Application of 9-component S-wave 3D seismic data to study sedimentary facies and reservoirs in a biogasbearing area:A case study on the Pleistocene Qigequan Formation in Taidong area,Sanhu Depression,Qaidam Basin,NW China

To solve the problems in restoring sedimentary facies and predicting reservoirs in loose gas-bearing sediment,based on seismic sedimentologic analysis of the first 9-component S-wave 3D seismic dataset of China,a fourth-order isochronous stratigraphic framework was set up and then sedimentary facies and reservoirs in the Pleistocene Qigequan Formation in Taidong area of Qaidam Basin were studied by seismic geomorphology and seismic lithology.The study method and thought are as following.Firstly,techniques of phase rotation,frequency decomposition and fusion,and stratal slicing were applied to the 9-component S-wave seismic data to restore sedimentary facies of major marker beds based on sedimentary models reflected by satellite images.Then,techniques of seismic attribute extraction,principal component analysis,and random fitting were applied to calculate the reservoir thickness and physical parameters of a key sandbody,and the results are satisfactory and confirmed by blind testing wells.Study results reveal that the dominant sedimentary facies in the Qigequan Formation within the study area are delta front and shallow lake.The RGB fused slices indicate that there are two cycles with three sets of underwater distributary channel systems in one period.Among them,sandstones in the distributary channels of middle-low Qigequan Formation are thick and broad with superior physical properties,which are favorable reservoirs.The reservoir permeability is also affected by diagenesis.Distributary channel sandstone reservoirs extend further to the west of Sebei-1 gas field,which provides a basis to expand exploration to the western peripheral area.

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

Estimation of maximum mass velocity from macroseismic data: A new method and application to archeoseismological data

An important task in seismic hazard assessment is the estimation of intensity and frequency of rare strong seismic shaking, in particular, the long-term peak ground velocity values(PGVs). A recently proposed method is suitable for simply estimating PGVs based on the examination of the magnitude of displacements of rock blocks. The effectiveness of this method is demonstrated by results of studies on the source zones of two large earthquakes and a vicinity of one strong explosion. In this study, the method is applied to the examination of archeoseismological data from the ancient Rehovot-ba-Negev city and other ancient cities from the Negev desert(in Southern Israel) where numerous evidences of presumable seismic damage were found earlier. The cities and also a sophisticated irrigation system within the region, which existed in the Negev desert, were abandoned however in the middle of the seventh century. The abandonment could be caused by a combined effect, from not only the cessation of the state support from Byzantium as a result of the Arab conquest but also the severe destruction from the strong earthquake that hit the area at that time. The intensities of the seismic events that hit the cities were estimated earlier, which are within the range of 8 e9. Our estimates indicate that the PGV values are about 1.5 m/s. Hence, the magnitude of the causative earthquake could be in the range M z 6.5 e7.5, and the location of the epicenter might be at a distance of a few dozens of kilometers from the ancient Rehovot-ba-Negev city, while the other variants associated with the earthquake seem to be less probable.

Sparse Seismic Data Reconstruction Based on a Convolutional Neural Network Algorithm

At present,the acquisition of seismic data is developing toward high-precision and high-density methods.However,complex natural environments and cultural factors in many exploration areas cause difficulties in achieving uniform and intensive acquisition,which makes complete seismic data collection impossible.Therefore,data reconstruction is required in the processing link to ensure imaging accuracy.Deep learning,as a new field in rapid development,presents clear advantages in feature extraction and modeling.In this study,the convolutional neural network deep learning algorithm is applied to seismic data reconstruction.Based on the convolutional neural network algorithm and combined with the characteristics of seismic data acquisition,two training strategies of supervised and unsupervised learning are designed to reconstruct sparse acquisition seismic records.First,a supervised learning strategy is proposed for labeled data,wherein the complete seismic data are segmented as the input of the training set and are randomly sampled before each training,thereby increasing the number of samples and the richness of features.Second,an unsupervised learning strategy based on large samples is proposed for unlabeled data,and the rolling segmentation method is used to update(pseudo)labels and training parameters in the training process.Through the reconstruction test of simulated and actual data,the deep learning algorithm based on a convolutional neural network shows better reconstruction quality and higher accuracy than compressed sensing based on Curvelet transform.

