Signal-to-noise ratio application to seismic marker analysis and fracture detection

Seismic data with high signal-to-noise ratios(SNRs) are useful in reservoir exploration. To obtain high SNR seismic data, significant effort is required to achieve noise attenuation in seismic data processing, which is costly in materials, and human and financial resources. We introduce a method for improving the SNR of seismic data. The SNR is calculated by using the frequency domain method. Furthermore, we optimize and discuss the critical parameters and calculation procedure. We applied the proposed method on real data and found that the SNR is high in the seismic marker and low in the fracture zone. Consequently, this can be used to extract detailed information about fracture zones that are inferred by structural analysis but not observed in conventional seismic data.

An automatic seismic signal detection method based on fourth-order statistics and applications

Real-time, automatic, and accurate determination of seismic signals is critical for rapid earthquake reporting and early warning. In this study, we present a correction trigger function（CTF） for automatically detecting regional seismic events and a fourth-order statistics algorithm with the Akaike information criterion（AIC） for determining the direct wave phase, based on the differences, or changes, in energy, frequency, and amplitude of the direct P- or S-waves signal and noise. Simulations suggest for that the proposed fourth-order statistics result in high resolution even for weak signal and noise variations at different amplitude, frequency, and polarization characteristics. To improve the precision of establishing the S-waves onset, first a specific segment of P-wave seismograms is selected and the polarization characteristics of the data are obtained. Second, the S-wave seismograms that contained the specific segment of P-wave seismograms are analyzed by S-wave polarization filtering. Finally, the S-wave phase onset times are estimated. The proposed algorithm was used to analyze regional earthquake data from the Shandong Seismic Network. The results suggest that compared with conventional methods, the proposed algorithm greatly decreased false and missed earthquake triggers, and improved the detection precision of direct P- and S-wave phases.

Improving Airgun Signal Detection with Small-Aperture Seismic Array in Yunnan

Repeating airgun sources are eco-friendly sources for monitoring the changes in the physical properties of subsurface mediums,but their signals decay quickly and are buried in the noises soon after traveling short distances.Stacking waveforms from different airgun shots recorded by a single seismic station(shot stacking)is the most popular technique to detect weak signals from noisy backgrounds,and has been widely used to process the data of Fixed Airgun Signal Transmission Stations(FASTS)in China.However,shot stacking sacrifices the time resolution in monitoring to recover a qualified airgun signal by stacking many shots at distance stations,and also suffers from persistent local noises.In this paper,we carried out several small-aperture seismic array experiments around the Binchuan FAST Station(BCFASTS)in Yunnan Province,China,and applied the array technique to improve airgun signal detection.The results show that seismic array processing combining with shot stacking can suppress seismic noises more efficiently,and provide better signal-to-noise ratio(SNR)and coherent airgun signals with less airgun shots.This work suggests that the array technique is a feasible and promising tool in FAST to increase the time resolution and reduce noise interference on routine monitoring.

A 15-year-Long catalog of seismicity in the Eastern Tennessee Seismic Zone(ETSZ)using matched filter detection

We present a detailed catalog of 13671 earthquakes in the Eastern Tennessee Seismic Zone(ETSZ)that spans January 1,2005 to July 31,2020.We apply a matched filter detection technique on over 15 years of continuous data,resulting in arguably the most complete catalog of seismicity in the ETSZ yet.The magnitudes of newly detected events are determined by computing the amplitude ratio between the detections and templates using a principal component fit.We also compute the b-value for the new catalog and comparatively relocate a subset of newly detected events using XCORLOC and hypoDD,which shows a more defined structure at depth.We find the greatest concentration along and to the east of the New York-Alabama Lineament,as defined by the magnetic anomaly,supporting the argument that this feature likely is related to the generation of seismicity in the ETSZ.We examine seismicity in the vicinity of the Watts Bar Reservoir,which is located about 5 km from the epicenter of the M_(W) 4.4 December 12,2018 Decatur,Tennessee earthquake,and find possible evidence for reservoir modulated seismicity in this region.We also examine seismicity in the entire ETSZ to search for a correlation between shallow earthquakes and seasonal hydrologic changes.Our results show limited evidence for hydrologicallydriven shallow seismicity due to seasonal groundwater levels in the ETSZ,which contradicts previous studies hypothesizing that most intraplate earthquakes are associated with the dynamics of hydrologic cycles.

