Basis Of Seismic Data Research Articles

Enhancing analyst decisions for seismic source discrimination with an optimized learning model

Sustainable development in urban areas requires a wide variety of current and theme-based information for efficient management and planning. In addition, researching the spatial distribution of earthquake (EQ) clusters is an important step in reducing seismic risks and EQ losses through better assessment of seismic hazards, therefore it is desirable to acquire an uncontaminated database of seismic activity. Quarry blasts (QBs) conducted over the mapped area have tainted the seismicity inventory in the northwestern region of Egypt, which is the focus of this paper. Separating these QBs from the EQs is hence preferable for accurate seismicity and risk assessments. Consequently, we present a highly effective ML model for cleaning up the seismicity database, allowing for the accurate delineation of EQ clusters using data from a single seismic station, “AYT”, which is part of the Egyptian National Seismic Network. The magnitudes ≤3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\le 3$$\\end{document} that are very uncertain as EQs or QBs and need a significant amount of time to analyze are the primary focus of the model. In order to find the best way to classify EQs and QBs, the method looks at a number of ML models before settling on the best one using eight features. The results show that the suggested method, which uses the quadratic discrimination analysis model for discriminating, successfully separates EQs and QBs with a 99.4% success rate.

A global SS precursor method for imaging discontinuities: the Moho and beyond

SUMMARY Imaging seismic velocity discontinuities within the Earth's interior offers important insight into our understanding of the tectonic plate, associated mantle dynamics, and the evolution of the planet. However, imaging velocity discontinuities in locations where station coverage is sparse, is sometimes challenging. Here we demonstrate the effectiveness of a new imaging approach using deconvolved SS precursor phases. We demonstrate its effectiveness by applying it to synthetic seismograms. We also apply it to ∼1.6 M SS precursor waveforms from the global seismic database (1990–2018) for comparison with CRUST1.0. We migrate to depth and stack the data in circular 6° bins. The synthetic tests demonstrate that we can recover Moho depths as shallow as 20 km. Globally, the Moho is resolved at 21–67 km depth beneath continental regions. The Moho increases in depth from 21 km ± 4 km beneath the continental shelf to 45–50 km beneath the continental interiors and is as deep as 67 ± 4 km beneath Tibet. We resolve the Moho in 77 percent of all continental bins, within 10 km of CRUST1.0, with all outliers located in coastal regions. We also demonstrate the feasibility of using this method to image discontinuities associated with the mantle transition zone with both synthetic and real data. Overall, the approach shows broad promise for imaging mantle discontinuities.

Preparing A Seismic Database for Nineveh Governorate Using Modern Technologies

Preparing A Seismic Database for Nineveh Governorate Using Modern Technologies

Achieving a Comprehensive Microseismicity Catalog through a Deep-Learning-Based Workflow: Applications in the Central Ecuadorian Subduction Zone

ABSTRACT Although seismological networks have densified along the Ecuadorian active margin since 2010, visual phase reading, ensuring high arrival times quality, is more and more time-consuming and becomes impossible to handle for the very large amount of recorded seismic traces, even when preprocessed with a detector. In this article, we calibrate a deep-learning-based automatized workflow to acquire accurate phase arrival times and build a reliable microseismicity catalog in the central Ecuadorian forearc. We reprocessed the dataset acquired through the OSISEC local onshore–offshore seismic network that was already used by Segovia et al. (2018) to produce a reference seismic database. We assess the precision of phase pickers EQTransformer and PhaseNet with respect to manual arrivals and evaluate the accuracy of hypocentral solutions located with NonLinLoc. Both the phase pickers read arrival times with a mean error for P waves lower than 0.05 s. They produce 2.7 additional S-labeled picks per event compared to the bulletins of references. Both detect a significant number of waves not related to seismicity. We select the PhaseNet workflow because of its ability to retrieve a higher number of reference picks with greater accuracy. The derived hypocentral solutions are also closer to the manual locations. We develop a procedure to automatically determine thresholds for location attributes to cull a reliable microseismicity catalog. We show that poorly controlled detection combined with effective cleaning of the catalog is a better strategy than highly controlled detection to produce comprehensive microseismicity catalogs. Application of this technique to two seismic networks in Ecuador produces a noise-free image of seismicity and retrieves up to twice as many microearthquakes than reference studies.