Stratigraphic Sequence and Sedimentary Systems in the Middle-Southern Continental Slope of the East China Sea from Seismic Reflection Data: Exploration Prospects of Gas Hydrate

Many evidences for gas hydrate bearing sediments had been found in the continental slope of the East China Sea,such as bottom simulating reflections(BSRs),undersea gas springs,pyrite associated with methane leakage,mud diapirs/mud volcanos,bottom-water methane anomalies and so on.In this study,six key stratigraphic interfaces including T_0(seafloor),T_1(LGM,23 kyr B.P.),T_2(2.58 Myr),T_3(5.33 Myr),T_4(11.02 Myr)and T_5(16.12 Myr)were identified,and then five third-order sequences of SQIII1 to SQIII5 were divided.However,T5 in southern continental slope is not found,which shows that the middle-northern Okinawa Trough had begun to rift in the early Miocene,earlier than the southern segment.Four system tracts including lowstand systems tract(LST),transgressive systems tract(TST),highstand systems tract(HST)and falling stage systems tract(FSST)are further divided.The marine erosion interface of 11.02 Myr and regressive unconformity interface of 23 kyr B.P.indicate two large-scale sea level drop events in the research area.Seven typical seismic facies identified in the continental slope are continental shelf-edge deltas,littoral fluvial-delta plains,incised channels or submarine canyons,slope fans,submarine fans or coastal sandbars,littoral-neritic finegrained sediments,mud volcanos and some other geological bodies respectively.The minimum water depth for hydrate occurrence in the Okinawa Trough is 630 m,and the thickness of gas hydrate stability zone in continental slope is between 0 and 590 m.The calculated bottom boundary of hydrate stability zone is slightly deeper than BSRs on the seismic sections.The re-depositional turbidite sand bodies,such as canyon channels,slope fans and submarine fans developed in Quaternary strata,are the predominant hydrate reservoirs.According to developing process,the dynamic accumulation of hydrate systems can be divided into three evolutionary stages including canyon erosion and hydrate stability zone migration stage,sediments destabilizing and methane leakage stage,and channel filling and hydrate re-occurrence stage.

Velocity structure in the South Yellow Sea basin based on first-arrival tomography of wide-angle seismic data and its geological implications

The South Yellow Sea basin is filled with Mesozoic-Cenozoic continental sediments overlying pre-Palaeozoic and Mesozoic-Palaeozoic marine sediments.Conventional multi-channel seismic data cannot describe the velocity structure of the marine residual basin in detail,leading to the lack of a deeper understanding of the distribution and lithology owing to strong energy shielding on the top interface of marine sediments.In this study,we present seismic tomography data from ocean bottom seismographs that describe the NEE-trending velocity distributions of the basin.The results indicate that strong velocity variations occur at shallow crustal levels.Horizontal velocity bodies show good correlation with surface geological features,and multi-layer features exist in the vertical velocity framework(depth:0–10 km).The analyses of the velocity model,gravity data,magnetic data,multichannel seismic profiles,and drilling data showed that high-velocity anomalies(>6.5 km/s)of small(thickness:1–2 km)and large(thickness:>5 km)scales were caused by igneous complexes in the multi-layer structure,which were active during the Palaeogene.Possible locations of good Mesozoic and Palaeozoic marine strata are limited to the Central Uplift and the western part of the Northern Depression along the wide-angle ocean bottom seismograph array.Following the Indosinian movement,a strong compression existed in the Northern Depression during the extensional phase that caused the formation of folds in the middle of the survey line.This study is useful for reconstructing the regional tectonic evolution and delineating the distribution of the marine residual basin in the South Yellow Sea basin.

