A novel technique for automatic seismic data processing using both integral and local feature of seismograms

A novel technique for automatic seismic data processing using both integral and local feature of seismograms was presented in this paper. Here, the term integral feature of seismograms refers to feature which may depict the shape of the whole seismograms. However, unlike some previous efforts which completely abandon the DIAL approach, i.e., signal detection, phase identifi- cation, association, and event localization, and seek to use envelope cross-correlation to detect seismic events directly, our technique keeps following the DIAL approach, but in addition to detect signals corresponding to individual seismic phases, it also detects continuous wave-trains and explores their feature for phase-type identification and signal association. More concrete ideas about how to define wave-trains and combine them with various detections, as well as how to measure and utilize their feature in the seismic data processing were expatiated in the paper. This approach has been applied to the routine data processing by us for years, and test results for a 16 days＇ period using data from the Xinjiang seismic station network were presented. The automatic processing results have fairly low false and missed event rate simultaneously, showing that the new technique has good application prospects for improvement of the automatic seismic data processing.

Online Data Processing Methods in Xlet Applications for Seismic Early Warning and Emergency Services on Interactive Digital Television

In this paper, I described the methods that I used for the creation of Xlets, which are Java applets that are developed for the IDTV environment;and the methods for online data retrieval and processing that I utilized in these Xlets. The themes that I chose for the Xlets of the IDTV applications are Earthquake and Tsunami Early Warning;Recent Seismic Activity Report;and Emergency Services. The online data regarding the Recent Seismic Activity Report application are provided by the Kandilli Observatory and Earthquake Research Institute (KOERI) of Bogazici University in Istanbul;while the online data for the Earthquake and Tsunami Early Warning and the Emergency Services applications are provided by the Godoro website which I used for storing (and retrieving by the Xlets) the earthquake and tsunami early warning simulation data, and the DVB network subscriber data (such as name and address information) for utilizing in the Emergency Services (Police, Ambulance and Fire Department) application. I have focused on the methodologies to use digital television as an efficient medium to convey timely and useful information regarding seismic warning data to the public, which forms the main research topic of this paper.

Seismic Data Processing Approaches for the Study of Gas Hydrates in the East China Sea

A comprehensive study of the data profiles, including the 2D seismic data, single channel seismic data, shallow sections, etc., reveals that gas hydrates occur in the East China Sea. A series of special techniques are used in the processing of seismic data, which include enhancing the accuracy of velocity analysis and resolution, estimating the wavelet, suppressing the multiple, preserving the relative amplitude, using the DMO and AVO techniques and some special techniques in dealing with the wave impedance. The existence of gas hydrates is reflected in the subbottom profiles in the appearance of BSRs, amplitude anomalies, velocity anomalies and AVO anomalies, etc. Hence the gas hydrates can be identified and predicted. It is pointed out that the East China Sea is a favorable area of the gas hydrates resource, and the Okinawa Trough is a target area of gas hydrates reservoir.

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

Seismic Data Processing and Interpretation on the Loess Plateau, Part1: Seismic Data Processing

Branching river channels and the coexistence of valleys, ridges, hiils, and slopes＇as the result of long-term weathering and erosion form the unique loess topography. The Changqing Geophysical Company, working in these complex conditions, has established a suite of technologies for high-fidelity processing and fine interpretation of seismic data. This article introduces the processes involved in the data processing and interpretation and illustrates the results.

Data processing of the Kuiyang-ST2000 deep-towed high-resolution multichannel seismic system and application to South China Sea data

The Kuiyang-ST2000 deep-towed high-resolution multichannel seismic system was designed by the First Institute of Oceanography,Ministry of Natural Resources(FIO,MNR).The system is mainly composed of a plasma spark source(source level:216 dB,main frequency:750 Hz,frequency bandwidth:150-1200 Hz)and a towed hydrophone streamer with 48 channels.Because the source and the towed hydrophone streamer are constantly moving according to the towing configuration,the accurate positioning of the towing hydrophone array and the moveout correction of deep-towed multichannel seismic data processing before imaging are challenging.Initially,according to the characteristics of the system and the towing streamer shape in deep water,travel-time positioning method was used to construct the hydrophone streamer shape,and the results were corrected by using the polynomial curve fitting method.Then,a new data-processing workflow for Kuiyang-ST2000 system data was introduced,mainly including float datum setting,residual static correction,phase-based moveout correction,which allows the imaging algorithms of conventional marine seismic data processing to extend to deep-towed seismic data.We successfully applied the Kuiyang-ST2000 system and methodology of data processing to a gas hydrate survey of the Qiongdongnan and Shenhu areas in the South China Sea,and the results show that the profile has very high vertical and lateral resolutions(0.5 m and 8 m,respectively),which can provide full and accurate details of gas hydrate-related and geohazard sedimentary and structural features in the South China Sea.

