Depth Uncertainty and its Impact on Seismic Inversion

Depth uncertainty can have an enormous impact on the quality of seismic inversion, so understanding its causes and how to address them is essential to ensuring accurate results. These insights, shared by Riehle et al., will help you understand what depth uncertainty is and how it affects your seismic interpretation results and subsequent interpretations.

An Introduction to Depth Uncertainty

The precision of an earthquake’s hypocentral location is affected by how deep it occurs. Understanding how depth uncertainty impacts each stage of seismic inversion (stage: calculation, observation, modelling) is critical to assessing depth uncertainty; learning more about depth uncertainty is important because of it can be used for a better estimation of seismic inversions quality. These stages include computing internal model parameters; normalizing gathers into synthetic seismograms; and inverting for structure-factor models. It is crucial that we understand these three steps so that we can properly account for depth uncertainty when making decisions regarding seismic interpretation. Depth Uncertainty and Its Impact on Seismic Inversion discusses these three steps with respect to their impact on depth uncertainty. It also examines how we should account for them during seismic inversions analysis. This paper also provides examples of how depth uncertainty may affect each step within these three stages.

How can we model it?

Two ways to model uncertainty in depth conversion methods: statistical and geophysical. Statistical models are based off what we can find in available databases about how seismic waves travel through earth; these models usually rely on having an existing model of a structure. Geophysical models take into account how seismic waves interact with different types of rock by calibrating these interactions with physical measurements. They also typically use a single known structure as a calibration point, or baseline, for each region they analyse.

The effect of depth uncertainty on the inverse problem

Let’s take a moment to step back from seismic wave propagation. The seismic inverse problem we have just been discussing can be separated into two parts:

1. depth conversion, which is about estimating crustal velocity structure from observed travel times; and
2. wavefield inversion, which is about predicting surface-wave amplitudes from that same model of crustal structure. Depth conversion addresses depth uncertainty by propagating waves through various models of crustal velocity structure.

How do we reduce them?

Certain anisotropic properties of geologic materials such as porosity and permeability can lead to depth uncertainty in depth conversion, which leads to inconsistent predictions of seismic waves propagating through heterogeneous geologic media. One way to combat depth uncertainty is by calculating appropriate values for density-anisotropy products, referred to as seismic anisotropy tensors. However, due to a lack of empirical information regarding values for these tensors throughout different geological environments, they have traditionally been assigned uniform or constant values.

Conclusion & References

Depth uncertainty, or error, can be defined as a systematic deviation from an expected value of a parameter that is caused by an incomplete knowledge of actual subsurface structure. There are many factors that cause depth uncertainty in seismic inversions. Errors introduced during acquisition and processing are usually much larger than errors due to subsurface uncertainty. Although most focus has been placed on understanding subsurface uncertainties, there have also been advances in improving our ability to predict depth error propagation through process models and better quality control techniques.