Research on Seismic Data Processing and Interpretation System Model Based on CSCW

Through analyzing the needs of seismic data processing and interpretation,a system model based on CSCW is designed.Using the technology of CSCW to build the environment of cooperative work allows the field data acquisition to possess the functions of remote real-time guidance by experts and remote real-time processing of the data.The model overcomes the influences and barriers existing in the areas

Tectonics of the Solomon Sea Basin from Vertical Gravity Gradient and Seismic Data

The Solomon Sea Basin is a Cenozoic back-arc spreading basin within the convergence system of the Pacific and Indo-Australian plates.Against the background of subduction polarity reversal,the current Solomon Sea Basin gradually formed a rhombic morphology with the subduction of the basin along the New Britain Trench and the Trobriand Trough.By analyzing the vertical gravity gradient,natural earthquake and seismic reflection data,this study determines the structural characteristics of the Solomon Sea Basin.It was found that the tectonics of the basin are characterized by the original expansion structure within the central part in addition to the structure induced by the latest subduction along the basin margin.The original spreading structure of the basin presented an east–west linear graben and horst controlled by normal faults during the basin expansion period.As a result of the subduction and slab-pull of the Solomon Sea Basin,extensional structure belts parallel to the New Britain Trench formed along the basin margin.

Structure and Temporal Variability of Mediterranean Water in Hydrological and Marine Seismic Data South of Portimao Canyon (Gulf of Cadiz), from 1999 to 2002

Hydrological and marine seismic data, collected in the Gulf of Cadiz (respectively in July 1999, 2000, 2001 and 2002, and in April 2000 and 2001) are analysed to reveal the various structures of Mediterranean Water (MW). Both the hydrological and seismic data clearly identify the MW undercurrents on the Iberian slope, detached MW eddies (meddies and a cyclone) and smaller fragments of MW (filaments and small eddies). Seismic reflectivity and synthetic reflectivity computed from hydrology, indicate that strong acoustic reflectors, associated with 8 - 64 m thick homogeneous water layers, are found above and below meddies and filaments, around the MW undercurrents, but mostly in the lower part of cyclones and below submesoscale eddies. Reflectors are also observed in the near surface layers where thermohaline contrasts are quite pronounced. The successful use of seismic data to locate submesoscale MW structures, superior to that of hydrology, is related to the improved horizontal resolution.

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

Blind Deconvolution of Seismic Data Based on the Spearman’s Rho

In this paper, we propose a novel seismic blind deconvolution approach based on the Spearman’s rho in the case of band-limited seismic data with a low dominant frequency and short data records. The Spearman’s rho is a measure of the dependence between two continuous random variables without the influence of the marginal distributions, by which a new criterion for blind deconvolution is constructed. The optimization program for new criterion of blind deconvolution is performed by applying Neidell’s wavelet model to the inverse filter. The noise-free and noisy synthetic data, onshore seismic trace in the Ordos Basin, and offshore stacked section in the Bohai Bay Basin examples show good results of the method.

Simultaneous Denoising and Interpolation of Seismic Data via the Deep Learning Method

Utilizing data from controlled seismic sources to image the subsurface structures and invert the physical properties of the subsurface media is a major effort in exploration geophysics. Dense seismic records with high signal-to-noise ratio(SNR) and high fidelity helps in producing high quality imaging results. Therefore, seismic data denoising and missing traces reconstruction are significant for seismic data processing. Traditional denoising and interpolation methods rarely occasioned rely on noise level estimations, thus requiring heavy manual work to deal with records and the selection of optimal parameters. We propose a simultaneous denoising and interpolation method based on deep learning. For noisy records with missing traces, we adopt an iterative alternating optimization strategy and separate the objective function of the data restoring problem into two sub-problems. The seismic records can be reconstructed by solving a least-square problem and applying a set of pre-trained denoising models alternatively and iteratively.We demonstrate this method with synthetic and field data.

