In current seismic acquisition practice, there is an increasing drive for sparsely (in space) acquired data, often in irregular geometry. These surveys can trade off subsurface information for efficiency/cost - creating a problem of "missing seismic data" that can greatly hinder subsequent processing and interpretation. Reconstruction of regularly sampled dense data from highly sparse, irregular data can therefore aid in processing and interpretation of these far sparser, more efficient seismic surveys. Here, two methods are compared to solve the reconstruction problem in both space-time and wavenumber-frequency domain. This requires an operator that maps sparse to dense data: the operator is generally unknown, being the inverse of a known data sampling operator. As such, here the deterministic inversion is efficiently solved by least squares optimisation using a numerically efficient Python-based linear operator representation. An alternative approach is probabilistic and uses deep learning. Here, two deep learning architectures are benchmarked against each other and the deterministic approach; a Recurrent Inference Machine (RIM), which is designed specifically to solve inverse problems given known forward operators, and the well-known U-Net. The trained deep learning networks are capable of successfully mapping sparse to dense seismic data for a range of different datasets and decimation percentages, thereby significantly reducing spatial aliasing in the wavenumber-frequency domain. The deterministic inversion on the contrary, could not reconstruct the missing data and thus did not reduce the undesired spatial aliasing. Results show that the application of Deep Learning for seismic reconstruction is promising, but the treatment of large-volume, multi-component seismic datasets will require dedicated learning architectures not yet realisable with existing tools.
School of Mathematics and Physics, China University of Geosciences（中国地质大学数学与物理学院）；Department of Computer Science, Central China Normal University（华中师范大学计算机学院）
Missing traces in acquired seismic data is a common occurrence during the collection of seismic data. Deep neural network (DNN) has shown considerable promise in restoring incomplete seismic data. However, several DNN-based approaches ignore the specific characteristics of seismic data itself, and only focus on reducing the difference between the recovered and the original signals. In this study, a novel Seismic U-net InterpolaTor (SUIT) is proposed to preserve the seismic texture information while reconstructing the missing traces. Aside from minimizing the reconstruction error, SUIT enhances…
SeisFusion: Constrained Diffusion Model with Input Guidance for 3D Seismic Data Interpolation and Reconstruction
Key Laboratory of Earth Exploration and Information Techniques of Education Ministry, College of Geophysics, Chengdu University of Technology（成都理工大学地球物理学院 教育部地球勘探与信息技术重点实验室）；College of Computer Science and Cyber Security, Chengdu University of Technology（成都理工大学计算机与网络安全学院）
We present a technique for reconstructing subsurface velocity model changes from time-lapse seismic survey data using full-waveform inversion (FWI). The technique is based on simultaneously inverting multiple survey vintages, with model difference regularization using the total variation (TV) seminorm. We compare the new TV-regularized time-lapse FWI with the $L_2$-regularized joint inversion proposed in our earlier work, using synthetic data sets that exhibit survey repeatability issues. The results demonstrate clear advantages of the proposed TV-regularized joint inversion over alternatives …
MDA GAN: Adversarial-Learning-based 3-D Seismic Data Interpolation and Reconstruction for Complex Missing
College of computer science and technology, China University of Petroleum (East China)（中国石油大学（华东）计算机科学与技术学院）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；Shengli Oilfield Company, SINOPEC（中国石化胜利油田分公司）；Center on Frontiers of Computing Studies, Peking University（北京大学计算前沿研究中心）
本文提出三维 MDA GAN 框架，通过三判别器、特征拼接和 TCE 损失解决地震数据复杂缺失重建问题，在高比例随机缺失、连续缺失及富断层盐体场景中优于现有方法。
Seismic data quality is vital to geophysical applications, so methods of data recovery, including denoising and interpolation, are common initial steps in the seismic data processing flow. We present a method to perform simultaneous interpolation and denoising, which is based on double-sparsity dictionary learning. This extends previous work that was for denoising only. The original double sparsity dictionary learning algorithm is modified to track the traces with missing data by defining a masking operator that is integrated into the sparse representation of the dictionary. A weighted low-ran…