Application of multi-scaled morphology in denoising seismic data

In this paper, multi-scaled morphology is introduced into the digital processing domain for land seismic data. First, we describe the basic theory of multi-scaled morphology image decomposition of exploration seismic waves; second, we illustrate how to use multi-scaled morphology for seismic data processing using two real examples. The first example demonstrates suppressing the surface waves in pre-stack seismic records using multi-scaled morphology decomposition and reconstitution and the other example demonstrates filtering different interference waves on the seismic record. Multi-scaled morphology filtering separates signal from noise by the detailed differences of the wave shapes. The successful applications suggest that multi-scaled morphology has a promising application in seismic data processing.

Sedimentary Microfacies and Porosity Modeling of Deep-Water Sandy Debris Flows by Combining Sedimentary Patterns with Seismic Data： An Example from Unit I of Gas Field A, South China Sea

Sandy debris flow deposits are present in Unit I during Miocene of Gas Field A in the Baiyun Depression of the South China Sea. The paucity of well data and the great variability of the sedimentary microfacies make it difficult to identify and predict the distribution patterns of the main gas reservoir, and have seriously hindered further exploration and development of the gas field. Therefore, making full use of the available seismic data is extremely important for predicting the spatial distribution of sedimentary microfacies when constructing three-dimensional reservoir models. A suitable reservoir modeling strategy or workflow controlled by sedimentary microfacies and seismic data has been developed. Five types of seismic attributes were selected to correlate with the sand percentage, and the root mean square （RMS） amplitude performed the best. The relation between the RMS amplitude and the sand percentage was used to construct a reservoir sand distribution map. Three types of main sedimentary microfacies were identified： debris channels, fan lobes, and natural levees. Using constraints from the sedimentary microfacies boundaries, a sedimentary microfacies model was constructed using the sequential indicator and assigned value simulation methods. Finally, reservoir models of physical properties for sandy debris flow deposits controlled by sedimentary microfacies and seismic inversion data were established. Property cutoff values were adopted because the sedimentary microfacies and the reservoir properties from well-logging interpretation are intrinsically different. Selection of appropriate reservoir property cutoffs is a key step in reservoir modeling when using simulation methods based on sedimentary microfacies control. When the abnormal data are truncated and the reservoir properties probability distribution fits a normal distribution, microfacies-controlled reservoir property models are more reliable than those obtained from the sequence Gauss simulation method. The cutoffs for effective porosity of the debris channel, fan lobe, and natural levee facies were 0.2, 0.09, and 0.12, respectively; the corresponding average effective porosities were 0.24, 0.13, and 0.15. The proposed modeling method makes full use of seismic attributes and seismic inversion data, and also makes the property data of single-well depositional microfacies more conformable to a normal distribution with geological significance. Thus, the method allows use of more reliable input data when we construct a model of a sandy debris flow.

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%.

Application of Fractional Fourier Transform in seismic data denoising

Data processing is a basic and crucial factor in seismic exploration,which can influence the effect of subsequent processing directly. Thus the selection of appropriate method for data processing is one of the most important tasks throughout the work. By simulating,the authors analyze and compare Fractional Fourier Transform( FRFT) and Wigner-Ville distribution( WVD),then summarize the similarities and advantages and disadvantages of the two methods. The results reveal that FRFT is more effective and suitable for application in seismic exploration than WVD.

SeisResoDiff: Seismic resolution enhancement based on a diffusion model

High resolution of post-stack seismic data assists in better interpretation of subsurface structures as well as high accuracy of impedance inversion. Therefore, geophysicists consistently strive to acquire higher resolution seismic images in petroleum exploration. Although there have been successful applications of conventional signal processing and machine learning for post-stack seismic resolution enhancement,there is limited reference to the seismic applications of the recent emergence and rapid development of generative artificial intelligence. Hence, we propose to apply diffusion models, among the most popular generative models, to enhance seismic resolution. Specifically, we apply the classic diffusion model—denoising diffusion probabilistic model(DDPM), conditioned on the seismic data in low resolution, to reconstruct corresponding high-resolution images. Herein the entire scheme is referred to as SeisResoDiff. To provide a comprehensive and clear understanding of SeisResoDiff, we introduce the basic theories of diffusion models and detail the optimization objective's derivation with the aid of diagrams and algorithms. For implementation, we first propose a practical workflow to acquire abundant training data based on the generated pseudo-wells. Subsequently, we apply the trained model to both synthetic and field datasets, evaluating the results in three aspects: the appearance of seismic sections and slices in the time domain, frequency spectra, and comparisons with the synthetic data using real well-logging data at the well locations. The results demonstrate not only effective seismic resolution enhancement,but also additional denoising by the diffusion model. Experimental comparisons indicate that training the model on noisy data, which are more realistic, outperforms training on clean data. The proposed scheme demonstrates superiority over some conventional methods in high-resolution reconstruction and denoising ability, yielding more competitive results compared to our previous research.

