Noise is inevitable when acquiring seismic data, and effective random noise attenuation is crucial for seismic data processing and interpretation. Training and inferencing two-stage deep learning-based denoising methods typically require massive noisy–clean or noisy–noisy pairs to train the network. In this paper, we propose an unsupervised seismic data denoising framework called a re-visible blind block network. It is a training-as-inferencing one-stage method and utilizes only single noisy data for denoising, thereby eliminating the effort to prepare training data pairs. First, we introduce a global masker and a corresponding mask mapper to obtain the denoised result containing all blind block information, enabling simultaneous optimization of all blind blocks via the loss function. The global masker consists of two complementary block-wise masks. It is utilized to mask noisy data to obtain two corrupted data, which are then input into the denoising network for noise attenuation. The mask mapper samples the value of blind blocks in the denoised data and projects it onto the same channel to gather the denoised results of all blind blocks together. Second, the original noisy data are incorporated into the network training process to prevent information loss, and a hybrid loss function is employed for updating the network parameters. Synthetic and field seismic data experiments demonstrate that our proposed method can protect seismic signals while suppressing random noise compared with traditional methods and several state-of-the-art unsupervised deep learning denoising techniques.
{"title":"Re-visible blind block network: An unsupervised seismic data random noise attenuation method","authors":"Jing Wang, Bangyu Wu, Hui Yang, Bo Li","doi":"10.1111/1365-2478.13559","DOIUrl":"10.1111/1365-2478.13559","url":null,"abstract":"<p>Noise is inevitable when acquiring seismic data, and effective random noise attenuation is crucial for seismic data processing and interpretation. Training and inferencing two-stage deep learning-based denoising methods typically require massive noisy–clean or noisy–noisy pairs to train the network. In this paper, we propose an unsupervised seismic data denoising framework called a re-visible blind block network. It is a training-as-inferencing one-stage method and utilizes only single noisy data for denoising, thereby eliminating the effort to prepare training data pairs. First, we introduce a global masker and a corresponding mask mapper to obtain the denoised result containing all blind block information, enabling simultaneous optimization of all blind blocks via the loss function. The global masker consists of two complementary block-wise masks. It is utilized to mask noisy data to obtain two corrupted data, which are then input into the denoising network for noise attenuation. The mask mapper samples the value of blind blocks in the denoised data and projects it onto the same channel to gather the denoised results of all blind blocks together. Second, the original noisy data are incorporated into the network training process to prevent information loss, and a hybrid loss function is employed for updating the network parameters. Synthetic and field seismic data experiments demonstrate that our proposed method can protect seismic signals while suppressing random noise compared with traditional methods and several state-of-the-art unsupervised deep learning denoising techniques.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"72 7","pages":"2739-2760"},"PeriodicalIF":1.8,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multicomponent seismic technology utilizes the kinematic and dynamic characteristics of reflected P-waves and converted S-waves to reduce ambiguity in seismic exploration. The imaging and inversion accuracy of P-SV-converted waves are important in determining whether multicomponent seismic exploration can achieve higher exploration accuracy than conventional P-wave exploration. Pre-stack inversion of P-SV-converted waves requires precise input of P-SV-converted wave angle-domain common-image gathers. Consequently, the P-SV-converted wave angle-domain common-image gather extraction accuracy will significantly affect the P-SV-converted wave inversion accuracy. However, existing methods for extracting P-SV-converted wave angle-domain common-image gathers are constrained by issues such as the P- and S-wave crosstalk artefacts, low-frequency noises and inaccurate calculation of P-wave incident angles, leading to poor imaging accuracy. We study an angle-domain cross-correlation imaging condition and address three key issues based on this condition: the decoupling of P- and S-waves, the separation of up-going and down-going waves and the precise calculation of P-wave incident angles. Our strategies facilitate high-precision extraction of P-SV-converted wave angle-domain common-image gathers using elastic wave reverse-time migration. In this paper, first, we employ the first-order velocity-dilatation-rotation elastic wave equations to decouple P- and S-waves automatically during source and receiver wavefield extrapolations. Second, we calculate the optical flow vectors of P- and S-waves to ensure stable calculations of wave propagation directions. Based on this, we obtain up-going and down-going waves of P- and S-waves. Meanwhile, we calculate the incident angle of the source P-wave using geometric relations. Lastly, we apply the angle-domain imaging condition to achieve high-precision extraction of P-SV-converted wave angle-domain common-image gathers. Model examples demonstrate the effectiveness and advantages of the proposed method.