Hyper‐reduced order models for accelerating parametric analyses on reinforced concrete structures subjected to earthquakes

AbstractThis paper combines a hyper‐reduction procedure, the proper orthogonal decomposition unassembled discrete empirical interpolation method, with a non‐iterative α‐operator splitting (α‐OS) time‐integration scheme for accelerating parametric analyses on damageable civil engineering structures subjected to earthquakes. Applications on a two‐story reinforced concrete frame building modeled by multi‐fiber beams provide guidelines to define hyper‐reduced order models (HROMs) for real‐life applications that use seismic databases or variable loading assumptions. The α‐OS HROMs proved their applicability in approximating high‐fidelity dynamic responses with speed‐up factors higher than 60 and errors lower than 0.6% for the present case study.

Prediction model of lateral spreading of liquefied soil during earthquakes based on neural network

In response to the complex issue of predicting lateral spreading of liquefied soil during earthquakes, this study establishes a comprehensive seismic database. The Newmark sliding block method is utilized to calculate lateral displacements, and a sensitivity analysis model for liquefaction-induced lateral spreading is developed by considering multiple parameters. Based on the three most influential parameters identified through sensitivity analysis, a novel liquefaction-induced lateral spreading prediction model is proposed. The reliability of this new model is then demonstrated by comparing it with existing classical prediction models, as well as by analyzing correlation coefficients and root mean square errors.

A New Type of Bipartite Random Graph as a Transform of Seismogram and Its Potential for Organizing Seismic Databases

This paper proposes a method to reduce seismogram variability as a determining factor in its interpretation, processing, and clustering. By introducing the concept of single fluctuations (SFs), the seismogram can be parsed into a sequence of random impulses with subsequent ordering. This rearrangement of SFs, if they are assigned by positive integers, represents the formal mapping of a regular string of integers into a random one, which can be represented with a bipartite random graph (bigraph). Due to its specific randomness, such a bigraph is considered a new type of random balanced bigraph. The R-envelope and RQ-envelope, its equidistant version, are defined by tracing the peak envelope over ordered SFs. The equivalence and complementariness of the RQ and bigraph are considered and discussed, forming a combined characteristic of the seismogram. The R/RQ provided a considerable reduction in seismogram variability, which was confirmed by creating and analyzing an ensemble of RQ from several seismograms. In the RQ domain, distance is defined as a possible basis for metrics and clustering, but the ensemble variability was quite narrow and not as suitable for this purpose. Otherwise, the ensemble shows high redundancy hidden in the seismogram population. As for the bigraph, the mesh of its edges is structuralized in bundles, forming a skeleton, which reflects the internal structural content of the seismogram. The distance over the domain of bigraphs is proposed to show the possibility of clustering. This means that only a combined RQ and bigraph provides a suitable frame for seismogram representation with reduced variability and, thus, the potential for more effectively organizing seismic databases and a deeper interpretation of seismograms; therefore, RQs and bigraphs can be considered as a transform of a seismogram. Many aspects of these concepts are thoroughly discussed. The similarity between concepts of SF and wavelets is briefly considered. This very complex theme is new and promises broad further research. All issues considered in the paper are abundantly illustrated.

Reconstrucción de fuentes sísmicas usando un enfoque de Redes Neuronales Profundas de Datos Previos

Seismic surveys are often affected by environmental obstacles or restrictions that prevent regular sampling in seismic acquisition. To address missing data, various methods, including deep learning techniques, have been developed to extract features from complex information, albeit with the limitation of requiring external seismic databases. While previous works have primarily focused on trace reconstruction, missing shot-gathers directly impact the seismic processing flow and represent a major challenge in seismic data regularization. In this paper, we propose DIPsgr, a seismic shot-gather reconstruction method that uses only the incomplete seismic acquisition measurements to estimate their missing information employing unsupervised deep learning. Numerical experiments on three databases demonstrate that DIPsgr recovers the complete set of traces in each shot-gather, with preserved information and seismic events.

A new seismic damage assessment method for single-layer spherical reticulated shells based on structural residual displacement under sequential earthquakes

Single-layer Spherical Reticulated Shell (SSRS) is usually utilized as an emergency shelter or a command post for disaster relief. However, the structural damage indicators in current studies are difficult to observe or record so as to hardly assess the damage state after the main shock. In order to explore the damage state assessment timely and accurately for SSRS, a new seismic damage assessment method based on structural residual displacement under main shock-aftershock ground motions is proposed in this paper. The finite element model of Kiewit 8-SSRS (K8-SSRS) is established in the ANSYS with 40 groups of actual sequential earthquake from the Pacific Earthquake Engineering Research Center (PEER) seismic database. Then, the dynamic responses of the model are analyzed to calculate the damage index and eight structural dynamic response indicators. Moreover, the correlation study between the damage index and eight indicators is carried out to make the comparison analyses. Finally, it is verified that the structural plasticity appeared under main shock can develop further under strong aftershock to cause greatly additional damage or even result in structural strength failure. It is concluded that the maximum residual displacement has the strongest correlation with the damage index of the structure under sequential earthquake, which means the most feasible indicator to characterize the structural damage under sequential earthquake.