General seismic wave and phase detection software driven by deep learning

We developed an automatic seismic wave and phase detection software based on PhaseNet,an efficient and highly generalized deep learning neural network for P-and S-wave phase picking.The software organically combines multiple modules including application terminal interface,docker container,data visualization,SSH protocol data transmission and other auxiliary modules.Characterized by a series of technologically powerful functions,the software is highly convenient for all users.To obtain the P-and S-wave picks,one only needs to prepare threecomponent seismic data as input and customize some parameters in the interface.In particular,the software can automatically identify complex waveforms(i.e.continuous or truncated waves)and support multiple types of input data such as SAC,MSEED,NumPy array,etc.A test on the dataset of the Wenchuan aftershocks shows the generalization ability and detection accuracy of the software.The software is expected to increase the efficiency and subjectivity in the manual processing of large amounts of seismic data,thereby providing convenience to regional network monitoring staffs and researchers in the study of Earth's interior.

Singularity detection of the thin bed seismic signals with wavelet transform

The location of singularities may be detected by local maxima of the wavelet transform modulus. The digital modeling and focusing process to wavelet transform of the reflecting seismic signals have been done. It has been found that the locations of singularities after wavelet transform are only affected by two factors, their original locations and the seismic wavelet length, which says it does not matter with what shape the wavelet will be. The wavelet length can be determined according to the wavelet transform results and be eliminated thereafter so that we are able to detect thin bed seismic signal with resolution of l/32 wavelength. The singularities have been recovered with improved resolution of the seismic section by real data processing.

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources.Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs.However,manual channel picking is both time consuming and tedious.Moreover,similar to any other process dependent on human intervention,manual channel picking is error prone and inconsistent.To address these issues,automatic channel detection is both necessary and important for efficient and accurate seismic interpretation.Modern systems make use of real-time image processing techniques for different tasks.Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies.In this paper,we propose an innovative automatic channel detection algorithm based on machine learning techniques.The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process.The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches.We provide a field data example to demonstrate the performance of the new algorithm.The training phase gave a maximum accuracy of 84.6%for the classifier and it performed even better in the testing phase,giving a maximum accuracy of 90%.

TransQuake: A transformer-based deep learning approach for seismic P-wave detection

Recent years,we have witnessed the increasing research interest in developing machine learning,especially deep learning which provides approaches for enhancing the performance of microearthquake detection.While considerable research efforts have been made in this direction,most of the state-of-the-art solutions are based on Convolutional Neural Network(CNN)structure,due to its remarkable capability of modeling local and static features.Indeed,the globally dynamic characteristics contained within time series data(i.e.,seismic waves),which cannot be fully captured by CNN-based models,have been largely ignored in previous studies.In this paper,we propose a novel deep learning approach,TransQuake,for seismic P-wave detection.The approach is based on the most advanced sequential model,namely Transformer.To be specific,TransQuake can exploit the STA/LTA algorithm for adapting the three-component structure of seismic waves as input,and take advantage of the multi-head attention mechanism for conducting explainable model learning.Extensive evaluations of the aftershocks following the 2008 Wenchuan MW 7.9 earthquake clearly demonstrates that TransQuake is able to achieve the best detection performance which excels the results obtained using other baselines.Meanwhile,experimental results also validate the interpretability of the results obtained by TransQuake,such as the attention distribution of seismic waves in different positions,and the analysis of the optimal relationship between coda wave and P-wave for noise identification.

Robust salt-dome detection using the ranking of texture-based attributes

The accurate interpretation and analysis of seismic data heavily depends on the robustness of the algorithms used. We focus on the robust detection of salt domes from seismic surveys. We discuss a novel feature-ranking classification model for saltdome detection for seismic images using an optimal set of texture attributes. The proposed algorithm overcomes the limitations of existing texture attribute-based techniques, which heavily depend on the relevance of the attributes to the geological nature of salt domes and the number of attributes used for accurate detection. The algorithm combines the attributes from the Gray-Level Co-occurrence Matrix （GLCM）, the Gabor filters, and the eigenstructure of the covariance matrix with feature ranking using the information content. The top-ranked attributes are combined to form the optimal feature set, which ensures that the algorithm works well even in the absence of strong reflectors along the salt-dome boundaries. Contrary to existing salt-dome detection techniques, the proposed algorithm is robust and eomputationally efficient, and works with small-sized feature sets. I used the Netherlands F3 block to evaluate the performance of the proposed algorithm. The experimental results suggest that the proposed workflow based on information theory can detect salt domes with accuracy superior to existing salt-dome detection techniques.

