Joint denoising and classification network: Application to microseismic event detection in hydraulic fracturing distributed acoustic sensing monitoring
文献类型：期刊论文
| 作者 | Wu, Shaojiang2,3; Wang, Yibo2,3; Liang, Xing1 |
| 刊名 | GEOPHYSICS
 |
| 出版日期 | 2023-07-01 |
| 卷号 | 88期号:4页码:L53-L63 |
| ISSN号 | 0016-8033 |
| DOI | 10.1190/GEO2022-0296.1 |
| 英文摘要 | Deep learning has been applied to microseismic event detec-tion over the past few years. However, it is still challenging to detect microseismic events from records with low signal-to-noise ratios (S/Ns). To achieve high accuracy of event detection in a low-S/N scenario, we have developed an end-to-end network that jointly performs denoising and classification tasks (JointNet) and applied it to fiber-optic distributed acoustic sensing (DAS) micro -seismic data. JointNet consists of 2D convolution layers that are suitable for extracting features (such as moveout and amplitude) of the dense DAS data. Moreover, JointNet uses a joint loss, rather than any intermediate loss, to simultaneously update the coupled denoising and classification modules. With the preceding advantages, JointNet is capable of simultaneously attenuating noise and preserving fine details of events and therefore improv-ing the accuracy of event detection. We generate synthetic events and collect real background noise from a real hydraulic fracturing project and then expand them using data augmentation methods to yield sufficient training data sets. We train and validate the JointNet using training data sets of different S/Ns and compare it with the conventional classification networks visual geometry group (VGG) and deep VGG (DVGG). The results demonstrate the effectiveness of JointNet: it consistently outperforms the VGG and DVGG in all S/N scenarios and it has a superior capability to detect events, especially in a low-S/N scenario. Finally, we apply JointNet to detect microseismic events from the real DAS data acquired during hydraulic fracturing. JointNet successfully detects all manually detected events and has a better performance than VGG and DVGG. |
| WOS关键词 | RECOGNITION ; STRAIN ; PHASE |
| 资助项目 | CAS Project for Young Scientists in Basic Research[YSBR-020] ; National Key Ramp;D Program of China[2021YFA0716800] ; major field test project of China National Petroleum Corporation[2020F-44] |
| WOS研究方向 | Geochemistry & Geophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:001030659300001 |
| 出版者 | SOC EXPLORATION GEOPHYSICISTS - SEG |
| 资助机构 | CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; National Key Ramp;D Program of China ; National Key Ramp;D Program of China ; National Key Ramp;D Program of China ; National Key Ramp;D Program of China ; major field test project of China National Petroleum Corporation ; major field test project of China National Petroleum Corporation ; major field test project of China National Petroleum Corporation ; major field test project of China National Petroleum Corporation ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; National Key Ramp;D Program of China ; National Key Ramp;D Program of China ; National Key Ramp;D Program of China ; National Key Ramp;D Program of China ; major field test project of China National Petroleum Corporation ; major field test project of China National Petroleum Corporation ; major field test project of China National Petroleum Corporation ; major field test project of China National Petroleum Corporation ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; National Key Ramp;D Program of China ; National Key Ramp;D Program of China ; National Key Ramp;D Program of China ; National Key Ramp;D Program of China ; major field test project of China National Petroleum Corporation ; major field test project of China National Petroleum Corporation ; major field test project of China National Petroleum Corporation ; major field test project of China National Petroleum Corporation ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; National Key Ramp;D Program of China ; National Key Ramp;D Program of China ; National Key Ramp;D Program of China ; National Key Ramp;D Program of China ; major field test project of China National Petroleum Corporation ; major field test project of China National Petroleum Corporation ; major field test project of China National Petroleum Corporation ; major field test project of China National Petroleum Corporation |
| 源URL | [http://ir.iggcas.ac.cn/handle/132A11/111293]  |
| 专题 | 地质与地球物理研究所_中国科学院油气资源研究重点实验室 |
| 通讯作者 | Wang, Yibo |
| 作者单位 | 1.PetroChina Zhejiang Oilfield Co, Hangzhou, Peoples R China 2.Chinese Acad Sci, Inst Geol & Geophys, Lab Petr Resource Res, Beijing, Peoples R China 3.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wu, Shaojiang,Wang, Yibo,Liang, Xing. Joint denoising and classification network: Application to microseismic event detection in hydraulic fracturing distributed acoustic sensing monitoring[J]. GEOPHYSICS,2023,88(4):L53-L63. |
| APA | Wu, Shaojiang,Wang, Yibo,&Liang, Xing.(2023).Joint denoising and classification network: Application to microseismic event detection in hydraulic fracturing distributed acoustic sensing monitoring.GEOPHYSICS,88(4),L53-L63. |
| MLA | Wu, Shaojiang,et al."Joint denoising and classification network: Application to microseismic event detection in hydraulic fracturing distributed acoustic sensing monitoring".GEOPHYSICS 88.4(2023):L53-L63. |
入库方式： OAI收割
来源：地质与地球物理研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。