Optimization of the seismic resistance of school buildings using artificial intelligence and sensitivity analysis theories – A Taiwan case study

School buildings in Taiwan are designed not only as places of education, but also as temporary shelters in the aftermath of major earthquakes. Therefore, seismic resistance assessments of existing school buildings are very important. Another issue worth discussing is how to choose the appropriate number of school case studies and seismic factors when using artificial intelligence (AI) to infer the seismic resistance of school buildings. This is because when the number is too high, a large amount of time and money will be required to build the seismic database. If the number is too low, then the inference results will be unsatisfactory. This study applied different research methods to address these issues. First, sensitivity analysis was conducted to determine the optimal number of seismic factors and school case studies. Then, gray theory was utilized to explore the correlations between the seismic factors and seismic resistance of school buildings. Finally, a support vector machine (SVM) and gene expression programming (GEP) were used to determine the optimal assessment models. The SVM was also validated by 10-fold cross-validation and back-propagation network. The results show that when researchers used AI theories, at least five factors were considered. If researchers want to obtain a better inference result, then the number of factors can exceed 10. With regard to the number of cases, it should be twice the number of factors, but more than three times is ideal. For the seismic assessment models inferred by SVM and GEP, the root mean square error of the testing cases ranged from 0.0501 to 0.0541, and 0.0757 to 0.0981, respectively, which indicates good performance. The results can be adopted by structural engineers and architects. The research methods developed in this study can also be referenced by researchers in the future.

Finite element modelling of Indian plate

One of the most challenging problems that earthquake engineers must resolve is the prediction of seismic activity and the estimation of the accompanying dangers. Numerous seismologists have proposed empirical equations as solutions to these problems. Since the process of establishing empirical equations is unpredictable, engineers have to develop mechanistic models to quantify seismic activity. Due to its simplicity, the finite element method, one of several numerical approaches, is frequently utilized in a number of fields and applications. Despite the Himalaya being considered a plate boundary and an active zone in Indian context, the seismicity database demonstrates that other regions of the Indian shield are sometimes experiencing seismic activity. The finite element method-based mechanistic models of the Indian plate are expected to simulate stress fields, which will measure the seismic potential of India's active zones. By examining the simulated stress field for various boundary conditions, geological circumstances, and rheological conditions, this study develops a finite element model for the Indian plate. The literature claims that these circumstances have an impact on the magnitude and direction of strains in the plate. The simulated stress field demarcates the seismically active regions of India, according to the numerical analysis of the models.

Seismo-Lineaments in Egypt: Analysis and Implications for Active Tectonic Structures and Earthquake Magnitudes

Quiescent faults may be capable of creating catastrophic earthquakes in locations with moderate and/or low seismic activity, such as Egypt. This study combines structural, remote sensing (RS), geophysical, and seismic activity data to examine and analyze the relationship between tectonic structures and seismotectonic activity in Egypt. In a new seismo-lineaments map of Egypt, tectonic lineaments of the Egyptian mainland were delineated and classified. The database contains 8000 lineaments that were divided into distinct geographical zones using statistical analysis and general features. Delineated lineaments were integrated with digitized geological and geophysical surface and subsurface faults and geographic information systems (GIS) processing techniques were applied to produce 4249 faults. The spatial distribution of seismic activity was determined to extract 1968 competent faults out of 4249 capable faults (i.e., greater than 10 km and suitably orientated concerning the existing stress field). Maximum expected magnitudes (Mmax) were calculated for distinct seismogenic locations in Egypt, taking into account the nature of the regional rupture. At the national scale, empirical scaling relations between fault lengths and earthquake magnitude were employed for all mapped faults in Egypt. The findings concerning the faults were highly consistent with traditional geological information. The results suggest that our technique for estimating the highest predicted magnitudes produces similar values and might be used to evaluate Egypt’s possible future seismic hazard. The results were compared to seismic databases. The similarity of our results with those reported in the catalogs lends confidence to the proposed scheme.

