TY - JOUR A1 - Hensch, Martin A1 - Dahm, Torsten A1 - Ritter, Joachim A1 - Heimann, Sebastian A1 - Schmidt, Bernd A1 - Stange, Stefan A1 - Lehmann, Klaus T1 - Deep low-frequency earthquakes reveal ongoing magmatic recharge beneath Laacher See Volcano (Eifel, Germany) JF - Geophysical journal international N2 - The occurrence of deep low-frequency (DLF) microearthquakes beneath volcanoes is commonly attributed to mass transport in the volcanic plumbing system and used to infer feeding channels from and into magma reservoirs. The key question is how magmas migrate from depth to the shallow crust and whether magma reservoirs are currently being recharged. For the first time since the improvement of the local seismic networks in the East Eifel region (Rhineland-Palatinate, Germany), we detect and locate recurrent DLF earthquakes in the lower crust and upper mantle beneath the Laacher See Volcano (LSV), using a joint data set of permanent sensors and a temporary deployment. So far, eight DLF earthquake sequences were observed in four distinct clusters between 10 and 40 km depth. These clusters of weak events (M-L< 2) align along an approximately 80. southeast dipping line south of the LSV. Moment tensor solutions of these events have large shear components, and the irregular dispersion and long coda of body waves indicate interaction processes between shear cracks and fluids. We find a rotation of P-axes orientation for shallow tectonic earthquakes compared to DLF events, indicating that the stress field in the depth interval of DLF events might favour a vertical migration of magma or magmatic fluids. The caldera of the LSV was formed by the last major eruption of the East Eifel Volcanic Field only 12.9 kyr ago, fed by a shallow magma chamber at 5-8 km depth and erupting a total magma volume of 6.7 km(3). The observed DLF earthquake activity and continuous volcanic gas emissions around the LSV indicate an active magmatic system, possibly connected with an upper mantle melt zone. KW - Waveform inversion KW - Volcano seismology KW - Magma migration and fragmentation KW - Volcano monitoring Y1 - 2019 U6 - https://doi.org/10.1093/gji/ggy532 SN - 0956-540X SN - 1365-246X VL - 216 IS - 3 SP - 2025 EP - 2036 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Dahm, Torsten A1 - Fischer, Tomas T1 - Velocity ratio variations in the source region of earthquake swarms in NW Bohemia obtained from arrival time double-differences JF - Geophysical journal international N2 - Crustal earthquake swarms are an expression of intensive cracking and rock damaging over periods of days, weeks or month in a small source region in the crust. They are caused by longer lasting stress changes in the source region. Often, the localized stressing of the crust is associated with fluid or gas migration, possibly in combination with pre-existing zones of weaknesses. However, verifying and quantifying localized fluid movement at depth remains difficult since the area affected is small and geophysical prospecting methods often cannot reach the required resolution. We apply a simple and robust method to estimate the velocity ratio between compressional (P) and shear (S) waves (upsilon(P)/upsilon(S)-ratio) in the source region of an earthquake swarm. The upsilon(P)/upsilon(S)-ratio may be unusual small if the swarm is related to gas in a porous or fractured rock. The method uses arrival time difference between P and S waves observed at surface seismic stations, and the associated double differences between pairs of earthquakes. An advantage is that earthquake locations are not required and the method seems lesser dependent on unknown velocity variations in the crust outside the source region. It is, thus, suited for monitoring purposes. Applications comprise three natural, mid-crustal (8-10 km) earthquake swarms between 1997 and 2008 from the NW-Bohemia swarm region. We resolve a strong temporal decrease of upsilon(P)/upsilon(S) before and during the main activity of the swarm, and a recovery of upsilon(P)/upsilon(S) to background levels at the end of the swarms. The anomalies are interpreted in terms of the Biot-Gassman equations, assuming the presence of oversaturated fluids degassing during the beginning phase of the swarm activity. KW - Tomography KW - Earthquake source observations KW - Volcano seismology Y1 - 2014 U6 - https://doi.org/10.1093/gji/ggt410 SN - 0956-540X SN - 1365-246X VL - 196 IS - 2 SP - 957 EP - 970 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Beyreuther, Moritz A1 - Hammer, Conny A1 - Wassermann, Joachim A1 - Ohrnberger, Matthias A1 - Megies, Tobias T1 - Constructing a hidden Markov Model based earthquake detector: application to induced seismicity JF - Geophysical journal international N2 - The triggering or detection of seismic events out of a continuous seismic data stream is one of the key issues of an automatic or semi-automatic seismic monitoring system. In the case of dense networks, either local or global, most of the implemented trigger algorithms are based on a large number of active stations. However, in the case of only few available stations or small events, for example, like in monitoring volcanoes or hydrothermal power plants, common triggers often show high false alarms. In such cases detection algorithms are of interest, which show reasonable performance when operating even on a single station. In this context, we apply Hidden Markov Models (HMM) which are algorithms borrowed from speech recognition. However, many pitfalls need to be avoided to apply speech recognition technology directly to earthquake detection. We show the fit of the model parameters in an innovative way. State clustering is introduced to refine the intrinsically assumed time dependency of the HMMs and we explain the effect coda has on the recognition results. The methodology is then used for the detection of anthropogenicly induced earthquakes for which we demonstrate for a period of 3.9 months of continuous data that the single station HMM earthquake detector can achieve similar detection rates as a common trigger in combination with coincidence sums over two stations. To show the general applicability of state clustering we apply the proposed method also to earthquake classification at Mt. Merapi volcano, Indonesia. KW - Time-series analysis KW - Neural networks KW - fuzzy logic KW - Seismic monitoring and test-ban treaty verification KW - Volcano seismology Y1 - 2012 U6 - https://doi.org/10.1111/j.1365-246X.2012.05361.x SN - 0956-540X VL - 189 IS - 1 SP - 602 EP - 610 PB - Wiley-Blackwell CY - Malden ER -