing distance in the near field (less than 20 km). The variability becomes almost constant at large fault distances. We also find that low-amplitude fault roughness leads to ruptures that are likely to generate higher PGA variability than events on faults with high-amplitude roughness. Increasing fault roughness distorts the radiation pattern, thereby reducing directivity effects and, hence, potentially lowering ground-motion variability. The average PGV variability from our rough-fault rupture models is consistent with estimates from empirical ground-motion models (GMMs). However, the average PGA variability exceeds the variability encoded in empirical GMMs by nearly 20%. Hence, our findings have implications for near-source ground-motion prediction in seismic hazard studies, because ground-motion variability depends on details of the earthquake rupture process and is larger than GMM estimates.

GST-based polarization approach to picking P-and S-waves arrival time for high-speed rail early warning system

The first arrival time of seismic P- and S- waves affects the determination of the beginning of an earthquake by the Earthquake Early Warning System for High-speed Rail (HSREEWs). Accordingly, we propose an approach to picking P- and S-waves arrival time for HSREEWs. This approach can treat the continuous seismic signals in real-time without prior knowledge of whether an earthquake occurs. Firstly, we replace the fixed time window of the S transform (ST) with the dynamic window of the generalized S transform (GST) to make the necessary time of signal processing more flexible. Meanwhile, the GST extends the sequential signal to the time-frequency domain (TF), and combines the TF polarization to construct four three-component polarization filters. Accordingly, the P-wave and S-wave can be separated from the seismic waveform by those filters, which effectively avoids the phenomenon that the S-wave phase is difficult to determine because of the interference of S-wave overlapping in the coda region. Secondly, based on the instantaneous frequency of seismic signal, we construct a new STA/LTA (GSPA-STA/LTA) with the characteristic function (CF) of constrained TF spectrum to pick the first arrivals of the P- and S-waves, and provide the corresponding automatic picking algorithm. Finally, we analyze 504 seismic events larger than Ms40 near the Japanese Tohoku Shinkansen to comprehensively verify the effectiveness of our approach. Since the P-wave and S-wave are first separated, the subsequent process just has to identify the first arrival times of their respective components. The approach has a good practical value in maintaining the safe operation of high-speed rail under earthquake, which meets the industry demands for speed, scale and accuracy in HSREEWs.

Sparse time-frequency analysis of seismic data via convolutional neural network

Time-frequency (TF) analysis is commonly used to reveal the local properties of seismic signals, such as frequency and spectral contents varying with time/depth. Aiming to realize a highly localized TF representation of seismic signals, researchers treated the TF analysis as an inverse problem, and the regularization is adopted in the objective functions. Traditionally, the TF sparse inversion process is solved by the Lasso regression. It has been proven that the Lasso regression needs a large number of iterations to reach a high accurate solution for the convex problem. Recently, convolutional neural network (CNN) has been successfully used to solve the convex problem due to their high computational efficiency and strong nonlinear characterization ability. We use CNN to solve the sparse TF inversion, and our method is called STFA-CNN. The objective function in the neural network architecture consists of two portions. The first one is to minimize the difference between the local forward and backward Fourier transform of seismic signals. The second is minimizing the regularization ([Formula: see text] norm) of TF results. To demonstrate the effectiveness of our method, we apply it to synthetic and real seismic data. We further use the calculated TF spectra to compute the attenuation of seismic waveforms and apply the attenuation attribute to predict the hydrocarbons of a seismic survey acquired over the Ordos Basin, northwest of China.

Noise masking of near-surface scattering (heterogeneities) on subsurface seismic reflectivity

Abstract Near-surface velocity variations are the main cause of seismic scattering in exploration seismology. Many studies create the near-surface heterogeneity as velocity models that have random velocity distribution, random objects, or irregular subsurface topography to study and mitigate the resultant scattering effects of the near-surface layer. Von Kármán (self-similar) method is a known method in the literatures for modeling heterogeneous earth in a statistical way. This research modifies the self-similar method, and throughout the work, it has proven that the self-similar provides a robust method for generating realistic near-surface velocity models with different spatial velocity distributions. This study creates four-velocity models with simple subsurface layering and structure, three of which include a near-surface layer in three different degrees of velocity heterogeneity. Synthetic acoustic seismic reflections are produced for the four-velocity models to investigate the resultant scattering effects of the near-surface velocity heterogeneity on the quality of seismic waveform coherency. Spectacular negative observations are witnessed of the near-surface layer involvement to the quality of seismic reflection coherency that increases as velocity dramatically varies. Subtracting the scattering noise, which is modeled using an exact heterogeneous model, enhances seismic reflection coherency for the subsurface layers, but waveforms that are affected by scattering must be reconstructed for true amplitude and seismic waveform analysis.

Co-seismic surface displacement of the June 21, 2022 MW6 Khōst, Afghanistan earthquake from InSAR observations

A robust estimation of the earthquake location, seismic moment, and fault geometry is essential for objective seismic hazard assessment. Seismic events in a remote location, specifically in the absence of seismic and GNSS networks, can be investigated effectively using the InSAR-based technique. This study adopts the Differential Interferometric SAR (DInSAR) technique to quantify the co-seismic surface displacement caused by the June 21, 2022, Khōst MW6 earthquake that occurred along the western plate boundary between the Indian and Eurasian plate. The interferograms show that the maximum surface deformation occurred on the northwest and southwest of the fault line. From coherence, the Line of Sight (LOS) displacement, and the co-seismic surface displacement analysis, it has been observed that surface deformation was most pronounced in the southwest region of the fault line, and the surface has moved to the opposite direction along the fault line, which indicates a sinistral slightly oblique strike-slip movement. This InSAR-based observation appears consistent with the seismic waveforms derived from co-seismic surface displacements. Further, it has been argued that the slip deficit accumulated during the period of the last about 48 years along the frontal region of the northward extension of the Suleiman range and associated fault zone is qualitatively estimated at about 1.5 m, which is consistent with the seismic waveforms derived finite slip model.

Multipath Transfer-Function Correction Method to Predict Site-Specific Amplification at City Scale

Abstract The site-specific amplification of seismic waves is an essential component of local seismic hazard assessment. It can be evaluated from empirical data, but measurements are feasible just in a limited number of locations. Hence, at the city scale, there is a need for the theoretical prediction and interpolation of the amplification. In this article, we introduce a physics-based method to predict the site-specific amplification and duration in a broad frequency range. The method is based on a novel energy-based concept of the multipath propagation of waves in viscoelastic media with random heterogeneities. The amplification is expressed by the surface-outcrop transfer function of the multipath wave propagation, which is defined by expected values of the energy spectral ratio. The method is applied to the near-surface 2D velocity model in the city of Zürich in Switzerland. The predicted amplification is validated by empirical data at a nearby seismic station, and it is compared with the soil class and other site-condition proxies. Finally, the method performance is demonstrated by the prediction of site-specific seismic waveforms and response spectra for the 2022 ML 4.7 Mulhouse earthquake.