Seismotectonics of Shallow-Focus Earthquakes in Venezuela with Links to Gravity Anomalies and Geologic Heterogeneity Mapped by a GMT Scripting Language

This paper presents a cartographic framework based on algorithms of GMT codes for mapping seismically active areas in Venezuela. The data included raster grids from GEBCO, EGM-2008, and vector geological layers from the USGS. The data were iteratively processed in the console of GMT, converted by GDAL, formatted, and mapped for geophysical data visualisation; the QGIS was applied for geological mapping. We analyzed 2000 samples of the earthquake events obtained from the IRIS seismic database with a 25-year time span (1997–2021) in order to map the seismicity. The approach to linking geological, topographic, and geophysical data using GMT scripts aimed to map correlations among the geophysical phenomena, tectonic processes, geological setting, seismicity, and earthquakes. The practical application of the GMT scripts consists in automated mapping for the visualization of geological risks and hazards in the mountainous region of the Venezuelan Andes. The proposed method integrates the approach of GMT scripts with state-of-the-art GIS techniques, which demonstrated its effectiveness as a tool for mapping spatial datasets and rapid data processing in an iterative regime. In this context, using GMT and GIS to find similarities between the regional earthquake distribution and the geological and topographic setting is essential for hazard risk assessment. This study can serve as a basis for predictive seismic analysis in geologically vulnerable regions of Venezuela. In addition to a technical demonstration of GMT algorithms, this study also contributes to geological and geophysical mapping and seismic hazard assessments in South America. We present the full scripts used for mapping in a GitHub repository.

Late Quaternary sedimentary record of estuarine incised-valley filling and interfluve flooding: The Manfredonia paleovalley system (southern Italy)

Multiple paleovalley systems of late Quaternary age have been widely explored in previous research from the Gulf of Manfredonia on the basis of seismic data, but only limited information is available on their proximal (onshore) segments. Through an integration of sedimentary, paleoecological (mollusks, benthic foraminifers, ostracods), and geochemical analyses from three 30-50 m-long onshore cores and a sequence-stratigraphic framework chronologically constrained by 25 radiocarbon data, we document the stratigraphic architecture of three contiguous paleovalley systems (Candelaro, Cervaro and Carapelle rivers) and their sedimentary response to Late Pleistocene valley excavation, Holocene filling and basinwide interfluve flooding. Above a prominent sequence boundary formed during the Late Pleistocene relative sea-level fall, fluvial-channel gravels and sands (lowstand systems tract) are overlain by a deepening-upward, valley-fill succession of inner-estuarine (freshwater) to outer-estuarine (brackish) muds (lower transgressive systems tract – TST). The individual valley fills are overlain by laterally extensive bay deposits (upper TST) and by a progradational succession of prodelta/delta front and offshore/shoreface deposits that reflect normal regression under highstand conditions (HST). The transition from fluvial to inner-estuary (fluvial-dominated) and outer-estuary (wave-influenced) deposits records the progressive shift in sediment provenance from Southern Apennine source rocks to a mixed composition that reflects increasing alongshore sediment contribution from northern sources via the SE-directed Western Adriatic Current. The stratigraphic surface that demarcates the transition from estuarine to bay depositional systems (lower/upper TST boundary) represents the physical prolongation of interfluvial terrace surfaces, as valley margins were flooded during the Early Holocene transgression. This surface, typically recognizable on seismic profiles at the boundary between low-amplitude and overlying high-amplitude reflectors, has no obvious lithologic signature in core. However, it can be readily identified on the basis of paleoecologic features (sharp increase in species diversity and in the proportion of marine taxa) that reveal the abrupt transition from laterally confined to unconfined settings.

Seismic design of geosynthetic-reinforced earth retaining walls following a pseudo-static approach

Geosynthetic-reinforced earth (GRE) retaining walls show a better performance than conventionally-designed walls during destructive earthquakes, due to their capability of redistributing seismic-induced deformations within the reinforced zone. In this paper, a recently-proposed method to design GRE walls is first recalled, where the wall is designed to trigger an internal plastic mechanism in the presence of strong earthquakes. Following a pseudo-static approach, the seismic coefficient k is therefore assumed equal to the internal seismic resistance of the wall k c int. The seismic coefficient is then calibrated against given seismic wall performance, expressed in terms of limit values of earthquake-induced displacements. Permanent displacements are evaluated through empirical relationships that were previously developed on the basis of a parametric integration of an updated Italian seismic database. Effectiveness of the proposed procedure is then demonstrated by assessing, through Finite Difference nonlinear dynamic analyses, the seismic performance of two walls, namely a GRE and a conventional gravity wall, characterised by the same seismic resistance but triggering an internal and external plastic mechanism, respectively. They are both subjected to a real strong motion, capable of activating a plastic mechanism. Results showed that lower permanent displacements are accumulated in the GRE wall where internal mechanisms are triggered.