A deep learning framework for suppressing prestack seismic random noise without noise-free labels

Random noise attenuation is significant in seismic data processing.Supervised deep learning-based denoising methods have been widely developed and applied in recent years.In practice,it is often time-consuming and laborious to obtain noise-free data for supervised learning.Therefore,we propose a novel deep learning framework to denoise prestack seismic data without clean labels,which trains a high-resolution residual neural network(SRResnet)with noisy data for input and the same valid data with different noise for output.Since valid signals in noisy sample pairs are spatially correlated and random noise is spatially independent and unpredictable,the model can learn the features of valid data while suppressing random noise.Noisy data targets are generated by a simple conventional method without fine-tuning parameters.The initial estimates allow signal or noise leakage as the network does not require clean labels.The Monte Carlo strategy is applied to select training patches for increasing valid patches and expanding training datasets.Transfer learning is used to improve the generalization of real data processing.The synthetic and real data tests perform better than the commonly used state-of-the-art denoising methods.

大倾角煤田地震勘探关键技术研究

我国大倾角煤层分布范围广、储量丰富。由于大倾角煤层开采难度较大,在开采中的安全隐患非常严重,因而在开采前能够掌握准确的煤层地质信息,查明煤层的赋存范围、倾角、构造发育情况等地质信息,为煤矿的开采工作提供最基础的地质依据。对于煤层倾角大于30°的区域开展地震勘探仍然存在一系列没有解决的基本技术难题。因此,研究地震勘探在大倾角煤层地区的应用方法,既非常必要,又十分迫切。将地震勘探方法应用到新疆和布克赛尔蒙古自治县某大倾角煤田地质勘探工作中,针对研究区的大倾角煤层特点,在地震勘探资料采集中采用小道距、变偏移距和高覆盖,资料处理中采用叠前时间偏移等关键技术,获得了较高品质的地震资料和较可靠的地质成果,以期为其它类似地区的地震勘探工作提供借鉴。

A real-time AI-assisted seismic monitoring system based on new nodal stations with 4G telemetry and its application in the Yangbi M_(S) 6.4 aftershock monitoring in southwest China

A rapidly deployable dense seismic monitoring system which is capable of transmitting acquired data in real time and analyzing data automatically is crucial in seismic hazard mitigation after a major earthquake.However,it is rather difficult for current seismic nodal stations to transmit data in real time for an extended period of time,and it usually takes a great amount of time to process the acquired data manually.To monitor earthquakes in real time flexibly,we develop a mobile integrated seismic monitoring system consisting of newly developed nodal units with 4G telemetry and a real-time AI-assisted automatic data processing workflow.The integrated system is convenient for deployment and has been successfully applied in monitoring the aftershocks of the Yangbi M_(S) 6.4 earthquake occurred on May 21,2021 in Yangbi County,Dali,Yunnan in southwest China.The acquired seismic data are transmitted almost in real time through the 4G cellular network,and then processed automat-ically for event detection,positioning,magnitude calculation and source mechanism inversion.From tens of seconds to a couple of minutes at most,the final seismic attributes can be presented remotely to the end users through the integrated system.From May 27 to June 17,the real-time system has detected and located 7905 aftershocks in the Yangbi area before the internal batteries exhausted,far more than the catalog provided by China Earthquake Networks Center using the regional permanent stations.The initial application of this inte-grated real-time monitoring system is promising,and we anticipate the advent of a new era for Real-time Intelligent Array Seismology(RIAS),for better monitoring and understanding the subsurface dynamic pro-cesses caused by Earth's internal forces as well as anthropogenic activities.

Full wave seismic exploration technology

Reflected wave seismology has the following defects:the acquisition design is based on the assumption of layered media,the signal processing suppresses weak signals such as diffracted wave and scattered wave,and the seismic wave band after the image processing is narrow.They limit the full utilization of broadband raw data.The concept of full wave seismic exploration is redefined based on the idea of balanced utilization of reflected wave,diffracted wave and scattered wave information,its characteristics and adaptive conditions are clarified.A set of key technologies suitable for full wave seismic exploration are put forward.During seismic acquisition period,it is necessary to adopt multi geometry,i.e.embed small bin,small offset and small channel interval data in conventional geometry.By discretizing of common midpoint(CMP)gathers,small offset with high coverage,the weak signals such as diffracted wave and scattered wave in the raw seismic data can be enhanced.During seismic processing,the signal and noise in the original seismic data need to be redefined at first.The effective signals of seismic data are enhanced through merging of multi-geometry data.By means of differential application of data with different bin sizes and different arrangement modes,different regimes of seismic waves can be effectively decomposed and imaged separately.During seismic interpretation stage,making the most of the full wave seismic data,and adopting well-seismic calibration on multi-scale and multi-dimension,the seismic attributes in multi-regimes and multi-domains are interpreted to reveal interior information of complex lithology bodies and improve the lateral resolution of non-layered reservoirs.