Numerical analysis of the resonance mechanism of the lumped parameter system model for acoustic mine detection

The method of numerical analysis is employed to study the resonance mechanism of the lumped parameter system model for acoustic mine detection. Based on the basic principle of the acoustic resonance technique for mine detection and the characteristics of low-frequency acoustics, the “soil-mine” system could be equivalent to a damping “mass-spring” resonance model with a lumped parameter analysis method. The dynamic simulation software, Adams, is adopted to analyze the lumped parameter system model numerically. The simulated resonance frequency and anti-resonance frequency are 151 Hz and 512 Hz respectively, basically in agreement with the published resonance frequency of 155 Hz and anti-resonance frequency of 513 Hz, which were measured in the experiment. Therefore, the technique of numerical simulation is validated to have the potential for analyzing the acoustic mine detection model quantitatively. The influences of the soil and mine parameters on the resonance characteristics of the soil–mine system could be investigated by changing the parameter setup in a flexible manner.

A new approach for salt dome detection using a 3D multidirectional edge detector

Accurate salt dome detection from 3D seismic data is crucial to different seismic data analysis applications. We present a new edge based approach for salt dome detection in migrated 3D seismic data. The proposed algorithm overcomes the drawbacks of existing edge-based techniques which only consider edges in the x （crossline） and y （inline） directions in 2D data and the x （crossline）, y （inline）, and z （time） directions in 3D data. The algorithm works by combining 3D gradient maps computed along diagonal directions and those computed in x, y, and z directions to accurately detect the boundaries of salt regions. The combination of x, y, and z directions and diagonal edges ensures that the proposed algorithm works well even if the dips along the salt boundary are represented only by weak reflectors. Contrary to other edge and texture based salt dome detection techniques, the proposed algorithm is independent of the amplitude variations in seismic data. We tested the proposed algorithm on the publicly available Netherlands offshore F3 block. The results suggest that the proposed algorithm can detect salt bodies with high accuracy than existing gradient based and texture-based techniques when used separately. More importantly, the proposed approach is shown to be computationally efficient allowing for real time implementation and deployment.

Detectability and reliability analysis of the local seismic network in Pakistan

The detectability and reliability analysis for the local seismic network is performed employing by Bungum and Husebye technique. The events were relocated using standard computer codes for hypocentral locations. The detectability levels are estimated from the twenty-five years of recorded data in terms of 50%, 90% and 100% cumulative detectability thresholds, which were derived from frequency-magnitude distribution. From this analysis the 100% level of detectability of the network is M L=1.7 for events which occur within the network. The accuracy in hypocentral solutions of the network is investigated by considering the fixed real hypocenter within the network. The epicentral errors are found to be less than 4 km when the events occur within the network. Finally, the problems faced during continuous operation of the local network, which effects its detectability, are discussed.

South China Sea Typhoon Hagibis enhanced Xinfengjiang Reservoir seismicity

There was an evident increase in the number of earthquakes in the Xinfengjiang Reservoir from June to July 2014 after the landing of Typhoon Hagibis.To understand the spatial and temporal evolution of this microseismicity,we built a high-precision earthquake catalog for 2014 and relocated 2275 events using recently developed methods for event picking and catalog construction.Seismicity occurred in the southeastern part of the reservoir,with the preferred fault plane orientation aligned along the Heyuan Fault.The total seismic energy peaked when the typhoon passed through the reservoir,and seismicity correlated with typhoon energy.In contrast,a limited seismic response was observed during the later Typhoon Rammasun.Combining data regarding the water level in the Xinfengjiang Reservoir and seismicity frequency changes in the Taiwan region during these two typhoon events,we suggest that typhoon activity may increase microseism energy by impacting fault stability around the Xinfengjiang Reservoir.Whether a fault can be activated also depends on how close the stress accumulation is to its failure point.