Development and Application of a Platform for Multidimensional Urban Earthquake Impact Simulation

Earthquakes can be devastating to cities. Therefore, accurate and efficient simulation of the potential seismic damage to buildings has become an indispensable part of worldwide efforts to mitigate earthquake hazards. Precise simulation results can highlight potential seismic damage and serve as a reference in urban development and for planning post-earthquake rescue operations. In urban areas, simulation of the structural dynamic response during an earthquake is key for planning an appropriate emergency response. Multidimensional visualization of the effects of earthquakes is a popular technique in current earthquake simulation research. In the present study, a dynamic multidimensional simulation method was developed for analyzing the impact of an earthquake on buildings. This method involved conducting three-dimensional (3D) dynamic analysis of data from a seismic capacity database. Polygonal models and seismic capacity data were used in a high-performance computing process to calculate the seismic response of each building in an urban environment. On the basis of the calculations, a platform was developed for multidimensional urban earthquake impact simulation (MDUES). The developed MDUES platform enables analysis of the impact of long-period earthquakes, simulation of the 3D seismic responses of buildings, and visualization of the disaster risk and earthquake impact in a specified area. In summary, simulation of structural seismic responses in an urban setting necessitates a careful examination of the characteristics of ground and building motion, and the results of this simulation can provide a crucial reference for city planning, post-earthquake rescue operations, and seismic damage assessments.

Practical seismic fragility estimation of Japanese railway embankments using three seismic intensity measures

This study proposes a practical fragility estimation equation for Japanese standard models of railway embankments using the peak ground acceleration, peak ground velocity, and Arias intensity. The analytical models were implemented as unreinforced and geosynthetic-reinforced embankment models. A sensitivity analysis of the seismic fragility estimation of the embankment models was conducted on various embankment heights, average values of friction angles in the backfill soil, and tensile strengths of the primary reinforcement. Consequently, a unique formula for the fragility function in the presence of different seismic intensities was successfully presented. The parameters of the fragility function were successfully estimated using commonly used design parameters, such as the embankment height, average value of the friction angle of the backfill soil, and average value of the tensile strength. Additionally, another sensitivity analysis using different seismic databases was conducted to explore the effect of the seismic database on the fragility curve estimation of railway embankments. As a result, using different seismic databases, different fragility curves were obtained. These results highlight the importance of checking the sensitivity of the seismic database when developing the fragility curve.

Visual duplicates search of fracture zones of seismic databasesbased on the method of solving the ICP variational problemin closed form and inverted index

In this paper fast methods are proposed for search the fracture zones in seismic databases on two types of data: seismic section (two-dimensional data) and seismic cube (three-dimensional data). These methods are an integral part of the mapping technology for filtering channels and large volumes of seismic data and useful for automating interpretation of heterogeneous seismic data. The proposed methods for searching the similarity of fracture zones were investigated using the Open Seismic Repository reference dataset, which contains information about geological rocks in the area of the North Sea and compared with other known methods for solving this problem, the results were discussed in the article.

Deep Neural Networks for Creating Reliable PmP Database With a Case Study in Southern California

AbstractRecent progresses in artificial intelligence and machine learning make it possible to automatically identify seismic phases from exponentially growing seismic data. Despite some exciting successes in automatic picking of the first P‐ and S‐wave arrivals, auto‐identification of later seismic phases such as the Moho‐reflected PmP waves remains a significant challenge in matching the performance of experienced analysts. The main difficulty of machine‐identifying PmP waves is that the identifiable PmP waves are rare, making the problem of identifying the PmP waves from a massive seismic database inherently unbalanced. In this work, by utilizing a high‐quality PmP data set (10,192 manual picks) in southern California, we develop PmPNet, a deep‐neural‐network‐based algorithm to automatically identify PmP waves efficiently; by doing so, we accelerate the process of identifying the PmP waves. PmPNet applies similar techniques in the machine learning community to address the unbalancement of PmP datasets. The architecture of PmPNet is a residual neural network (ResNet)‐autoencoder with additional predictor block, where encoder, decoder, and predictor are equipped with ResNet connection. We conduct systematic research with field data, concluding that PmPNet can efficiently achieve high precision and high recall simultaneously to automatically identify PmP waves from a massive seismic database. Applying the pre‐trained PmPNet to the seismic database from January 1990 to December 1999 in southern California, we obtain nearly twice more PmP picks than the original PmP data set, providing valuable data for other studies such as mapping the topography of the Moho discontinuity and imaging the lower crust structures of southern California.

Conventional data display and implications for the interpretation of seismic profiles: a discussion on the ViDEPI seismic database offshore Apulia (southern Italy)

Conventional data display and implications for the interpretation of seismic profiles: a discussion on the ViDEPI seismic database offshore Apulia (southern Italy)