• Distributed Acoustic sensing
• earthquake detection
• microseismicity

Distributed Acoustic Sensing (DAS) is becoming increasingly popular in microseismic monitoring operations. Fiber-optic cables such as conventional telecommunication or built-for-purpose cables can be turned into a dense array of geophones that samples seismic wavefields continuously for several kilometers. DAS is particularly interesting for microseismic monitoring of geothermal systems since it does not have the same temperature limitations as standard electronic equipment. The sensing fiber can therefore be installed at high-temperature reservoir conditions and in the same well that is being stimulated. Because of these advantages, the distance between the detecting sensor and the induced seismicity can be minimized, maximizing the detection capability. Typical DAS acquisition samples the wavefield at about 1 m spacing and sampling frequencies of 1 kHz or higher. Unfortunately, standard seismological techniques are not capable of exploiting this high spatial density of sensors, hence they are ineffective in processing this kind of data. Here we propose a semblance-based seismic event detection method that fully exploits the characteristics of the DAS data. The detector identifies seismic events by looking at waveform coherence along hyperbolas while changing the curvature and position of the vertex. The method returns a time series of coherence values and, if these values are higher than a determined threshold, it catches a seismic event.
We first evaluate the performance of a DAS-based microseismic monitoring system by comparing the DAS array with a 12 geophone array co-located in a borehole installation at the FORGE geothermal experiment site in Utah, USA. We first compared the noise levels of the two acquisition systems with different microseismic events, more specifically we carried out a spectral analysis of the DAS dataset to characterize its signal-to-noise-ratio (SNR) properties both frequency and time domains. Then we performed a similarity analysis by cross-correlating each geophone (vertical channel) to its closest DAS sensing point along the fiber.
Afterwards, we defined an automated denoising workflow which consists of the removal of the linear trend and mean of each single trace in the array, a bandpass (BP) filter between 10-300 Hz as the events of interest are contained in this frequency range. Next, we performed a Frequency-Wavenumber (FK) filter to remove the coherent noise along the k-zero present in the data as horizontal coherent noise.
Due to the radiation pattern of the earthquake sources, using the semblance directly with raw or filtered seismic traces may lead to spurious results. More specifically we tackled the theoretical problem of using the semblance function to measure the waveform coherency of wavefields whose source radiation pattern is not isotropic (e.g. explosive) but depends on the earthquake fault direction. Potential radiation pattern effects are present when the fault strike direction crosses the array, causing some polarities to be reversed and this attenuates stacked traces.
For this reason, we tested different stacking functions ranging from the envelope, Short-Time-Average Long-Time-Average ratio (STA/LTA) and its derivative. While the STALTA removes the radiation pattern effect, it has the limitation of transforming the data into only positive values, which reduces the capability of the semblance to attenuate random noise. We found that thet derivative of the STALTA applied on the denoised data have the potential to both resolve the problem of the non-isotropic radiation pattern of earthquake sources and improve the detection capability of the algorithm. In this way, random noise is successfully attenuated and the energy signal is well stacked along hyperbolic trajectories.
We find that the algorithm successfully detects all the visible (over the SNR) events in the catalog and therefore we confirm its feasibility to become a real-time microseismic event detector, that can be used for monitoring of geothermal energy production activities. Further research will be conducted towards improving the speed of the algorithm to reach real-time processing requirements. This work is supported by the EU-Geothermica DEEP project.