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Automated location of seismic events is a very important task in microseismic monitoring operations as well for local and regional seismic monitoring. Since microseismic records are generally characterised by low signal-to-noise ratio, such methods are requested to be noise robust and sufficiently accurate. Most of the standard automated location routines are based on the automated picking, identification and association of the first arrivals of P and S waves and on the minimization of the residuals between theoretical and observed arrival times of the considered seismic phases. Although current methods can accurately pick P onsets, the automatic picking of the S onset is still problematic, especially when the P coda overlaps the S wave onset. In this thesis I developed a picking free automated method based on the Short-Term-Average/Long-Term-Average (STA/LTA) traces at different stations as observed data. I used the STA/LTA of several characteristic functions in order to increase the sensitiveness to the P wave and the S waves. For the P phases we use the STA/LTA traces of the vertical energy function, while for the S phases, we use the STA/LTA traces of the horizontal energy trace and then a more optimized characteristic function which is obtained using the principal component analysis technique. The orientation of the horizontal components can be retrieved by robust and linear approach of waveform comparison between stations within a network using seismic sources outside the network (chapter 2). To locate the seismic event, we scan the space of possible hypocentral locations and origin times, and stack the STA/LTA traces along the theoretical arrival time surface for both P and S phases. Iterating this procedure on a three-dimensional grid we retrieve a multidimensional matrix whose absolute maximum corresponds to the spatial and temporal coordinates of the seismic event. Location uncertainties are then estimated by perturbing the STA/LTA parameters (i.e the length of both long and short time windows) and relocating each event several times. In order to test the location method I firstly applied it to a set of 200 synthetic events. Then we applied it to two different real datasets. A first one related to mining induced microseismicity in a coal mine in the northern Germany (chapter 3). In this case we successfully located 391 microseismic event with magnitude range between 0.5 and 2.0 Ml. To further validate the location method I compared the retrieved locations with those obtained by manual picking procedure. The second dataset consist in a pilot application performed in the Campania-Lucania region (southern Italy) using a 33 stations seismic network (Irpinia Seismic Network) with an aperture of about 150 km (chapter 4). We located 196 crustal earthquakes (depth < 20 km) with magnitude range 1.1 < Ml < 2.7. A subset of these locations were compared with accurate locations retrieved by a manual location procedure based on the use of a double difference technique. In both cases results indicate good agreement with manual locations. Moreover, the waveform stacking location method results noise robust and performs better than classical location methods based on the automatic picking of the P and S waves first arrivals.
Earthquakes occurring close to hydrocarbon fields under production are often under critical view of being induced or triggered. However, clear and testable rules to discriminate the different events have rarely been developed and tested. The unresolved scientific problem may lead to lengthy public disputes with unpredictable impact on the local acceptance of the exploitation and field operations. We propose a quantitative approach to discriminate induced, triggered, and natural earthquakes, which is based on testable input parameters. Maxima of occurrence probabilities are compared for the cases under question, and a single probability of being triggered or induced is reported. The uncertainties of earthquake location and other input parameters are considered in terms of the integration over probability density functions. The probability that events have been human triggered/induced is derived from the modeling of Coulomb stress changes and a rate and state-dependent seismicity model. In our case a 3-D boundary element method has been adapted for the nuclei of strain approach to estimate the stress changes outside the reservoir, which are related to pore pressure changes in the field formation. The predicted rate of natural earthquakes is either derived from the background seismicity or, in case of rare events, from an estimate of the tectonic stress rate. Instrumentally derived seismological information on the event location, source mechanism, and the size of the rupture plane is of advantage for the method. If the rupture plane has been estimated, the discrimination between induced or only triggered events is theoretically possible if probability functions are convolved with a rupture fault filter. We apply the approach to three recent main shock events: (1) the M-w 4.3 Ekofisk 2001, North Sea, earthquake close to the Ekofisk oil field; (2) the M-w 4.4 Rotenburg 2004, Northern Germany, earthquake in the vicinity of the Sohlingen gas field; and (3) the M-w 6.1 Emilia 2012, Northern Italy, earthquake in the vicinity of a hydrocarbon reservoir. The three test cases cover the complete range of possible causes: clearly human induced, not even human triggered, and a third case in between both extremes.
Preparatory mechanisms accompanying or leading to nucleation of larger earthquakes have been observed at both laboratory and field scales, but conditions favoring the occurrence of observable preparatory processes are still largely unknown. In particular, it remains a matter of debate why some earthquakes occur spontaneously without noticeable precursors as opposed to events that are preceded by an extended failure process. In this study, we have generated new high-resolution seismicity catalogs framing the occurrence of 20 M-L > 2.5 earthquakes at The Geysers geothermal field in California. To this end, a seismicity catalog of the 11 days framing each large event was created. We selected 20 sequences sampling different hypocentral depths and hydraulic conditions within the field. Seismic activity and magnitude frequency distributions displayed by the different earthquake sequences are correlated with their location within the reservoir. Sequences located in the northwestern part of the reservoir show overall increased seismic activity and low b values, while the southeastern part is dominated by decreased seismic activity and higher b values. Periods of high injection coincide with high b values and vice versa. These observations potentially reflect varying differential and mean stresses and damage of the reservoir rocks across the field. About 50% of analyzed sequences exhibit no change in seismicity rate in response to the large main event. However, we find complex waveforms at the onset of the main earthquake, suggesting that small ruptures spontaneously grow into or trigger larger events.