Yihuai Lou

Zhejiang University

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Naihao Liu

Xi'an Jiaotong University

Bo Zhang

Aerospace Information Research Institute

Danping Cao

China University of Petroleum, East China

Hao Wu

China University of Geosciences

Rongchang Liu

Research Institute of Petroleum Exploration and Development

Jinghuai Gao

Xi'an Jiaotong University

Huijing Fang

Anhui Academy of Coal Science

Tengfei Lin

Research Institute of Petroleum Exploration and Development

Jiale Wang

Xi'an Jiaotong University

Zhizhou Huo

State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation

Cooperative Institutions

Xi'an Jiaotong University

Zhejiang University

China National Petroleum Corporation (China)

Chinese Academy of Sciences

China University of Petroleum, Beijing

Sinopec (China)

Research Institute of Petroleum Exploration and Development

Powerchina Huadong Engineering Corporation (China)

University of Electronic Science and Technology of China

University of Alabama

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NS2NS: Self-Learning for Seismic Image Denoising

IEEE Transactions on Geoscience and Remote Sensing (2022)

Naihao Liu Jiale Wang Jinghuai Gao Kai Yu Yihuai Lou

Attenuation of incoherent noise is an effective way to improve signal-to-noise ratio (SNR) of seismic data. Recently, supervised deep learning based methods have been widely utilized for seismic image denoising, which often need plenty of noise-free data as training labels. However, noise-free seismic data are often unavailable in field applications. We propose an unsupervised learning method (NS2NS) to train a denoising network by using single noisy seismic data. The proposed model is based on two basic truths of seismic data: (1) High self-similarity of seismic data; (2) Spatially independence of incoherent noise in seismic data. To implement the proposed method, we first build a sampling workflow to generate paired noisy images based on single noisy seismic image. Moreover, we create similar noisy images that are similar but different with the original noisy image by using the proposed self-similar sampler. The original noisy images and generated similar noisy images are then fused by using a suggested Bernoulli sampler to create new paired noisy images. These new paired noisy images are used as the input and target of the denoising model, respectively. Next, an end-to-end convolutional neural network (CNN) is built for seismic image denoising, which aims to learn features of valid signals and suppress unpredictable random noise. Finally, we apply the proposed NS2NS method to both synthetic and field data. The results show that our proposed method can effectively suppress incoherent noise while preserving valid signals.

Seismic Noise

10.1109/tgrs.2022.3217289

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Citations (30)

Seismic spectrum decomposition using flattened multiple seismic traces

Zhizhou Huo Bo Zhang Yihuai Lou Xiwu Liu

Time-frequency analysis of seismic data helps seismic interpreters to analyze the frequency of seismic data varying with spatial locations and is widely used in assisting reservoir characterization. The time-frequency algorithms can be categorized into simple projection based methods and computationally intensive least-square based approaches. Current time-frequency analysis algorithms are usually implemented on single seismic trace. As a result, the decomposed frequency components are strong affected by noise and lack of spatial continuity. Simultaneous processing multiple seismic traces is an effect mean to stabilize the solution and improve the spatial continuity. To consider the structures apparent in seismic images, we first flatten the seismic traces centered at the analysis seismic trace. We then simultaneously perform time-frequency analysis for the flattened seismic traces. In this paper, we only show the multi-trace spectrum decomposition using marching pursuit algorithm. Instead of obtaining the best match wavelet to the analysis trace in each decomposition iteration, we compute an optimal wavelet shared by neighboring flattened traces. Therefore our method might effectively suppress the data noise and stabilize the performance along the apparent structures in the seismic image. It is found that our method generate a plausible time-frequency spectrum with high resolution and greatly improves spatial continuity in decomposed frequency components. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 1:50 PM Presentation Time: 2:40 PM Location: 221B Presentation Type: Oral

10.1190/segam2019-3216907.1

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Automatic Seismic Fault Interpretation with the Aid of Data-, Physics-, and Math-assisted Synthetic Data Generation

IEEE Transactions on Geoscience and Remote Sensing (2024)

Yihuai Lou Yusheng Wang Yunmin Chen Naihao Liu Daosheng Ling

Seismic fault interpretation is a crucial task for hydrocarbon reservoir characterization, CO ₂ geological storage, and geothermal energy evaluation. Deep learning (DL)-based methods have been conducted to accelerate seismic fault interpretation and studies have shown that the most practicable way is to train neural networks using synthetic data with ground truth labels. However, synthetic and field data are different in local seismic structures, seismic reflection characteristics, and seismic fault features. These differences would lead to the poor generalization of DL-based methods and unreliable fault predictions. We propose an automatic fault interpretation method with the aid of the Data-, Physics-, and Math-assisted synthetic data generation, including the Data-assisted module, the Physics-assisted module, and the Math-assisted module. The Data-assisted module provides structural features and reflection characteristics of seismic events. The Physics-assisted module provides seismic fault features from physical experiments. The Math-assisted module generates realistic synthetic data and ground truth fault labels based on the extracted features. We then propose the Multi-scale Attention-based Convolutional Neural Network (MSACNN), by combining a simplified deeplab module and attention mechanism. Finally, we train the MSACNN using the generated synthetic dataset. To illustrate the validity and generalization of the proposed model, we apply it to synthetic data and two 3D real seismic volumes. The superiority of the proposed method is experimentally demonstrated with the qualitative and quantitative comparisons of fault interpretation results using different methods.