多分量地震技术利用反射 P 波和转换 S 波的运动学和动力学特征来减少地震勘探中的模糊性。P-SV 转换波的成像和反演精度是决定多分量地震勘探能否达到比传统 P 波勘探更高的勘探精度的重要因素。P-SV 转换波的叠前反演需要精确输入 P-SV 转换波角域共像集。因此,P-SV 转换波角域共像采集提取精度将极大地影响 P-SV 转换波反演精度。然而,现有的 P-SV 转换波角域共像集提取方法受到 P 波和 S 波串扰伪影、低频噪声和 P 波入射角计算不准确等问题的制约,导致成像精度不高。我们研究了一种角域交叉相关成像条件,并在此基础上解决了三个关键问题:P 波和 S 波的解耦,上行波和下行波的分离,以及 P 波入射角的精确计算。我们的策略有助于利用弹性波逆时迁移技术高精度提取 P-SV 转换波角度域共同图像集。在本文中,首先,我们采用一阶速度-扩张-旋转弹性波方程,在源波场和接收器波场外推过程中自动解耦 P 波和 S 波。其次,我们计算 P 波和 S 波的光流矢量,以确保波传播方向的稳定计算。在此基础上,我们得到 P 波和 S 波的上行波和下行波。同时,我们利用几何关系计算源 P 波的入射角。最后,我们应用角域成像条件,实现高精度提取 P-SV 转换波的角域共像集。模型实例证明了所提方法的有效性和优势。
{"title":"A method for extracting P-SV-converted wave angle-domain common-image gathers based on elastic-wave reverse-time migration","authors":"Qianqian Ci, Bingshou He","doi":"10.1111/1365-2478.13571","DOIUrl":"10.1111/1365-2478.13571","url":null,"abstract":"<p>Multicomponent seismic technology utilizes the kinematic and dynamic characteristics of reflected P-waves and converted S-waves to reduce ambiguity in seismic exploration. The imaging and inversion accuracy of P-SV-converted waves are important in determining whether multicomponent seismic exploration can achieve higher exploration accuracy than conventional P-wave exploration. Pre-stack inversion of P-SV-converted waves requires precise input of P-SV-converted wave angle-domain common-image gathers. Consequently, the P-SV-converted wave angle-domain common-image gather extraction accuracy will significantly affect the P-SV-converted wave inversion accuracy. However, existing methods for extracting P-SV-converted wave angle-domain common-image gathers are constrained by issues such as the P- and S-wave crosstalk artefacts, low-frequency noises and inaccurate calculation of P-wave incident angles, leading to poor imaging accuracy. We study an angle-domain cross-correlation imaging condition and address three key issues based on this condition: the decoupling of P- and S-waves, the separation of up-going and down-going waves and the precise calculation of P-wave incident angles. Our strategies facilitate high-precision extraction of P-SV-converted wave angle-domain common-image gathers using elastic wave reverse-time migration. In this paper, first, we employ the first-order velocity-dilatation-rotation elastic wave equations to decouple P- and S-waves automatically during source and receiver wavefield extrapolations. Second, we calculate the optical flow vectors of P- and S-waves to ensure stable calculations of wave propagation directions. Based on this, we obtain up-going and down-going waves of P- and S-waves. Meanwhile, we calculate the incident angle of the source P-wave using geometric relations. Lastly, we apply the angle-domain imaging condition to achieve high-precision extraction of P-SV-converted wave angle-domain common-image gathers. Model examples demonstrate the effectiveness and advantages of the proposed method.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"72 7","pages":"2469-2485"},"PeriodicalIF":1.8,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The gravimetric forward method is crucial in geophysical applications for a gravimetric interpretation of the Earth's inner structure. In this study, we present the gravimetric forward modelling open-source software that incorporates a graphical user interface. This software allows data preparation, manipulation and result interpretation both spatially and spectrally. For spatial domain modelling, it uses prism and tesseroid elements, whereas in the spectral domain, it extends Parker's formulas within specified boundaries. The software's utility is demonstrated through synthetic models and real-world applications, including calculating corrections for topography, sediments and consolidated crust using ETOPO1 and CRUST1.0 models. Performance comparisons show that Parker's method delivers computation speed superior to that of the prism, tesseroid and Terrain gravity forward (TGF) software, with variances ranging within ±12 mGal for