Lab

Seismic Imaging - Prof Yike Liu's Lab

About the lab

Our Lab is working on Advanced Seismic Data Processing in Exploration Geophysics related stuff, which includes Full Waveform Inversion, Reverse Time Migration, Phase Encoding Schemes, Multiples Imaging, Fast VSP Processing methods, Sparsity Inversion, Seismic Sequence Stratigraphy, Post Processing of Seismic data as an aid for interpretation etc..

Featured projects (1)

Project
This is my current doctoral degree project, my main focus is on delineation of complete depositional system and subsidence history of a passive margin in Pakistan.

Featured research (5)

Multiples follow long paths and carry more information on the subsurface than primary reflections, making them particularly useful for imaging. However, seismic migration using multiples can generate crosstalk artifacts in the resulting images because multiples of different orders interfere with each others, and crosstalk artifacts greatly degrade the quality of an image. We propose to form a supergather by applying phase-encoding functions to image multiples and stacking several encoded controlled-order multiples. The multiples are separated into different orders using multiple decomposition strategies. The method is referred to as the phase-encoded migration of all-order multiples (PEM). The new migration can be performed by applying only two finite-difference solutions to the wave equation. The solutions include backward-extrapolating the blended virtual receiver data and forward-propagating the summed virtual source data. The proposed approach can significantly attenuate crosstalk artifacts and also significantly reduce computational costs. Numerical examples demonstrate that the PEM can remove relatively strong crosstalk artifacts generated by multiples and is a promising approach for imaging subsurface targets.
Traditional full-waveform inversion (FWI) seeks to find the best model by minimizing an objective function defined as difference between the model-predicted and observed data in both amplitude and phase. In principle, FWI should fit all wave types including direct wave, diving wave, primaries, and multiples. However, when an initial model is far from the true model, FWI will encounter difficulties in matching multiples. Physically, multiples may contain more subsurface information compared to primary and diving waves. Multiples cover a wide range of reflection angles during wave propagation and offer the advantage of imaging the shadow zones that cannot be reached or poorly illuminated by primary reflections. Here, we develop a new method of waveform inversion using multiples. We first separate the multiples into different orders. The objective function we seek to minimize consists of the data difference between the modeled data using a lower-order multiple as the source and the higher-order multiple as data. This method is denoted as controlled-order multiple waveform inversion (CMWI). Our numerical examples demonstrated that the CMWI is a promising method to improve velocity updates.
Traditional iteration-based full waveform inversion (FWI) methods encounter serious challenges if the initial velocity model is far from the true model or the observed data are lacking low-frequency content. As such, the optimization algorithm may be trapped in local minima and fail to go to a global optimal model. In addition, the traditional FWI method requires long-offset data to update the deep structure of a velocity model with diving waves. To overcome traditional FWI’s disadvantages under these circumstances, we propose a reflection intensity waveform inversion (RIWI) method. This method aims to minimize the seismic intensity differences between modeled reflection data and field data. The proposed method is less dependent on the starting model and long-offset data are no longer required. The wave intensity, is proportional to the square of original data amplitude, can have both a low-frequency band and a higher frequency band, even for waveforms without initial low-frequency contents. Our multi-scale intensity inversion starts from the low-frequency information in the intensity data and it can largely avoid the cycle-skipping problem. Synthetic and field data examples demonstrate that the proposed method is able to overcome cycle-skipping in handling data with no low-frequency information.

Lab head

Yike Liu
Department
  • Institue of Geology and Geophysics

Members (9)

Majid Khan
  • University of Science and Technology Beijing
Xuejian Liu
  • Pennsylvania State University
Bin He
  • University of Toronto
Huiyi lu
  • Chinese Academy of Sciences
Yanbao Zhang
  • Institute of Geophysics, China Earthquake Administration
Lanshu Bai
  • Institute of Geophysics, China Earthquake Administration
Jia Yi
  • Institute of Geology and Geophysics, Chinese Academy of Sciences
Yan Tianfan
  • Chinese Academy of Sciences