A B S T R A C TWe present the chain of time-reverse modeling, image space wavefield decomposition and several imaging conditions as a migration-like algorithm called time-reverse imaging. The algorithm locates subsurface sources in passive seismic data and diffractors in active data. We use elastic propagators to capitalize on the full waveforms available in multicomponent data, although an acoustic example is presented as well.For the elastic case, we perform wavefield decomposition in the image domain with spatial derivatives to calculate P and S potentials. To locate sources, the time axis is collapsed by extracting the zero-lag of auto and cross-correlations to return images in physical space. The impulse response of the algorithm is very dependent on acquisition geometry and needs to be evaluated with point sources before processing field data. Band-limited data processed with these techniques image the radiation pattern of the source rather than just the location. We present several imaging conditions but we imagine others could be designed to investigate specific hypotheses concerning the nature of the source mechanism. We illustrate the flexible technique with synthetic 2D passive data examples and surface acquisition geometry specifically designed to investigate tremor type signals that are not easily identified or interpreted in the time domain.

We apply seismic interferometry to data from an OBS survey offshore Norway and show that ambient seismic noise can be used to constrain subsurface attenuation on a reservoir scale.By crosscorrelating only a few days of recordings by broad-band ocean bottom seismometers we are able to retrieve empirical Green's Functions (EGFs) associated with surface waves in the frequency range between 0.2 and 0.6 Hz and acoustic waves traveling through the seawater between 1.0 and 2.5 Hz. We show that the decay of theses u r f a c ew a v e sc a n n o t be explained by geometrical spreading alone and requires an additional loss of energy with distance. We quantify this observed attenuation in the frequency domain using a modified 1 Bessel function to describe the cross-spectrum in a stationary field. We average crossspectra of equally spaced station couples and sort these azimuthally averaged cross-spectra with distance. We then obtain frequency-dependent estimates of attenuation by minimizing the misfit of the real parts to a damped Bessel function. The resulting quality factors as function of frequency are indicative of the depth variation of attenuation and correlate with the geology in the survey area.2

We present location results for a group of ∼200 microearthquakes that occurred in 2012 in a region of Oklahoma hosting ongoing exploration activities. Using a local passive surface seismic monitoring network of 15 broadband stations, we applied two modern location techniques that use fundamentally different approaches. The first is a pick-based double-difference relocation method with waveform crosscorrelation. Multipleevent location techniques such as these are generally regarded as the best approach for obtaining high-precision locations from pick data. The second approach is an automated waveform migration stacking method. These types of methods are becoming increasingly common due to increasing network station density and computer power. The results from the two methods show excellent agreement and provide similar results for the interpreter. Both methods reveal spatial and temporal patterns in the locations that are not visible in results obtained using a more traditional pick-based approach. We performed two statistical uncertainty tests to assess the effects of data quality and quantity on the two methods. We show that the uncertainties for both methods are comparable, but that the stack-based locations are less sensitive to station geometry, likely due to the different treatment of outliers and the beneficial inclusion of noisier data. Finally, we discuss the favorable conditions in which to apply each method and argue that for small aperture surface arrays where accurate velocity information exists, such as in this study, the stack-based method is preferable due to the higher degree of automation. Under these conditions, stack-based methods better allow for rapid and precise determination of microearthquake locations, facilitating improved interpretations of seismogenic processes.

Locating subsurface sources from passive seismic recordings is difficult when attempted with data that has no observable arrivals or a low signal-to-noise ratio. Using time-reversal techniques recorded energy can be focused at its source depth. However, when a focus cannot be matched to a particular event, it can be difficult to distinguish true focusing from artifacts. Artificial focusing could arise from numerous causes, including surface waves, local noise sources, acquisition geometry and velocity model effects. We present a method to more reliably locate subsurface sources that reduces the ambiguity of the results. Time-reverse imaging techniques are implemented on both the recorded data and a noise model. In the data domain, the noise model only approximates the energy of local noise sources. After imaging, however, the result also captures the effects of acquisition geometry and the velocity model. The noise image is then used to correct the data image to produce an estimate of the signal-to-noise ratio. Synthetic data examples show the versatility of this technique to varying amounts of noise and to challenging velocity models. A field data example shows how this technique can be used to locate the source of low-frequency energy collocated with an oil reservoir.

No abstract