Assessment of earthquake location uncertainties for the design of local seismic networks

The ability to estimate earthquake source locations,along with the appraisal of relevant uncertainties,is paramount in monitoring both natural and human-induced micro-seismicity.For this purpose,a monitoring network must be designed to minimize the location errors introduced by geometrically unbalanced networks.In this study,we first review different sources of errors relevant to the localization of seismic events,how they propagate through localization algorithms,and their impact on outcomes.We then propose a quantitative method,based on a Monte Carlo approach,to estimate the uncertainty in earthquake locations that is suited to the design,optimization,and assessment of the performance of a local seismic monitoring network.To illustrate the performance of the proposed approach,we analyzed the distribution of the localization uncertainties and their related dispersion for a highly dense grid of theoretical hypocenters in both the horizontal and vertical directions using an actual monitoring network layout.The results expand,quantitatively,the qualitative indications derived from purely geometrical parameters(azimuthal gap(AG))and classical detectability maps.The proposed method enables the systematic design,optimization,and evaluation of local seismic monitoring networks,enhancing monitoring accuracy in areas proximal to hydrocarbon production,geothermal fields,underground natural gas storage,and other subsurface activities.This approach aids in the accurate estimation of earthquake source locations and their associated uncertainties,which are crucial for assessing and mitigating seismic risks,thereby enabling the implementation of proactive measures to minimize potential hazards.From an operational perspective,reliably estimating location accuracy is crucial for evaluating the position of seismogenic sources and assessing possible links between well activities and the onset of seismicity.

Hydrocarbon detections using multi-attributes based quantum neural networks in a tight sandstone gas reservoir in the Sichuan Basin, China

A direct hydrocarbon detection is performed by using multi-attributes based quantum neural networks with gas fields.The proposed multi-attributes based quantum neural networks for hydrocarbon detection use data clustering and local wave decomposition based seismic attenuation characteristics,relative wave impedance features of prestack seismic data as the selected multiple attributes for one tight sandstone gas reservoir and further employ principal component analysis combined with quantum neural networks for giving the distinguishing results of the weak responses of the gas reservoir,which is hard to detect by using the conventional technologies.For the seismic data from a tight sandstone gas reservoir in the Sichuan basin,China,we found that multiattributes based quantum neural networks can effectively capture the weak seismic responses features associated with gas saturation in the gas reservoir.This study is hoped to be useful as an aid for hydrocarbon detections for the gas reservoir with the characteristics of the weak seismic responses by the complement of the multiattributes based quantum neural networks.

基于概率的震级完备度方法评估云南地震台网的监测能力

为了科学评估云南地震台网的监测能力,分析云南地区的地震监测能力时空分布特征,采用基于概率的震级完备度(PMC)方法,利用2009-2023年云南地震台网地震目录、震相和台站信息等数据,计算了云南地震台网在云南地区的平均地震监测能力和在不同深度的监测能力,得到滇中、滇西局部地区的基于概率的完备性震级M_(P)=1.5,云南全境基本都能监测到2.5级地震。评估了研究时段内云南地震台网台站监测能力的演变,发现部分台站的监测能力持续下降。但自2018年增加20个台站后,云南2.0及2.5级地震的监测范围有一定的扩大,但微震监测能力提升不明显,可能是因为2018年以来云南一直处于小震弱活动状态。最后,对应用PMC方法规划提升台网监测能力进行了展望。

虚拟震源地震探测方法及其应用

SsPmp震相是远震直达S波在地表激发的下行P波在Moho面反射的震相,具有信号能量大、信噪比高、不易受近地表沉积层和地壳小尺度结构扰动的影响等优势.虚拟地震测深方法(VDSS)是近年来发展的利用SsPmp震相与直达Ss波震相的到时差来研究地壳厚度(或Moho面深度)的探测方法.本文介绍了VDSS方法的原理、优势及其在实际应用中的表现.研究表明,VDSS方法在提高探测精度、降低成本、环境影响等方面具有显著优势,且成功应用于克拉通、造山带和沉积盆地、峨眉山大火成岩省等不同地质环境中,在探测地壳结构中展现出巨大潜力和应用价值.但是VDSS方法的准确度高度依赖于地震数据的质量,尤其是远震S波的清晰度和震中距的范围,使得该方法在复杂地质结构区域的应用受到较大限制.未来,VDSS与传统接收函数方法、地震层析成像、重力测量等多类地球物理方法的结合,有望为地壳结构探测提供更全面的约束.

