Moment tensor (MT) inversion studies of events in The Geysers geothermal field mostly focused on microseismicity and found a large number of earthquakes with significant non‐double‐couple (non‐DC) seismic radiation. Here we concentrate on the largest events in the area in recent years using a hierarchical Bayesian MT inversion. Initially, we show that the non‐DC components of the MT can be reliably retrieved using regional waveform data from a small number of stations. Subsequently, we present results for a number of events and show that accounting for noise correlations can lead to retrieval of a lower isotropic (ISO) component and significantly different focal mechanisms. We compute the Bayesian evidence to compare solutions obtained with different assumptions of the noise covariance matrix. Although a diagonal covariance matrix produces a better waveform fit, inversions that account for noise correlations via an empirically estimated noise covariance matrix account for interdependences of data errors and are preferred from a Bayesian point of view. This implies that improper treatment of data noise in waveform inversions can result in fitting the noise and misinterpreting the non‐DC components. Finally, one of the analyzed events is characterized as predominantly DC, while the others still have significant non‐DC components, probably as a result of crack opening, which is a reasonable hypothesis for The Geysers geothermal field geological setting.
Intraplate, or hot-spot, volcanism is typically interpreted as the result of plate motion over a spatially localized region of long-lived deep-mantle upwelling (Morgan, 1971). Arguably the most famous example of this process is the Hawaiian-Emperor Seamount Chain (e.g., Steinberger et al., 2004). Whether Bermuda belongs in this category of an intraplate volcano originating from a deep-mantle plume source remains uncertain. The island of Bermuda, situated atop a roughly 10 6 km 2 bathymetric swell in the Western Atlantic Ocean (Figure 1), lacks an age-progressive seamount chain and present-day active volcanism. Radiometric dating of borehole samples from the island and adjacent swell area revealed that the volcanic pedestal was formed during the Middle Eocene (ca. 48-45 Ma), and reached subaerial extent in the Late Eocene, approximately 40-36 Ma (Vogt & Jung, 2007). The pedestal has remained volcanically dormant since, and is now overlain by a fossiliferous carbonate platform. In spite of these facts, which suggest an isolated volcanic episode leading to Bermuda's formation, the region is included in some plume-hot-spot catalogs (King & Adam, 2014; Sleep, 1990), though it fails to meet others' definitions of a mantle plume (Courtillot et al., 2003) due to its lack of a hotspot track and weak evidence for a significant upper mantle low velocity anomaly from seismic tomography. Additionally, several studies suggest a link between the igneous activity which formed Bermuda and other volcanic events, such as the formation of the Mississippi Embayment (Cox & Van Arsdale, 2002; L. Liu et al., 2017) and the reactivation of the New Madrid rift system (Chu et al., 2013), though it may not be linked to all the magmatism along its purported track (Mazza et al., 2014). Recent work in whole mantle seismic tomographic imaging has facilitated the observation and interpretation of several of these deep-mantle plume structures (e.g., French & Romanowicz, 2015), particularly in association with the Pacific and African LLSVPs (Lekić et al., 2012). The presence of a strong, high temperature mantle upwelling should be visible in tomographic images of mantle velocity. To explore this hypothesis in the context of Bermuda, we made cross sections through two seismic tomography models (Figure 2), the joint P and S velocity model LLNL_G3D_JPS (Simmons et al., 2015) and the radially anisotropic S velocity model SEMUCB_WM1 (French & Romanowicz, 2014). Both models display a low velocity anomaly beneath Bermuda relative to the one-dimensional (1-D) average velocity of the profile interrogated. This anomaly extends as a continuous feature to roughly 1,500 km depth, and then is deflected eastward
To better understand earthquakes as a hazard and to better understand the interior structure of the Earth, we often want to measure the physical displacement, velocity, or acceleration at locations on the Earth’s surface. To this end, a routine step in an observational seismology workflow is the removal of the instrument response, required to convert the digital counts recorded by a seismometer to physical displacement, velocity, or acceleration. The conceptual framework, which we briefly review for students and researchers of seismology, is that of the seismometer as a linear time-invariant system, which records a convolution of ground motion via a transfer function that gain scales and phase shifts the incoming signal. In practice, numerous software packages are widely used to undo this convolution via deconvolution of the instrument’s transfer function. Here, to allow the reader to understand this process, we start by taking a step back to fully explore the choices made during this routine step and the reasons for making them. In addition, we introduce open-source routines in Python and MATLAB as part of our rflexa package, which identically reproduce the results of the Seismic Analysis Code, a ubiquitous and trusted reference. The entire workflow is illustrated on data recorded by several instruments on Princeton University campus in Princeton, New Jersey, of the 9 September 2020 magnitude 3.1 earthquake in Marlboro, New Jersey.
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