Using Waveform Amplitude Ratios

A new method of clustering seismic events has been developed to identify features within a large cloud of microearthquakes induced by the pressurization of a 3.5-km deep, jointed region of granitic rock. The relative amplitudes of shearand compressional-waves were taken as an expression of the source mechanism for each microearthquake. Then, the events were clustered according to their similarity in S/P ratio and a second criterion, their spatial proximity to each other. Thus, each cluster contains events with closely spaced hypocenters and similar S/P ratios, and therefore a high probability of the same (or very similar) focal mechanism. This method was applied to a data set of 8,000 microearthquakes recorded during hydraulic stimulation of the Hot Dry Rock geothermal reservoir at Fenton Hill, New Mexico, located on the western flank of the Valles Caldera. Over 50 clusters of more than 10 events each were identified as planar features and over 20 such clusters were identified as linear features, together containing 15% of the usable events. Two predominant sets of planar features strike north to N45°W, roughly tangent to the ring fault system circling the caldera and dip 60° in either direction. These planes represent joints that may be associated with stress fields accompanying caldera formation. Most observed planes contain significant resolved shear within the current stress field. However, wide ranges of equally probable orientations are not observed, indicating a relatively unbiased sampling of the actual range of joint orientations. Most linear features are oriented parallel to one or both sets of planar features 3 and may be associated with joint intersections or terminations. These results support a reservoir flow model composed of water storage within aseismic joints oriented perpendicular to the minimum stress direction and of limited dimension, connected by high impedance paths along joints striking north to N45°W as defined by the cluster results. 4 I n t r o d u c t i o n Microearthquakes recorded in seismically active regions can provide useful information about subsurface structures. However, it is often difficult to glean much structural detail from a plot of the hypocenters alone. Scatter in the hypocenter locations obscures features that are close together, and visualization of threedimensional features in a two-dimensional scatter plot is often difficult. Even with perfect hypocenter locations, it would still be difficult to identify features in a cloud of many thousands of microearthquakes. Within the last eight years, several techniques have been developed to discern structural details within such clouds of microseismic events. One recent method employs an iterative technique of moving event hypocenters around within their error ellipsoids in an attempt to concentrate, or focus, diffuse seismicity [1]. The 3-point method of Fehler [2] looks for planar features in an event cloud by taking all combinations of events three at a time to find all possible orientations of planes within the data set. A statistical analysis then identifies the most significant planar orientations given that some orientations will merely be artifacts of the overall shape of the seismic cloud. These methods [1,2] rely solely on the locations of the event hypocenters to define features in the event cloud. Modified versions of the 3-point method [3,4] use fault-plane solution data along with identified plane orientations of aftershocks to identify which of two nodal planes was the slip plane 5 of a main shock. This is a useful advance as both structure and mechanism can be inferred from the results. An alternative approach to these methods is to first collect groups of events with similar focal mechanisms, and then look for patterns defined by their hypocenters. One method of identifying similar events is through waveform cross-correlation across all possible event pairs in the data set, so that events with high correlation can be grouped together. An advantage to this approach is that event timing and location can be improved in parallel. However, the cross-correlation and timing improvement process can be computationally intensive for large data sets [5]. The method we have developed uses the ratio of the firstarrival Sto P-wave amplitudes (S/P) at a given station as an expression of the earthquake focal mechanism. Using this less sophisticated measure of similarity between microearthquakes to selectively cluster events, we were able to identify numerous features within the microseismic cloud with much less coding and computational time than would have been required using waveform cross-correlation techniques. In this paper we describe the application of this new method of analysis to a large data set of microearthquakes that were induced during the 1983 massive hydraulic fracturing (MHF) test performed at the Los Alamos National Laboratory's Fenton Hill Hot Dry Rock (HDR) test site in north-central New Mexico (Figure 1). In this 6 experiment, 21,600 m3 of water was injected in 62 hours into a packer-isolated, 21-m section of openhole wellbore at a depth of 3460 m. The mean pumping conditions were a flow rate of 106 l/s at a wellhead pressure of 48 MPa. We begin our discussion with the motivation for this new method of microseismic data analysis and then explain in detail how the method was applied to our particular data set. The results from this analysis consist of a large number of microearthquake clusters that are subsets of the total data set. Many of these clusters delineate planar and linear features that appear to be related to preexisting joints along which slip was induced. We anticipate that knowledge of the joint structure will help us to understand fluid flow through the reservoir.