Seismic coherent noise is often found in post-stack seismic data, which contaminates the resolution and integrity of seismic images. It is difficult to remove the coherent noise since the features of coherent noise, e.g., frequency, is highly related to signals. Recently, deep learning has proven to be uniquely advantageous in image denoise problems. To enhance the quality of the post-stack seismic image, in this letter, we propose a novel deep-residual-learning-based neural network named DR-Unet to efficiently learn the feature of seismic coherent noise. It includes an encoder branch and a decoder branch. Moreover, in order to collect enough training data, we propose a workflow that adds real seismic noise into synthetic seismic data to construct the training data. Experiments show that the proposed method can achieve good denoising results in both synthetic and field seismic data, even better than the traditional method.
Seismologists often need to gather information about the subsurface structure of a location to determine if it is fit to be drilled for oil. However, there may be electrical noise in seismic data which is often removed by disregarding certain portions of the data with the use of a notch filter. Instead, we use a convolutional encoder decoder network to remove such noise by training the network to take the noisy shot record as input and remove the noise from the shot record as output. In this way, we retain important information about the data collected while still removing coherent noise in se…
A Primal-Dual Proximal Algorithm for Sparse Template-Based Adaptive Filtering: Application to Seismic Multiple Removal
IFP Energies nouvelles（IFP能源新动力）；Univ. Paris-Est, LIGM UMR CNRS 8049（巴黎东大学，LIGM UMR CNRS 8049）；Aix-Marseille Univ., I2M UMR CNRS 7373（艾克斯-马赛大学，I2M UMR CNRS 7373）
A general lightweight global modeling framework for three-dimensional seismic exploration
State Key Laboratory of Deep Earth Exploration and Imaging, College of Instrumentation and Electrical Engineering, Jilin University（吉林大学仪器科学与电气工程学院深地探测与成像国家重点实验室）
The concept of residual migration to zero offset is introduced for the case that a prestack migration and a subsequent residual moveout analysis have been performed for a seismic survey and a new depth model has not yet been determined. Travel times of reflected events for individual traces are determined for diffraction points situated along horizons of analysis. These events are downward continued into the model used for the migration of the survey. The NIP wave theorem can then be applied to events exhibiting small subsurface offsets in order to determine residual radii of curvature to be u…
Practical approach to solvability: Geophysical application using complex decomposition into simple part (solvable) and complex part (interpretable) for seismic imaging
Recent progresses in artificial intelligence and machine learning make it possible to automatically identify seismic phases from exponentially growing seismic data. Despite some exciting successes in automatic picking of the first P- and S-wave arrivals, auto-identification of later seismic phases such as the Moho-reflected PmP waves remains a significant challenge in matching the performance of experienced analysts. The main difficulty of machine-identifying PmP waves is that the identifiable PmP waves are rare, making the problem of identifying the PmP waves from a massive seismic database i…
As new techniques exploiting the Earth's ambient seismic noise field are developed and applied, such as for the observation of temporal changes in seismic velocity structure, it is crucial to quantify the precision with which wave-type measurements can be made. This work uses array data at the Homestake mine in Lead, South Dakota and an array at Sweetwater, Texas to consider two aspects that control this precision: the types of seismic wave contributing to the ambient noise field at microseism frequencies and the effect of array geometry. Both are quantified using measurements of wavefield coh…
One of the most crucial tasks in seismic reflection imaging is to identify the salt bodies with high precision. Traditionally, this is accomplished by visually picking the salt/sediment boundaries, which requires a great amount of manual work and may introduce systematic bias. With recent progress of deep learning algorithm and growing computational power, a great deal of efforts have been made to replace human effort with machine power in salt body interpretation. Currently, the method of Convolutional neural networks (CNN) is revolutionizing the computer vision field and has been a hot topic…
PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for Full-Waveform Inversion Without Dataset Dependency
College of Instrumentation and Electrical Engineering, Jilin University（吉林大学仪器科学与电气工程学院）；Northeast Electric Power University（东北电力大学）；College of Communication Engineering, Jilin University（吉林大学通信工程学院）
