Quality Control of DAS VSP Data in Desert Environment Using Simulations and Matching Filters.

The unconsolidated near surface and large, daily temperature variations in the desert environment degrade the vertical seismic profiling (VSP) data, posing the need for rigorous quality control. Distributed acoustic sensing (DAS) VSP data are often benchmarked using geophone surveys as a gold standard. This study showcases a new simulation-based way to assess the quality of DAS VSP acquired in the desert without geophone data. The depth uncertainty of the DAS channels in the wellbore is assessed by calibrating against formation depth based on the concept of conservation of the energy flux. Using the 1D velocity model derived from checkshot data, we simulate both DAS and geophone VSP data via an elastic pseudo-spectral finite difference method, and estimate the source and receiver signatures using matching filters. These field geophone data show high amplitude variations between channels that cannot be replicated in the simulation. In contrast, the DAS simulation shows a high visual similarity with the field DAS first arrival waveforms. The simulated source and receiver signatures are visually indistinguishable from the field DAS data in this study. Since under perfect conditions, the receiver signatures should be invariant with depth, we propose a new DAS data quality control metric based on local variations of the receiver signatures which does not require geophone measurements.

Diffusion Model for DAS-VSP Data Denoising.

Distributed acoustic sensing (DAS) has emerged as a transformational technology for seismic data acquisition. However, noise remains a major impediment, necessitating advanced denoising techniques. This study pioneers the application of diffusion models, a type of generative model, for DAS vertical seismic profile (VSP) data denoising. The diffusion network is trained on a new generated synthetic dataset that accommodates variations in the acquisition parameters. The trained model is applied to suppress noise in synthetic and field DAS-VSP data. The results demonstrate the model's effectiveness in removing various noise types with minimal signal leakage, outperforming conventional methods. This research signifies diffusion models' potential for DAS processing.

Quantification of DAS VSP Quality: SNR vs. Log-Based Metrics.

The initial quantification of data quality is an important step in seismic data acquisition design, including the choice of sensing strategy. The signal-to-noise ratio (SNR) often drives the choice of distributed acoustic sensing (DAS) parameters in vertical seismic profiling (VSP). We compare this established approach for data quality assessment with metrics comparing DAS data products to available well logs. First, we create kinematic and dynamic data products derived from original seismic data, such as the interval velocity and amplitude of P-wave arrivals. Next, we quantify the quality of derived data products using well log data by calculating various statistical metrics. Using a large dataset of 220 different VSP experiments with a fixed source location and various DAS acquisition parameters, such as gauge length (GL), conveyance type, and lead-in length, we analyzed the statistical distribution of various metrics. The results indicate the decoupling between seismic-based and log-based metrics as well as between the quality of dynamic and kinematic data-products for the same record. Therefore, we propose using fit-for-purpose metrics to optimize the acquisition cost. In particular, for ray-based tomographic processing, it is sufficient to use traveltime-based metrics. On the other hand, for advanced dynamic analysis, amplitude-based metrics define the quality of final processing products. Hence, it is crucial to use fit-for-purpose metrics to optimize DAS VSP acquisition.