Acquisition and near-surface impacts on VSP mini-batch FWI and RTM imaging in desert environment

Abstract

The SEG Advanced Modeling (SEAM) Arid benchmark model was designed to simulate an extremely heterogeneous low-velocity near surface (NS), which is typical of desert environments and typically not well characterized or imaged. Imaging of land seismic data is highly sensitive to errors in the NS velocity model. Vertical seismic profiling (VSP) partly alleviates the impact of the NS as the receivers are located at depth in the borehole. Deep learning (DL) offers a flexible optimization framework for full-waveform inversion (FWI), often outperforming typically used optimization methods. We investigate the quality of images that can be obtained from SEAM Arid VSP data by acoustic mini-batch reverse time migration (RTM) and full-waveform imaging. First, we focus on the effects of seismic vibrator and receiver array positioning and imperfect knowledge of the NS model when inverting 2D acoustic data. FWI imaging expectedly and consistently outperforms RTM in our tests. We find that the acquisition density is critical for RTM imaging and less so for FWI, while NS model accuracy is critical for FWI and has less effect on RTM imaging. Distributed acoustic sensing along the full length of the well provides noticeable improvement over a limited aperture array of geophones in imaging deep targets in both RTM and FWI imaging scenarios. Finally, we compare DL-based FWI imaging with inverse scattering RTM using the upgoing wavefield from the original SEAM data. Use of significantly more realistic 3D elastic physics for the simulated data generation and simple 2D acoustic inversion engine makes our inverse problem more realistic. We observe that FWI imaging in this case produces an image with fewer artifacts.

Diffusion Model for DAS-VSP Data Denoising

“…The velocity model is based on the SEAM Arid model [39]. The SEAM Arid model is built from the Barrett model designed to represent unconventional reservoirs in Texas and near-surface model generated to represent land data challenges typically faced in exploration in the Middle East; it has been used extensively for studies on deep learningbased inversion of seismic data [40], acquisition design for VSP [41] and surface seismic [42], and evaluation of structural uncertainty in challenging desert environments [43]. We created 135 shot gathers in the size of 2000 (samples) by 1098 (traces) with different offsets ranging from 0 to 3.5 km by moving the shot location from left to right in the model in Figure 3 with increments of 50 m.…”

“…DAS spans the whole well length Figure3. SEAM Arid slice used for data generation same as[41]. Red triangles at the top of the model mark positions of the sources used for dataset generation.…”