Microseismic monitoring is a valuable tool in understanding the efficacy of hydraulic fracture treatments. Determination of event locations and magnitudes leads to estimations of the geometry of the fracture zone and certain dynamics of the fracturing process. With sufficient resolution, the hypocenters may even reveal failure planes or other underlying structures controlling the distribution of events and of interest to petroleum engineers to test various hypotheses on fracture growth.

Summary We derive a 3D Fréchet sensitivity kernel relating the rms amplitude of a far‐field, broad‐band body‐wave pulse to laterally heterogeneous seismic slowness variations within the earth. Unlike the ‘banana–doughnut’ sensitivity kernel for a cross‐correlation traveltime, the amplitude Fréchet kernel for a turning wave is maximally sensitive, rather than completely insensitive, to the 3D slowness perturbation along the central source‐to‐receiver ray. In the asymptotic limit of an infinite‐frequency pulse, our 3D amplitude kernel formulation is consistent with the dominant 1D integral involving the cross‐path curvature of the slowness perturbation along the unperturbed geometrical ray.

The use of ambient seismic noise has been intensively investigated to perform passive tomography at various scales. Besides passive tomography, passive monitoring is another application of seismic noise correlation as was shown by the recent observation of postseismic velocity changes around the San Andreas Fault in Parkfield, California. One of the drawbacks of using ambient noise correlation for passive monitoring is the need to average the correlations over a long time period in order to obtain a sufficient signal‐to‐noise ratio (SNR) for the phase fluctuations to be measured accurately. For the application to passive monitoring, one wants the possibility of following short‐term velocity variations (1 day or less) using noise correlation functions calculated on short time windows. Another difficulty may then appear when the spatial distribution of noise sources also evolves with time. The aim of this paper is to introduce an adaptive filter to the Parkfield data set in order to improve the SNR output of the ambient noise correlation functions. When applied to passive monitoring, the temporal resolution can be increased from 30 days up to 1 day. With this improved temporal resolution, the velocity drop observed at Parkfield is shown to be cosesimic with the 24 September 2004 Mw = 6.0 event. The relationship between the measured velocity fluctuations and the time evolution of the spatial distribution of the noise wavefield is also investigated. Finally, the error bar in the amplitudes of the velocity variations is compared with a theoretical expectation.