信息化测震数据管理平台的设计与实现

依托公共安全信息化工程,测震台网数据管理平台在数据资源池环境下,采用分层架构和模块化开发,实现了实时流波形接入和存储、站网基础信息管理、常规数据产品产出、针对特定地震的产品产出、波形数据质量分析等测震台网核心业务的信息化。通过平台的接口和界面,事件波形处理与服务、数据质量分析、震源参数基础数据库建设等测震台网业务取得了长足进步。该平台具有业务集成性、功能实用性、资源共享性和技术先进性等特点,有效提升了测震台网信息化水平,为测震台网现代化发展发挥了积极作用。

Study on Anti-Disturbance and High-Resolution Shallow Seismic Exploration of Active Faults in Urban Regions

The significance of detection of urban active faults and the general situation concerning detection of urban active faults in the world are briefly introduced. In a brief description of the basic principles of anti-disturbance and high-resolution shallow seismic exploration, the stress is put on the excitation of seismic sources, the performance of digital seismographs, receiving mode and conditions, geometry as well as data acquisition, processing and interpretation in the anti-disturbance and high-resolution shallow seismic exploration of urban active faults. The study indicates that a controlled seismic source with a linear or nonlinear frequency-conversion scanning function and the relevant seismographs must be used in data acquisition, as well as working methods for small group interval, small offset, multi-channel receiving, short-array and high-frequency detectors for receiving are used. Attention should be paid to the application of techniques for static correction of refraction, noise suppressing, high-precision analysis of velocity, wavelet compressing, zero-phasing of wavelet and pre-stacking migration to data processing and interpretation. Finally, some cases of anti-disturbance and high-resolution shallow seismic exploration of urban active faults are present in the paper.

Attenuation of seismic migration smile artifacts with deep learning

Attenuation of migration artifacts on Kirchhoff migrated seismic data can be challenging due to the relatively low amplitude of migration artifacts compared to reflections as well as the overlap in the kinematics of reflection and migration smiles.Several‘conventional’filtering methods exist and recently deep learning based workflows have been proposed.A deep learning workflow can be a simple and fast alternative to existing methods.In case of supervised training of a deep neural network using training data made by physics-based modelling or actual migrations is expensive and lacks diversity in terms of noise,amplitude,frequency content and wavelet.This can result in poor generalization beyond the training data without re-training and transfer learning.In this paper we demonstrate successful applications of migration smile separation using a conventional U-net architecture.The novelty in our approach is that we do not use synthetic data created from physics-based modelling,but instead use only synthetic data build form basic geometric shapes.Our domain of application is the migrated common offset domain,or simply the stack of the pre-stack migrated data,where reflections resemble local geology and migration smiles are upward convex hyperbolic patterns.Both patterns were randomly perturbed in many ways while maintaining their intrinsic features.This approach is inspired by the common practice of data augmentation in deep learning for machine vision applications.Since many of the standard data augmentation techniques lack a geophysical motivation,we have instead perturbed our synthetic training data in ways to make more sense for a signal processing perspective or given our‘domain knowledge’of the problem at hand.We did not have to retrain the network to produce good results on the field dataset.The large variety and diversity in examples enabled the trained neural network to show encouraging results on synthetic and field datasets that were not used in training.

应用生成对抗网络的地震数据重建和去噪一体化方法

在实际采集过程中,受地形条件和人为因素的影响,地震数据不仅在空间上会出现采样不足或不规则的情况,而且会混入噪声,不利于后续地震数据的处理和解释。通常将重建与去噪分为两个阶段处理,这样往往会引入额外的误差。为此,文中提出了一种基于条件韦氏生成对抗网络(cWGAN)的地震数据重建去噪一体化方法,该方法研究的重点是在缺失道和噪声的混合干扰下,准确提取地震数据的有效特征。首先,以U-Net模型为基本网络结构来构建生成器模型,分级提取地震数据同相轴特征;在判别器模型中引入条件约束,引导生成器优化梯度方向。其次,建立重建和去噪误差描述模型,该模型设计了一体化损失函数,可以兼顾重建与去噪两方面的处理任务。最后,经过合成数据和实际数据测试,证明文中所提的网络模型恢复的地震数据信噪比更高且具有较强鲁棒性。