Dawei Liu

Purdue University West Lafayette | Purdue · Department of Earth and Atmospheric Sciences

Due to challenging field operations and resource constraints, seismic data acquisition often requires coping with missing traces. Interpolation algorithms are crucial for reconstructing these missing traces to enable improved subsurface analysis and interpretation. While deep learning has made exciting advances in seismic reconstruction, its focus...

Seismic data reconstruction in five dimensions (5D) has become a central focus in seismic data processing, addressing challenges posed by irregular sampling due to physical and budgetary constraints. Most traditional high-dimensional reconstruction methods commonly utilize the fast Fourier transform (FFT), requiring regular grids and preliminary 4D...

Seismic data denoising is a critical component of seismic data processing, yet effectively removing erratic noise, characterized by its non-Gaussian distribution and high amplitude, remains a substantial challenge for conventional methods and deep learning (DL) algorithms. Supervised learning frameworks typically outperform others, but they require...

Seismic data processing, specifically tasks like denoising and interpolation, often hinges on sparse solutions of linear systems. Group sparsity plays an essential role in this context by enhancing sparse inversion. It introduces more refined constraints, which preserve the inherent relationships within seismic data. To this end, we propose a robus...

Ground-penetrating radar (GPR) is a pivotal noninvasive tool that yields subsurface images critical to archaeology, near-surface characterization, geotechnical studies, and disaster response. The antenna central frequency of the GPR system has a significant impact on penetration depth and resolution. Lower antenna frequencies penetrate deeper but a...

During the seismic acquisition, the received seismic data typically comprises many components, such as effective reflections and various interferences. Some components, such as industrial electrical interference and traffic vibrations, manifests as the equidistant narrow-band discrete spectra (ENBD-spectra) in the frequency domain. Morphological co...

How to represent a seismic wavefield? Traditionally, while seismic wavefields are conceptualized continuously, acquisition geometries capture seismic data discretely using 2D spatial coordinates. Motivated by recent advances in neural radiance fields for 3D reconstruction through implicit neural representation, we introduce Implicit Seismic Represe...

The moving high-speed train (HST) generates strong and repeatable vibrations in the railway roadbed, resulting in the propagation of complicated seismic waves into the subsurface medium. Therefore, the moving HST could be considered as a novel seismic source to detect the subsurface structure near high-speed railways (HSR). An HST consists of sever...

In land seismic acquisition, the quality of common-shot gathers is severely degraded by Wind Turbine Noise (WTN) when wind turbines are operating continuously in surveys. The high-amplitude WTN overlap or even completely submerge the body and surface waves (signals). Through time-space and frequency analysis, three main features of the WTN are obse...

Seismic vertical resolution is critical for accurately identifying subsurface structures and reservoir properties. Improving the vertical resolution of vintage seismic data with strongly supervised deep learning is challenging due to scarce or costly labels. To remedy the label-lacking problem, we propose a weakly supervised deep learning method to...

With the improvement of computing power and the rapid development of deep learning, deep-learning-based methods are widely used in the field of seismic data noise suppression. Supervised learning has proven to be effective but its performance largely relies on noise-free data labeling, which is often unavailable or an expensive process. Therefore,...

The attenuation of coherent noise in land seismic data, specifically ground roll and near-surface scattered energy, remains a longstanding challenge. Although recent advances in deep learning have improved signal separation from coherent noise, supervised methods are limited by the necessity for realistic training samples. To circumvent this issue,...

Revealing hidden reservoirs that are severely shielded by strong background interference (SBI) is critical to subsequent refined interpretation. To enhance the characterization of these reservoirs, current interpretation workflows merge multiple attribute information, necessitating intensive human expertise. As an alternative, we regard SBI suppres...

Recent years have witnessed many practical applications of supervised deep learning in seismic processing. However, a weak generalization behavior prevents widespread implementation on large-scale prestack datasets for coherent noise attenuation. This is particularly true when addressing strong near-surface scattered noise in land seismic data. To...

Investigating coherent noise attenuation is a continuing concern within seismic signal processing. As a common type of linear coherent noise, the multiple reflected refraction (MRR) occurs in seismic records where low-velocity strata overlie high-velocity strata, such as deserts, Loess Plateaus, etc. Due to its high velocity and strong energy, MRR...

Vibroseis acquisition, which uses slip sweep instead of traditional flip-flop acquisition, could significantly reduce cycle time and increase productivity. However, the vibroseis system suffers from harmonically distorted sweeps being used as correlation operators, thus causing sticky harmonic distortions in correlated data that cannot be eliminate...

Deblending can extract good quality seismic data from blended seismic data. Generally, the deblending methods can be categorized as model- and data-driven methods. The model-driven deblending methods usually suffer from a massive computational burden, while the data-driven ones need to implement a forward blending process to construct training samp...

Five-dimensional seismic reconstruction is receiving increasing attention and can be viewed as a tensor completion problem, which involves reconstructing a low-rank tensor from a partially observed tensor. Tensor train (TT) decomposition and tensor ring (TR) decomposition are two powerful tensor networks for solving this problem. However, updating...

Common-reflection-point (CRP) gather is one extensive-used prestack seismic data type. However, CRP suffers more noise than poststack seismic dataset. The events in the CRP gather are always flat, and the effective signals from neighboring traces in the CRP gather have similar forms not only in the time domain but also in the time-frequency domain....

Seismic volumetric dip is a crucial seismic geometric attribute, which can provide useful information for assisting subsequent processing and interpretation. Waveform similarity-scanning-based dip estimation (WSSB) delivers reliable dip estimation, but encounters problems of expensive computation. To improve computing efficiency, we use multi-task...

Acquisition footprint causes serious interference with seismic attribute analysis, which severely hinders accurate reservoir characterization. Therefore, acquisition footprint suppression has become increasingly important in industry and academia. In this work, we assume that the time slice of 3D post-stack migration seismic data mainly comprises t...

The attenuation of coherent noise plays a crucial role in reflection seismology but still poses some technical challenges. The multiple reflection-refraction (MRR) is one of the main coherent noises in land seismic surveys. The Cadzow filter can effectively attenuate incoherent noise. But it struggles in attenuating coherent noise. After keeping re...

Deep-learning-based methods have been successfully applied to seismic data random noise attenuation. Among them, the supervised deep-learning-based methods dominate the unsupervised ones. The supervised methods need accurate noise-free data as training labels. However, the field seismic data cannot meet this requirement. To circumvent it, some rese...

Deep learning has been successfully applied to image denoising. In this study, we take one step forward by using deep learning to suppress random noise in poststack seismic data from the aspects of network architecture and training samples. On the one hand, poststack seismic data denoising mainly aims at 3-D seismic data. We designed an end-to-end...