Quantitative Comparative Study of the Performance of Lossless Compression Methods Based on a Text Data Model

Data compression plays a key role in optimizing the use of memory storage space and also reducing latency in data transmission. In this paper, we are interested in lossless compression techniques because their performance is exploited with lossy compression techniques for images and videos generally using a mixed approach. To achieve our intended objective, which is to study the performance of lossless compression methods, we first carried out a literature review, a summary of which enabled us to select the most relevant, namely the following: arithmetic coding, LZW, Tunstall’s algorithm, RLE, BWT, Huffman coding and Shannon-Fano. Secondly, we designed a purposive text dataset with a repeating pattern in order to test the behavior and effectiveness of the selected compression techniques. Thirdly, we designed the compression algorithms and developed the programs (scripts) in Matlab in order to test their performance. Finally, following the tests conducted on relevant data that we constructed according to a deliberate model, the results show that these methods presented in order of performance are very satisfactory:- LZW- Arithmetic coding- Tunstall algorithm- BWT + RLELikewise, it appears that on the one hand, the performance of certain techniques relative to others is strongly linked to the sequencing and/or recurrence of symbols that make up the message, and on the other hand, to the cumulative time of encoding and decoding.

Seismic Data Recovery with Curvelet Bivariate Shrinkage Function Based on Compressed Sensing

Recovery of under-sampled seismic data is a critical problem,in oil and gas exploration,therefore recovery algorithms with iterative shrinkage based on compressed sensing have been recently proposed. However most of these algorithms usually adopt a soft shrinkage function,which assumes that all of the sparse coefficients are independent of each other in curvelet or other domains,little attention has so far been devoted to the inter-dependencies of coefficients. In this paper,the dependencies of parent-child curvelet coefficients of seismic data are exploited by Bayesian estimation,moreover the new seismic data recovery algorithm via curvelet-based bivariate shrinkage function is proposed. First the respective parent-child curvelet coefficients joint distribution models of fully-sampled seismic data and noise signal caused by missing traces are established,then the bivariate shrinkage function according to the Bayesian maximum posterior probability estimation is obtained,finally the Landweber iterative shrinkage algorithm is used in the recovery process.When compared with existing recovery algorithms,it is proved that the proposed algorithm can obtain higher PSNR performance,and maintains the texture details better in events of seismic data

Method for Morphological Filtering in Seismic Data Processing

A new method is introduced to suppress the noise in seismic data processing. Based on the subtle difference in shape between the noise and the actual signal, we introduce morphologic filtering into seismic data processing. From the shape and the S/N we can see that the effect of morphologic filtering is superior to other methods like id-value filtering, neighbor average filtering, etc. The SNR of the signal after morphological filtering is comparatively great. In addition, the precision of the seismic data after morphological filtering is high. The characteristics of the actual signal, such as frequency and amplitude, are preserved. We give an example of the real seismic data processing using morphological filtering, in which the actual signal is retained, while the random high intensity noise was removed.

Modeling Investigation of Correlations between Elastic Properties Predicted by Rock Physics Model with Seismic Data (Case Study of an Iranian Oil Field)

Accurate characterization of seismic properties in the prediction of P-wave and S-wave velocities through carbonate reservoirs is necessary due to their intrinsic heterogeneity. Moreover, both the waves velocities mentioned above are applied to the uncertainty analysis as well as the complexity investigation presented in the carbonate reservoirs. In this study, three wells of an Iranian oil field which its formation is the upper part of the Sarvak (Mishrif) has been studied. In accordance with the petrophysical interpretation of this oil field using Geo-log software, a rock physics model has been constructed based on Xu-Payne model (2009) using Hampson-Russel software to predict the elastic properties like P-wave and S-wave velocities as well as density. In the following, some synthetic seismic traces have been created based on the rock physics model using Hampson-Russel software to obtain the correlation coefficients of the seismic data with both the predicted and measured elastic properties. As results, the obtained correlation coefficients show that the predicted elastic properties by the rock physics model have higher quality than the measured elastic properties. In addition, the correlation coefficients of the predicted elastic properties in the well number 1, 2, and 3 have approximately increased by 19.6, 21.3, and 18.2 percent, respectively, in comparison to the correlation coefficients of the measured elastic properties. Therefore, the predicted elastic properties can be replaced with the low-quality measured elastic properties. Eventually, some templates have been created to accurate characterization the carbonate reservoir based on the rock physics model and also show the high-quality correlations between the rock physics model and the measured data.