10.1109/tgrs.2024.3402739

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Simulating the procedure of manual seismic horizon picking

Geophysics (2020)

Yihuai Lou Bo Zhang Huijing Fang Danping Cao Kangning Wang

Manual seismic horizon picking is the least efficient interpretation technique in terms of time and effort. The loop-tie is a key “element” and the most time-consuming task in manual horizon picking, which ensures the accuracy of horizon picking. Autopicking techniques have been used since the early 1980s. However, there are few studies simulating the procedure of manual seismic horizon picking and quantitatively evaluating the autopicked horizons. In our proposed method, we perform autopicking on inline and crossline seismic vertical slices independently, similar to the manual horizon picking procedure. We then evaluate the picked horizons using a loop-tie step similar to the loop-tie checking in manual horizon picking. To simulate the loop-tie step in manual picking, we define two dip attributes for each time sample of seismic traces, which are the “left” and “right” reflector dips. We only preserve the portions of the tracked horizons that meet the defined loop-tie checking. Next, we merge the tracked horizons centered at the seeded seismic traces. The two-way traveltime of the merged horizons functions as a “hard” control for the final step of autopicking. Finally, we use the seismic dip attribute to track the horizons over the seismic survey under the hard controls. The real data demonstrate that our algorithm can extract accurate horizons near discontinuities such as faults and unconformities.

Classification of discontinuities

Merge (version control)

10.1190/geo2020-0021.1

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Recent Technological and Methodological Advances for the Investigation of Submarine Landslides

Journal of Marine Science and Engineering (2022)

Zhigang Shan Hao Wu Weida Ni Miaojun Sun Kuanjun Wang

Submarine landslides have attracted widespread attention, with the continuous development of ocean engineering. Due to the recent developments of in-situ investigation and modelling techniques of submarine landslides, significant improvements were achieved in the evolution studies on submarine landslides. The general characteristics of typical submarine landslides in the world are analyzed. Based on this, three stages of submarine landslide disaster evolution are proposed, namely, the submarine slope instability evolution stage, the large deformation landslide movement stage, and the stage of submarine landslide deposition. Given these three stages, the evolution process of submarine landslide disaster is revealed from the perspectives of in-situ investigation techniques, physical simulation, and numerical simulation methods, respectively. For long-term investigation of submarine landslides, an in-situ monitoring system with long-term service and multi-parameter collaborative observation deserves to be developed. The mechanism of submarine landslide evolution and the early warning factors need to be further studied by physical modelling experiments. The whole process of the numerical simulation of submarine landslides, from seabed instability to large deformation sliding to the impact on marine structures, and economizing the computational costs of models by advanced techniques such as parallel processing and GPU-accelerators, are the key development directions in numerical simulation. The current research deficiencies and future development directions in the subject of submarine landslides are proposed to provide a useful reference for the prediction and early warning of submarine landslide disasters.

Submarine landslide

10.3390/jmse10111728

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Citations (24)

Orientation estimate of 3D seismic events using dynamic programming

Bo Zhang Yihuai Lou Hao Wu

Seismic volumetric dip and azimuth, which together reflect the orientations of seismic events, are important geometric attributes in assisting 3D seismic data interpretation. Current methods compute seismic volumetric dip and azimuth by analyzing seismic amplitude or deduced seismic attributes centered at analysis sample. Unfortunately, current methods usually fail to produce an accurate estimate of dip and azimuth for the samples which locate nearby the chaotic reflections such as faults and unconformity. Choosing a proper temporal window size along time axis is another challenge for the current methods. Instead of computing the dip and azimuth sample by sample, we propose to determine the dip and azimuth for all samples of a seismic trace simultaneously. We treat the dip and azimuth estimate as a global optimization problem and employ dynamic programing to obtain the dip and azimuth of seismic traces. The dynamic programming does not require a temporal window size and aims to align the analysis seismic trace with nearby seismic traces. The time lag set needed to align the seismic traces is then converted to the dip and azimuth of the analysis seismic trace. The application demonstrate that our method successfully avoids the sequence artifacts which are the common byproduct of current methods. Presentation Date: Tuesday, September 17, 2019 Session Start Time: 8:30 AM Presentation Time: 11:00 AM Location: 221B Presentation Type: Oral

10.1190/segam2019-3216886.1

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Citations (3)

Mimicking the process of manual sequence stratigraphy well correlation

Interpretation (2021)

