Real‐time imaging and data analysis for shallow seismic data using a cloud‐computing portal

The small geophone spacing and spread lengths commonly used to investigate ultra-shallow layers in conjunction with the limited bandwidth of surface impact sources do not allow for clear separation of different seismic arrivals. The interference of events and the high noise levels due to near-source offsets make shallow seismic data processing a challenging task. The wavefront attributes of seismic waves commonly used in conventional seismic processing are suggested to improve near-surface seismic reflection imaging. These wavefront attributes are often utilized as the stacking parameters in multidimensional time imaging methods such as common reflection surface (CRS). It is shown in this paper that the CRS method can improve near-surface seismic data processing by enhancing: (1) unstacked gathers via a CRS-based local stacking scheme, (2) semblance picking for velocity model building, and (3) zero-offset stacked data via the CRS global stacking. The CRS-based local stacking can mitigate the loss of data associated with optimum-windowing based muting of coherent noise. The CRS local stacking infills the muted zones via its robust data interpolation and regularization features and enhances the image quality. Furthermore, the CRS-based enhanced data allows for improved near-surface velocity model building by producing higher coherence and more focused semblance peaks. The CRS global stacking is shown to further smooth and provide more continuity of events in the zero-offset data. Applications to a synthetic and two field data sets collected from high and low velocity environments, show the efficiency and feasibility of the proposed approach.

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Common-Reflection-Surface Stack with Global Simultaneous Multi-Parameter Velocity Analysis—A Fit for Shallow Seismics

Heilmann,

Deidda

2024

Applied Sciences

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In many regions, particularly coastal areas, population growth, overuse of water, and climate change have put quality and availability of water under threat. While accurate, predictive groundwater flow models are essential for effective water resource management, the precision of these models relies on the ability to determine the geometries of geological structures and hydrogeologic systems accurately. In complex geological settings or with deep aquifers, the drilling of observation wells becomes too costly and shallow seismic surveys become the method of choice. Common-Reflection-Surface stacking is being used by major oil companies for hydrocarbon exploration but can serve also as an advanced imaging method for near-surface seismic data. Its spatial stacking aperture covers a whole group of neighboring common midpoint gathers and, as such, a multitude of traces contribute to every single stacking process. Since the level of noise suppression is proportional to the number of contributing traces, Common-Reflection-Surface stacking generates a large increase in signal-to-noise ratio. In addition, the data-driven velocity analysis is a statistical process and is, as such, the more stable the more input traces it has. At the beginning, however, the spatial operator was only used for stacking, not for velocity analysis, since limiting computational demand was mandatory to obtain results within a reasonable time frame. Today’s computing facilities are thousands of times faster and even large efficiency gains do not justify the loss of effectiveness anymore that comes with a truncated velocity analysis. We show that this is particularly true for near-surface data with low signal-to-noise ratio and modest common midpoint fold. For the spatial velocity analysis, we present two options: (1) as reference, a global search of all three parameters of the Common-Reflection-Surface operator, and (2) as a quicker solution, a strategy that uses the two-parameter Common-Diffraction-Surface operator to obtain initial values for a local three-parameter optimization. For shallow P-wave data from a hydrogeological survey, we show that the computational cost of option (2) is one order of magnitude smaller than the cost of option (1), while the stack and corresponding normal-moveout velocities are very similar. Comparing the results of the spatial velocity analysis to those of preceding, computationally lighter, strategies, we find a significant improvement, both in stack section resolution and stacking parameter accuracy.

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Real‐time imaging and data analysis for shallow seismic data using a cloud‐computing portal

Cited by 4 publications

References 36 publications

An Efficient Ambient Noise Cross-Correlation Algorithm on Heterogeneous CPU-GPU Cluster

An Efficient Ambient Noise Cross-Correlation Algorithm on Heterogeneous CPU-GPU Cluster

Using common-reflection-surface stack for enhanced near-surface seismic reflection imaging: Examples from consolidated and unconsolidated environments

Common-Reflection-Surface Stack with Global Simultaneous Multi-Parameter Velocity Analysis—A Fit for Shallow Seismics

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