Seismic prediction method of multiscale fractured reservoir

Common prestack fracture prediction methods cannot clearly distinguish multiplescale fractures. In this study, we propose a prediction method for macro- and mesoscale fractures based on fracture density distribution in reservoirs. First, we detect the macroscale fractures （larger than 1/4 wavelength） using the multidirectional coherence technique that is based on the curvelet transform and the mesoscale fractures （1/4-1/100 wavelength） using the seismic azimuthal anisotropy technique and prestack attenuation attributes, e.g., frequency attenuation gradient. Then, we combine the obtained fracture density distributions into a map and evaluate the variably scaled fractures. Application of the method to a seismic physical model of a fractured reservoir shows that the method overcomes the problem of discontinuous fracture density distribution generated by the prestack seismic azimuthal anisotropy method, distinguishes the fracture scales, and identifies the fractured zones accurately.

Joint Inversion of the 3D P Wave Velocity Structure of the Crust and Upper Mantle under the Southeastern Margin of the Tibetan Plateau Using Regional Earthquake and Teleseismic Data

The special seismic tectonic environment and frequent seismicity in the southeastern margin of the Qinghai-Tibet Plateau show that this area is an ideal location to study the present tectonic movement and background of strong earthquakes in China's Mainland and to predict future strong earthquake risk zones. Studies of the structural environment and physical characteristics of the deep structure in this area are helpful to explore deep dynamic effects and deformation field characteristics, to strengthen our understanding of the roles of anisotropy and tectonic deformation and to study the deep tectonic background of the seismic origin of the block＇s interior. In this paper, the three-dimensional （3D） P-wave velocity structure of the crust and upper mantle under the southeastern margin of the Qinghai-Tibet Plateau is obtained via observational data from 224 permanent seismic stations in the regional digital seismic network of Yunnan and Sichuan Provinces and from 356 mobile China seismic arrays in the southern section of the north-south seismic belt using a joint inversion method of the regional earthquake and teleseismic data. The results indicate that the spatial distribution of the P-wave velocity anomalies in the shallow upper crust is closely related to the surface geological structure, terrain and lithology. Baoxing and Kangding, with their basic volcanic rocks and volcanic clastic rocks, present obvious high-velocity anomalies. The Chengdu Basin shows low-velocity anomalies associated with the Quaternary sediments. The Xichang Mesozoic Basin and the Butuo Basin are characterised by low- velocity anomalies related to very thick sedimentary layers. The upper and middle crust beneath the Chuan-Dian and Songpan-Ganzi Blocks has apparent lateral heterogeneities, including low-velocity zones of different sizes. There is a large range of low-velocity layers in the Songpan-Ganzi Block and the sub-block northwest of Sichuan Province, showing that the middle and lower crust is relatively weak. The Sichuan Basin, which is located in the western margin of the Yangtze platform, shows high-velocity characteristics. The results also reveal that there are continuous low-velocity layer distributions in the middle and lower crust of the Daliangshan Block and that the distribution direction of the low-velocity anomaly is nearly SN, which is consistent with the trend of the Daliangshan fault. The existence of the low-velocity layer in the crust also provides a deep source for the deep dynamic deformation and seismic activity of the Daliangshan Block and its boundary faults. The results of the 3D P-wave velocity structure show that an anomalous distribution of high-density, strong-magnetic and high-wave velocity exists inside the crust in the Panxi region. This is likely related to late Paleozoic mantle plume activity that led to a large number of mafic and ultra-mafic intrusions into the crust. In the crustal doming process, the massive intrusion of mantle-derived material enhanced the mechanical strength of the crustal medium. The P-wave velocity structure also revealed that the upper mantle contains a low-velocity layer at a depth of 80-120 km in the Panxi region. The existence of deep faults in the Panxi region, which provide conditions for transporting mantle thermal material into the crust, is the deep tectonic background for the area＇s strong earthquake activity.