No conclusive evidence has been presented to date for tectonic tremor (TT) in the vicinity of central Chile, where the Nazca Plate is subducting beneath the South American Plate. Subduction in our experimental location (roughly 35.5° S, 70.5° W) is steep and fairly unobstructed compared to the flattened and more seismogenic behavior to the north. We seek to identify TT in our experimental area, whose geodynamics are comparable to tremor-rich subduction zones such as Cascadia and the Nankai Trough. Our method combines time-series visual inspection, frequency-spectrum analysis, waveform cross-correlation, and 3-component (3C) signal covariance to explore the presence of TT in this region. We have identified TT using stations in central Chile and the Malargüe region, Argentina. The TT exhibits similar features to other TT observations worldwide. Waveform characteristics for the TT in our study, particularly dimension of the 3C signal covariance, vary as a function of apparent source location. The duration of one episode of identified TT was about 10 h, which may indicate that the plate interface where tremor generates is strongly coupled. We conclude that our observations reflect features of the local propagation, rather than the tremor source itself.@en

We applied the seismic interferometry technique to characterize the subsurface velocities of the Planchón-Peteroa Volcanic Complex, Argentina-Chile, down to a depth of about 350 m. Ambient seismic noise data were recorded by an array of six stations deployed in the eastern flank of the current active volcano of this volcanic complex—the Peteroa. To ensure retrieval of accurate surface-wave Green’s functions, we analyzed the directivity of the recorded ambient noise and then selected the noise windows containing source directions in line with the stationary-phase area for each station pair. Then, we obtained dispersion curves and further utilized them for the estimation of the S-wave velocity profile for the area enclosed by the stations. We inferred two layers above the investigation depth limit. In the first 70 m, a lowvelocity layer (300–400 m=s) is present, which is followed by a higher velocity layer (450–570 m=s) down to, at minimum, a depth of about 350 m. Higher velocities are observed at the northeast and the very southwest of the area under investigation, and lower velocities are observed between these areas. The S-wave velocity structure is consistent with the known near-surface lithologies of the area. The results along the western side of the area corroborate previous results obtained from geochemical studies. Velocity variations in the area are potentially caused by changes in lithology, porosity, and water saturation. This work contributes to the understanding of the subsurface of Peteroa volcano and provides useful information to the authorities for decision- making. Furthermore, these results are expected to be used by studies preceding risk analysis and hazard-assessment investigations. @en

The reflection seismic method is the most frequently used exploration method for imaging and monitoring subsurface structures with high resolution. It has proven its qualities from the scale of regional seismology to the scale of near-surface applications that look just a few meters below the surface. The reflection method uses controlled active sources at known positions to give rise to reflections recorded at known receiver positions. The reflections’ two-wave travel time is used to extract desired information about and image the subsurface structures. When active sources are unavailable or undesired, one can retrieve body-wave reflections from application of seismic interferometry (SI) to sources of opportunity—quakes, tremors, ambient noise, or even man-made sources not connected to the exploration campaign. We show examples of imaging of subsurface structures using reflections retrieved from quakes and ambient noise. We apply SI by autocorrelation to global earthquake to image seismic and aseismic parts of the Nazca plate and the Moho at these places, SI by multidimensional deconvolution to P-wave coda from local earthquakes to image the Moho and the crust at the same places, and SI by autocorrelation to deep moonquakes to image the lunar Moho and to ambient noise to monitor CO2 sequestration. @en

