Elastic Velocity Updating through Image-Domain Tomographic Inversion of Passive Seismic Data

Abstract:

Seismic monitoring at injection sites (e.g., CO₂sequestration, waste water disposal, hydraulic fracturing) has become an increasingly important tool for hazard identification and avoidance. The information obtained from this data is often limited to seismic event properties (e.g., location, approximate time, moment tensor), the accuracy of which greatly depends on the estimated elastic velocity models. However, creating accurate velocity models from passive array data remains a challenging problem. Common techniques rely on picking arrivals or matching waveforms requiring high signal-to-noise data that is often not available for the magnitude earthquakes observed over injection sites.

We present a new method for obtaining elastic velocity information from earthquakes though full-wavefield wave-equation imaging and adjoint-state tomography. The technique exploits images of the earthquake source using various imaging conditions based upon the P- and S-wavefield data. We generate image volumes by back propagating data through initial models and then applying a correlation-based imaging condition. We use the P-wavefield autocorrelation, S-wavefield autocorrelation, and P-S wavefield cross-correlation images. Inconsistencies in the images form the residuals, which are used to update the P- and S-wave velocity models through adjoint-state tomography.

Because the image volumes are constructed from all trace data, the signal-to-noise in this space is increased when compared to the individual traces. Moreover, it eliminates the need for picking and does not require any estimation of the source location and timing. Initial tests show that with reasonable source distribution and acquisition array, velocity anomalies can be recovered. Future tests will apply this methodology to other scales from laboratory to global.