Time-domain identification of dynamic properties of layered soil by using extended Kalman filter and recorded seismic data

A novel time-domain identification technique is developed for the seismic response analysis of soil-structure interaction.A two-degree-of-freedom (2DOF) model with eight lumped parameters is adopted to model the frequency- dependent behavior of soils.For layered soil,the equivalent eight parameters of the 2DOF model are identified by the extended Kalman filter (EKF) method using recorded seismic data.The polynomial approximations for derivation of state estimators are applied in the EKF procedure.A realistic identification example is given for the layered-soil of a building site in Anchorage,Alaska in the United States.Results of the example demonstrate the feasibility and practicality of the proposed identification technique.The 2DOF soil model and the identification technique can be used for nonlinear response analysis of soil-structure interaction in the time-domain for layered or complex soil conditions.The identified parameters can be stored in a database tor use in other similar soil conditions,lfa universal database that covers information related to most soil conditions is developed in the thture,engineers could conveniently perform time history analyses of soil-structural interaction.

Source complexity of the 2016 M_W7.8 Kaikoura (New Zealand) earthquake revealed from teleseismic and InSAR data

On November 13, 2016, an MW7.8 earthquake struck Kaikoura in South Island of New Zealand. By means of back-projection of array recordings, ASTFs-analysis of global seismic recordings, and joint inversion of global seismic data and co-seismic In SAR data, we investigated complexity of the earthquake source. The result shows that the 2016 MW7.8 Kaikoura earthquake ruptured about 100 s unilaterally from south to northeast(~N28°–33°E), producing a rupture area about 160 km long and about 50 km wide and releasing scalar moment 1.01×1021 Nm. In particular, the rupture area consisted of two slip asperities, with one close to the initial rupture point having a maximal slip value ~6.9 m while the other far away in the northeast having a maximal slip value ~9.3 m. The first asperity slipped for about 65 s and the second one started 40 s after the first one had initiated. The two slipped simultaneously for about 25 s.Furthermore, the first had a nearly thrust slip while the second had both thrust and strike slip. It is interesting that the rupture velocity was not constant, and the whole process may be divided into 5 stages in which the velocities were estimated to be 1.4 km/s, 0 km/s, 2.1 km/s, 0 km/s and 1.1 km/s, respectively. The high-frequency sources distributed nearly along the lower edge of the rupture area, the highfrequency radiating mainly occurred at launching of the asperities, and it seemed that no high-frequency energy was radiated when the rupturing was going to stop.

Study of a Stratigraphic Trap of Paleocene/Late Cretaceous Age with the Help of Seismic Data in Sulaiman Foredeep and Kirthar Foredeep Area (Central &Southern Indus Basin, Pakistan)

The Research work comprises area of Punjab Platform, Sulaiman Foredeep and Eastern Part of Sulaiman Fold Belt (from east to west), which is further extended to Zamzama and Dadu area of Lower Indus Basin. Seismic data interpretation suggests a presence of a stratigraphic trap of Paleocene/Late Cretaceous age. It can act as a stratigraphic trap of reservoir quality for hydrocarbons. This feature is marked and discussed on various seismic sections. Sometimes it is difficult to locate this feature due to effect of folding, resulting from later compressions associated with a collision of Indian Plate. This structure could be explored for a hydrocarbon prospect in future. Such stratigraphic traps were not explored in the past.

Cyberattack Ramifications, The Hidden Cost of a Security Breach

In this in-depth exploration, I delve into the complex implications and costs of cybersecurity breaches. Venturing beyond just the immediate repercussions, the research unearths both the overt and concealed long-term consequences that businesses encounter. This study integrates findings from various research, including quantitative reports, drawing upon real-world incidents faced by both small and large enterprises. This investigation emphasizes the profound intangible costs, such as trade name devaluation and potential damage to brand reputation, which can persist long after the breach. By collating insights from industry experts and a myriad of research, the study provides a comprehensive perspective on the profound, multi-dimensional impacts of cybersecurity incidents. The overarching aim is to underscore the often-underestimated scope and depth of these breaches, emphasizing the entire timeline post-incident and the urgent need for fortified preventative and reactive measures in the digital domain.