Obtaining new seismic responses from existing recordings is generally referred to as seismic interferometry (SI). Conventionally, the SI responses are retrieved by simple crosscorrelation of recordings made by separate receivers: one of the receivers acts as a ‘virtual source’ whose response is retrieved at the other receivers.When SI is applied to recordings of ambient seismic noise, mostly surface waves are retrieved. The newly retrieved surface wave responses can be used to extract receiver-receiver phase velocities. These phase velocities often serve as input parameters for tomographic inverse problems. Another application of SI exploits the tempo- ral stability of the multiply scattered arrivals of the newly retrieved surface wave responses. Temporal variations in the stability and/or arrival time of these multiply scattered arrivals can often be linked to temporally varying parameters such as hydrocarbon production and precip- itation. For all applications, however, the accuracy of the retrieved responses is paramount. Correct response retrieval relies on a uniform illumination of the receivers: irregularities in the illumination pattern degrade the accuracy of the newly retrieved responses. In practice, the illumination pattern is often far from uniform. In that case, simple crosscorrelation of separate receiver recordings only yields an estimate of the actual, correct virtual-source response. Re- formulating the theory underlying SI by crosscorrelation as a multidimensional deconvolution (MDD) process, allows this estimate to be improved. SI by MDD corrects for the non-uniform illumination pattern by means of a so-called point-spread function (PSF), which captures the irregularities in the illumination pattern. Deconvolution by this PSF removes the imprint of the irregularities on the responses obtained through simple crosscorrelation. We apply SI by MDD to surface wave data recorded by theMalargue seismic array in western Argentina. The aperture of the array is approximately 60 km and it is located on a plateau just east of the Andean mountain range. The array has a T-shape: the receivers along one of the two lines act as virtual sources whose responses are recorded by the receivers along the other (perpendicular) line.We select time windows dominated by surface wave noise travelling in a favourable direction, that is, traversing the line of virtual sources before arriving at the receivers at which we aim to retrieve the virtual-source responses. These time windows are selected through a frequency-dependent slowness analysis along the two receiver lines. From the selected time windows, estimates of virtual-source responses are retrieved by means of crosscorrelations. Similarly, crosscorrelations between the positions of the virtual sources are computed to build the PSF. We use the PSF to deconvolve the effect of illumination irregularities and the source function from the virtual-source responses retrieved by crosscorrelation. The combined effect of time-window selection and MDD results in more accurate and temporally stable surface wave responses.@en

Obtaining new seismic responses from existing recordings is generally referred to as seismic interferometry (SI). Conventionally, these seismic interferometric responses are retrieved by simple crosscorrelation of recordings made by separate receivers: a first receiver acts as a 'virtual source' whose response is retrieved at the other receivers. When surface waves are retrieved, the newly retrieved responses can be used to extract receiver-receiver phase velocities. These phase velocities often serve as input parameters for tomographic inverse problems. Another application of SI exploits the temporal stability of the multiply scattered arrivals (the coda). For all applications, however, the accuracy of the retrieved responses is paramount. In practice, this accuracy is often degraded by irregularities in the illumination pattern: correct response retrieval relies on a uniform illumination of the receivers. Reformulating the theory underlying seismic interferometry by crosscorrelation as a multidimensional deconvolution (MDD) process, allows for correction of these non-uniform illumination patterns by means of a so-called point-spread function (PSF). We apply SI by MDD to surface-wave data recorded by the Malargüe seismic array in western Argentina. The aperture of the array is approximately 60 km and it is located on a plateau just east of the Andean mountain range. The array has a T-shape, which makes it very well suited for the application of SI by MDD. We select time windows dominated by surface-wave noise traveling in a favorable direction, that is, traversing the line of virtual sources before arriving at the receivers at which we aim to retrieve the virtual-source responses. These time windows are selected based upon the slownesses along the two receiver lines. From the selected time windows, virtual-source responses are retrieved by computation of ensemble-averaged crosscorrelations. Similarly, ensemble-averaged crosscorrelations between the positions of the virtual sources are computed: the PSF. We use the PSF to deconvolve the effect of illumination irregularities and the source function from the virtual-source responses retrieved by crosscorrelation. The combined effect of time-window selection and MDD results in more accurate and temporally stable surface-wave responses.@en

Generating new seismic responses from existing recordings is generally referred to as seismic interferometry (SI). Conventially, the new responses are retrieved by simple crosscorrelation of recordings made by separate receivers: a first receiver acts as `virtual source' whose response is retrieved at the other receivers. The newly retrieved responses can be used to extract receiver-receiver phase velocities, which often serve as input parameter for tomographic inverse problems, or which can be linked to temporally varying parameters such as hydrocarbon production and precipitation. For all applications, however, the accuracy of the retrieved responses is of great importance. In practice, this accuracy is often degraded by irregularities in the illumination pattern: correct response retrieval relies on a uniform illumination of the receivers. Reformulating the theory underlying seismic interferometry by crosscorrelation as a multidimensional deconvolution (MDD) process, allows for correction of these non-uniform illumination patterns by means of a so-called point-spread function (PSF). We apply SI by MDD to surface-wave data recorded by the Malargüe seismic array in western Argentina. The aperture of the array is approximately 60 km and it is located on a plateau just east of the Andean mountain range. The array has a T-shape: the receivers along one of the two lines act as virtual sources whose responses are retrieved by the receivers along the other (perpendicular) line of receivers. Because SI by MDD relies on one-way wavefields, we select time windows dominated by surface-wave noise traveling in a favorable direction, that is, traversing the line of virtual sources before arriving at the receivers at which we aim to reconstruct the virtual-source responses. These time windows are selected through a frequency-dependent slowness analysis along the two receiver lines. From the selected time windows, virtual-source responses are retrieved by computation of ensemble-averaged crosscorrelations. Similarly, ensemble-averaged crosscorrelations between virtual sources are computed: the point-spread function. We use the PSF to deconvolve the effect of illumination irregularities and the source function from the virtual-source responses. The combined effect of time-window selection and MDD results in more accurate surface-wave responses.@en