The inverse problem in Seismology is tackled in this paper under three particular circumstances. First, the inverse problem is defined as the determination of the seismic-moment tensor from the far-field seismic waves (P and S waves). We use the analytical expression of the seismic waves in a homogeneous isotropic body with a seismic-moment source of tensorial forces, the source being localized both in space and time. The far-field waves provide three equations for the sixth unknown parameters of the general tensor of the seismic moment. Second, the Kostrov vectorial (dyadic) representation of…
Harbin Institute of Technology（哈尔滨工业大学）；University of California（加利福尼亚大学）
We propose a convolutional neural network (CNN) denoising based method for seismic data interpolation. It provides a simple and efficient way to break though the lack problem of geophysical training labels that are often required by deep learning methods. The new method consists of two steps: (1) Train a set of CNN denoisers from natural image clean-noisy pairs to learn denoising; (2) Integrate the trained CNN denoisers into project onto convex set (POCS) framework to perform seismic data interpolation. The method alleviates the demanding of seismic big data with similar features as applicatio…
We demonstrate the efficacy of a Bayesian statistical inversion framework for reconstructing the likely characteristics of large pre-instrumentation earthquakes from historical records of tsunami observations. Our framework is designed and implemented for the estimation of the location and magnitude of seismic events from anecdotal accounts of tsunamis including shoreline wave arrival times, heights, and inundation lengths over a variety of spatially separated observation locations. As an initial test case we use our framework to reconstruct the great 1852 earthquake and tsunami of eastern Ind…
Seismic facies identification plays a significant role in reservoir characterization. It helps in identifying the various lithological and stratigraphical changes in reservoir properties. With the increase in the size of seismic data or number of attributes to be analyzed, the manual process for facies identification becomes complicated and time-consuming. Even though seismic attributes add multiple dimensions to the data, their role in reservoir characterization is very crucial. There exist different linear transformation methods that use seismic attributes for identification, characterizatio…
Travel time tomography is used to infer the underlying three-dimensional wavespeed structure of the Earth by fitting seismic travel time data collected at surface stations. Data interpolation and denoising techniques are important pre-processing steps that use prior knowledge about the data, including parsimony in the frequency and wavelet domains, low-rank structure of matricizations, and local smoothness. We show how local smoothness structure can be combined with low rank constraints using level-set optimization formulations, and develop a new relaxation algorithm that can efficiently solve…
Reconstructing Historical Climate Fields With Deep Learning
Department of Mathematics and Statistics, Faculty of Science and Technology, UiT - The Arctic University of Norway（乌伊特-北极大学数学与统计学系）；Physics of Ice, Climate and Earth, Niels Bohr Institute, University of Copenhagen（哥本哈根大学冰川、气候与地球物理研究所）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Earth System Modelling, School of Engineering, Design, Technical University of Munich（慕尼黑技术大学工程与设计学院）；Department of Mathematics and Global Systems Institute, University of Exeter（埃克塞特大学数学与全球系统研究所）
Automatic detection of low-magnitude earthquakes has become an increasingly important research topic in recent years due to a sharp increase in induced seismicity around the globe. The detection of low-magnitude seismic events is essential for microseismic monitoring of hydraulic fracturing, carbon capture and storage, and geothermal operations for hazard detection and mitigation. Moreover, the detection of micro-earthquakes is crucial to understand the underlying mechanisms of larger earthquakes. Various algorithms, including deep learning methods, have been proposed over the years to detect …
Seismic Traveltime Tomography with Label-free Learning
College of Big Data and Software Engineering, Zhejiang Wanli University（浙江万里学院大数据与软件工程学院）；Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University（浙江大学地球科学学院浙江省地球科学大数据与深部资源重点实验室）
Fast Fourier Convolutional GAN for 30 m Clear-Sky Land Surface Temperature Gap-Free Reconstruction
Technical University of Munich（慕尼黑工业大学）；Helmholtz Centre for Polar and Marine Research, Alfred Wegener Institute（亥姆霍兹极地与海洋研究中心阿尔弗雷德·韦格纳研究所）；Swiss Federal Institute of Technology Zurich (ETH Zurich)（瑞士联邦理工学院苏黎世分校）