Huijing Fang Yihuai Lou Bo Zhang Huaimin Xu Man Lu

Stratigraphic correlation of well logs is based on interactive interpreter-based pattern recognition. A skilled interpreter identifies similar patterns (such as upward fining and coarsening) in user-defined well sections and links them using either a conscious or subconscious stratigraphic model. This manual stratigraphic correlation of numerous wells in mature fields can be time-consuming and error-prone. To expedite the process of stratigraphic correlation, we have performed semiautomatic stratigraphic correlation of wireline logs from multiple wells using improved dynamic time warping (IDTW). IDTW uses semblance, which compares the shape of the well logs, to replace the Euclidean distance in pairwise error computation. The resulting error matrix is compatible with the lateral nonstationary variation of well logs in the same formation. The workflow begins with interpreting stratigraphic well tops on user-defined well sections that is similar to the current process of stratigraphy analysis. The interpreted wells are then treated as reference wells to aid in interpreting well tops for other wells. Necessary manual interventions are incorporated during the semiautomatic stratigraphic correlation process. We applied our method to two experimental fields: a sand-rich reservoir and a mud-rich reservoir. The applications illustrate that our method performs well in aggradational strata and successfully predicts discontinuities with manual interventions.

Wireline

10.1190/int-2020-0172.1

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Citations (2)

Generating Seismic Horizon Using Multiple Seismic Attributes

IEEE Geoscience and Remote Sensing Letters (2020)

Bo Zhang Jie Qi Yihuai Lou Huijing Fang Danping Cao

Today's 3-D seismic surveys usually contain hundreds of inline and crossline vertical seismic slices. Seismic interpreters usually need to spend weeks or even months for manually picking horizons on vertical seismic slices. Researchers have developed algorithms to accelerate horizon picking and most algorithms employ the seismic reflector's dip as the input. However, the computed seismic reflector's dip is usually inaccurate near and across the discontinuities in the seismic images. Note that the time samples which belong to the same horizon should have approximate similar seismic instantaneous phase values. We propose to automatically track the seismic horizon simultaneously considering the seismic reflector's dip and instantaneous phase attributes. Our algorithm aims to achieve three objectives: 1) minimizing the difference between the dip computed using tracked horizon and seismic dip attribute, 2) minimizing the difference among instantaneous phase value of the time samples on the tracked horizon, and 3) the tracked horizon exactly passes through user-defined control points (seeds). A constrained conjugate least-square algorithm is employed to solve our optimization problem. The applications show that the tracked horizon which only uses dip attribute would "jump" from one seismic event to another seismic event near the unconformity zone. However, the horizon tracked using the proposed method strictly follows the same seismic event over the whole seismic survey.

Classification of discontinuities

Reflector (photography)

10.1109/lgrs.2020.2991775

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Citations (18)

Semiautomated seismic horizon interpretation using the encoder-decoder convolutional neural network

Geophysics (2019)

Hao Wu Bo Zhang Tengfei Lin Danping Cao Yihuai Lou

The seismic horizon is a critical input for the structure and stratigraphy modeling of reservoirs. It is extremely hard to automatically obtain an accurate horizon interpretation for seismic data in which the lateral continuity of reflections is interrupted by faults and unconformities. The process of seismic horizon interpretation can be viewed as segmenting the seismic traces into different parts and each part is a unique object. Thus, we have considered the horizon interpretation as an object detection problem. We use the encoder-decoder convolutional neural network (CNN) to detect the “objects” contained in the seismic traces. The boundary of the objects is regarded as the horizons. The training data are the seismic traces located on a user-defined coarse grid. We give a unique training label to the time window of seismic traces bounded by two manually picked horizons. To efficiently learn the waveform pattern that is bounded by two adjacent horizons, we use variable sizes for the convolution filters, which is different than current CNN-based image segmentation methods. Two field data examples demonstrate that our method is capable of producing accurate horizons across the fault surface and near the unconformity which is beyond the current capability of horizon picking method.

10.1190/geo2018-0672.1

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Citations (78)

Separation and Suppression of Strong Reflections via a Multiscale Attention Deep Learning Model

IEEE Transactions on Geoscience and Remote Sensing (2024)

Shengjun Li Jianhu Gao Yihuai Lou Jinyong Gui Dongyang He

The existence of coal seams suppresses other useful information, especially the below-thin layers, and is unfavorable for delineating the target reservoirs beneath them. The matching pursuit (MP) based methods are commonly used for removing strong reflections caused by coal seams. They first decompose a seismic trace into several wavelets based on a user-defined wavelet dictionary and then separate the most similar wavelet with the coal seam. However, how to define a complete wavelet dictionary and how to maintain horizontal continuity are two unsolved issues. We propose a multi-scale attention deep learning (MSADL) model for separating and removing seismic strong reflections. First, we suggest a workflow to generate a synthetic data set for model training based on the characteristics of field data and well logs. Next, we build an MSADL model by integrating the discrete wavelet transform (DWT) and convolutional block attention module (CBAM) into the widely used Unet. After model training, we apply the well-trained MSADL model to 3D field data in the Sichuan Basin, China for the separation and removal of strong reflections and characterization of the beneath target thin layers.

Atmospheric models

Separation (statistics)

10.1109/tgrs.2024.3376336

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Citations (1)