Seismic interferometry refers to the principle of generating new responses. These new responses are conventionally obtained by simple crosscorrelation of recordings made by separate receivers: a first receiver acts as ‘virtual source’ whose response is retrieved at the other receivers. The recorded wavefields may be passive (e.g. seismic noise) or active (e.g. in an industrial context). The newly retrieved responses can be used to extract receiver-receiver phase velocities, which often serve as input parameter for tomographic inverse problems. More recently, the coda of the newly retrieved responses have been found to correlate with temporally varying parameters such as hydrocarbon production and precipitation. For all applications, however, the accuracy of the retrieved responses is of great importance. Irregularities in the illumination patttern often degrade this accuracy: correct response retrieval relies on a uniform illumination of the receivers. Reformulating the theory underlying seismic interferometry by crosscorrelation as a multidimensional deconvolution (MDD) process, allows the removal of the imprint of the illumination pattern on the retrieved responses by means of a so-called point-spread function (PSF). We use a seismic array in Malargüe, Argentina, to assess the feasibility of SI by MDD on ambient seismic noise recordings. The array, which has an aperture of approximately 60 km, is located just east of the Andean mountain range. The shape of the array lends itself well for the application of SI by MDD: its T-shape allows the construction of a PSF along one of the two receiver lines. These receivers act as the virtual sources and their responses are retrieved by the receivers along the other (perpendicular) line of receivers. A frequency-dependent analysis of the slowness along both lines allows us to select time windows during which most ambient seismic surface waves propagate in a favorable direction, that is, traversing the line of virtual sources prior to arrival at the receivers at which we aim to reconstruct the responses. During these time windows the wavefield therefore fulfills the assumption underlying SI by MDD that there is only energy propagating from the virtual sources to the receivers. Both the PSF and conventional crosscorrelations between the virtual sources and the receivers are computed from the selected time windows. We find that multidimensionally deconvolving the virtual-source responses by the point-spread function improves the responses’ accuracy. @en

Seismic interferometry (SI) retrieves virtual seismic signals from measurements at two receivers from surrounding sources. Studies have demonstrated that SI can image subsurface reflectivity. Claerbout (1968) showed that the reflection response can be obtained by autocorrelating the transmission response assuming a 1-D acoustic medium. Migration techniques using primary and multiple reflections in the autocorrelogram effectively imaged the subsurface structure (Schuster et al., 2003; Yu et al., 2003). This research aims to contribute to the knowledge of the subsurface structure at Planchón-Peteroa Volcano Complex (PPVC) by using SI. Inspired by the theory and applications in Wapenaar (2003) and Ruigrok and Wapenaar (2012), this work applies SI to fracture seismicity originated at PPVC or in active geologic faults located nearby this volcanic complex. Autocorrelating an extracted time window for selected events, zero-offset reflection responses were retrieved for each station. This response is further used to image shallow subsurface reflectors underneath each station. This application uses seismic data recorded by stations deployed in Argentina and Chile. The Argentine data was recorded by a temporary array of six 2-Hz 3-component stations deployed in 2012 on the eastern flank of the volcano. The Chilean data is provided by OVDAS-SERNAGEOMIN (South Andes Volcanic Observatory, Chile). OVDAS has a permanent array of six 30-seconds 3-component stations. Three of these stations recorded data simultaneously with the Argentine stations and were used for this research. Very local seismicity is required for this study, therefore event selection procedure is of great importance. The two arrays were initially used independently to locate fracture events (Casas, 2014; RAV-SERNAGEOMIN, 2012). In order to obtain accurate locations, the two datasets were used together to relocate the detected events (Casas et al., 2016; Olivera Craig et al., 2016). This preprocessing step enhances the resolution of the subsurface images obtained by application of SI at PPVC since appropriate wave paths are selected for processing @en

Seismic interferometry (SI) studies the interference phenomenon between pairs of signals in order to obtain information from the differences between them. SI is now regularly used in exploration and global seismology with active and/or passive sources, i.e., artificial sources (dynamite, vibroseis, sledge hammer, etc.) or natural sources (earthquakes, anthropogenic noise, ocean microseisms, etc.). SI allows one to extract subsurface information from complicated or random wavefields.This research aims to contribute to the knowledge of the subsurface structure at Planchón-Peteroa Volcano Complex (PPVC) by using SI technique. Inspired by the theory and applications in Wapenaar (2003) and Ruigrok and Wapenaar (2012), this work applies SI to fracture seismicity originated at PPVC or in active geologic faults located nearby this volcanic complex. Applying autocorrelation to a selected time window at each event, zero-offset reflection responses were obtained for each station. This response can be used to determine the location of shallow subsurface reflectors underneath each station.This application uses seismic data recorded by stations deployed in Argentina and Chile. The Argentine data was recorded by an array of six 2-Hz 3-component stations on the eastern flank of the volcano, deployed during the MalARRgue project in 2012. The Chile data is provided by OVDAS-SERNAGEOMIN (South Andes Volcanic Observatory, Chile). OVDAS has six 3-component 30-seconds stations located on the western flank of the volcano; these stations overlap in the same time period as the Argentine data.Events had been identified and located independently by the arrays deployed in each of the flanks (Casas, 2014; RAV SERNAGEOMIN, 2012). In order to obtain accurate locations of the detected events, the two datasets were used together to relocate them. This result constitutes a necessity for enhancing the resolution of subsurface images obtained by application of SI at PPVC.Preliminary results of this research will be presented.@en

Several seismic investigations - using receiver-function methods as well as tomographic approaches - have been carried out in the Malargüe region (Argentina) for various purposes over a few decades. We use a body-wave seismic interferometry (SI) approach to retrieve reflections later used for the consecutive imaging of the subsurface. We investigate the applicability of the body-wave SI using P-wave coda from local earthquakes with the aim to retrieve reflection responses from a part of the Andean crust below the seismic array we use. We called our technique local-earthquake P-wave coda (LEPC) SI. In this presentation, we show three different LEPC SI results based on three different SI theories: crosscorrelation, crosscoherence, and multidimensional deconvolution.We find that, from a structural-interpretation point of view, multidimensional deconvolution based on the truncated singularvalue decomposition scheme provides us with a better structural imaging than the other SI approaches.We interpret deep thrust faults in the imaging results from LEPC SI, whose presence in this region has previously been indicated from interpretation of active seismic-survey data and exploration-well data. We also interpret dimmed-amplitude parts in the reflection image as possible melting zones that have been previously indicated by magnetotelluric methods. The LEPC SI method we propose could be used as a low-cost alternative to active-source seismic surveys for imaging and monitoring purposes of deeper geothermal reservoirs, e.g. in enhanced geothermal systems where the target structures are down to 10 km depth. @en

Obtaining new seismic responses from existing recordings is generally referred to as seismic interferometry (SI). Conventionally, these seismic interferometric responses are retrieved by simple crosscorrelation of recordings made by separate receivers: a first receiver acts as a 'virtual source' whose response is retrieved at the other receivers. When surface waves are retrieved, the newly retrieved responses can be used to extract receiver-receiver phase velocities. These phase velocities often serve as input parameters for tomographic inverse problems. Another application of SI exploits the temporal stability of the multiply scattered arrivals (the coda). For all applications, however, the accuracy of the retrieved responses is paramount. In practice, this accuracy is often degraded by irregularities in the illumination pattern: correct response retrieval relies on a uniform illumination of the receivers. Reformulating the theory underlying seismic interferometry by crosscorrelation as a multidimensional deconvolution (MDD) process, allows for correction of these non-uniform illumination patterns by means of a so-called point-spread function (PSF). We apply SI by MDD to surface-wave data recorded by the Malargüe seismic array in western Argentina. The aperture of the array is approximately 60 km and it is located on a plateau just east of the Andean mountain range. The array has a T-shape, which makes it very well suited for the application of SI by MDD. We select time windows dominated by surface-wave noise traveling in a favorable direction, that is, traversing the line of virtual sources before arriving at the receivers at which we aim to retrieve the virtual-source responses. These time windows are selected based upon the slownesses along the two receiver lines. From the selected time windows, virtual-source responses are retrieved by computation of ensemble-averaged crosscorrelations. Similarly, ensemble-averaged crosscorrelations between the positions of the virtual sources are computed: the PSF. We use the PSF to deconvolve the effect of illumination irregularities and the source function from the virtual-source responses retrieved by crosscorrelation. The combined effect of time-window selection and MDD results in more accurate and temporally stable surface-wave responses.@en