The most common method of detecting subsurface structures on continental and marine surfaces is seismic imaging. Although technological advancements have been made, seismic analysis of carbonates remains challenging due to their strong petrophysical heterogeneity, which becomes more challenging when faults are incorporated. This work aims to produce unmigrated forward-seismic models to grasp the deformation behavior of carbonate-bearing fault systems and the related seismic response changes. The porous and faulted carbonates outcropping at the Majella Massif (central Italy) are here used as case study as an analogue of carbonate ramp reservoirs exploited worldwide. Field and laboratory petrophysical data of fault rocks collected at increasing distances from the fault planes show a damage zone/fault core architecture characterized by a decreasing porosity and an increase in shear modulus moving from host rocks towards fault planes. Starting from these observations, unmigrated stacked seismic models have been built simulating fault zones with both increased and decreased porosities with respect to the host rocks. Fault zones with lower porosity than the host rock show slight diffraction hyperbolas, while the diffractive component is pronounced in seismic images of fault rocks with higher porosity than the host rock. Such hyperbolas can be enhanced or weakened by modifying the dip angle of the fault plane or the width of the damage zone but a key role seems to be played by the decreased porosity. The existence of diffraction hyperbolas in unmigrated seismic models is then interpreted as evidence of a damage zone characterized by larger porosity compared to the host rock. Migrated stacked sections would not provide any evidence of increased porosity in the damage zone due to loss of information about the diffractive component resulting from the processing. Consequently, the absence of diffraction hyperbolas in actual unmigrated seismic images is suggested to be related to a decreased porosity in the fault zone. This can be related to cataclasis and solution/cementation of the damage zone rocks as observed in the study area, and related to confining stress acting at depth or fracture filling that counteracts the fracture-related increase in secondary porosity. On the other hand, diffraction hyperbolas in unmigrated seismic images can represent a clue of the presence of large-porosity fault zones.

How to cite: Tomassi, A., Trippetta, F., and de Franco, R.: Seismic signature of carbonate fault rocks changes with changing petrophysical properties: insights from unmigrated seismic forward modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3880, https://doi.org/10.5194/egusphere-egu23-3880, 2023.

The deformation pattern in the interior of cratonic blocks has critical implications for regional structural evolution but still remains an unresolved question. The southwestern Ordos Basin is at the convergence of multiple blocks, leading to intense and complex tectonic stress at this location. However, how the interior of the Ordos block responds to the complex stress field around it is still elusive. This study analyzes the characteristics and evolutionary history of faults in the southwestern part of the inner Ordos Basin using 3D seismic data. Three dominant sets of faults trending NW, NEE, and N–S have developed in the study area. All three sets of faults have subvertical dip angles and straight fault traces and structures that are common in such fault zones, such as flower structures and en-echelon and horsetail arrangements (all indicating strike-slip movement) are present. Structural deformation varies from the center to the periphery of the Ordos block. All three sets of fault systems exhibit small displacements. The polarity of the strike-slip changed in different periods, reflecting the transformation of the stress field in the southwestern Ordos Basin. This illustrates that the formation of cratonic faults is controlled by the regional stress field, while the related strain pattern in the interior of the cratonic block is very different from the deformation around its periphery. These characteristics also demonstrate the special nature of cratonic fault deformation.

How to cite: Wang, Z. and Huang, L.: Characteristics and evolution of strike-slip faults in a stable cratonic block, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1208, https://doi.org/10.5194/egusphere-egu23-1208, 2023.

The demand for reliable high-resolution reflection seismic exploration campaign in hard-rock environments increases continuously. The target of such surveys varies and covers e.g. mineral exploration, geothermal reservoir characterization or the exploration of potential nuclear waste deposit sites. Although the exploration targets are very different, the expectations on the seismic images and the challenges for data acquisition and processing are similar. The expected structures are often steeply dipping with varying strike directions and conflicting dip situations. Furthermore, the reflection seismic data often has to be acquired on land, possibly in populated areas, or in areas with severe accessibility restrictions. These limitations lead to irregular or sparse datasets in combination with sometimes low signal-to-noise ratio for the target reflections in the recorded data.

Therefore, robust imaging methods are needed to generate high resolution reflection seismic images for such kind of data. Focusing prestack depth migration methods have proven to deliver improved image quality compared to standard time- or depth migration approaches. We show the results of our focusing pre-stack depth migration techniques applied to a vintage 3D seismic data set (ISO89) acquired in 1989 around the German deep continental drill site (KTB). We show a comparison to other previously obtained seismic images for the same data set and how the image quality evolved over time.

How to cite: Hlousek, F. and Buske, S.: New insights into vintage 3D reflection seismic data in hard-rock environment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13872, https://doi.org/10.5194/egusphere-egu23-13872, 2023.

Unveiling the geometry and kinematics of subsurface seismic faults is important for earthquake hazard assessment. Earthquake focal mechanisms can provide such fundamental aspects of faulting; however, they often require additional information to distinguish faults and auxiliary planes. We attempt to identify faults using a stress inversion technique, in which fault planes in individual focal mechanisms are selected based on the fault instability parameter. This stress inversion algorithm developed by Vavryčuk (2014) selects a higher instability nodal plane as the fault and finds 70-80% faults correctly to derive reliable stress results. Our tests using synthetic and simulated focal mechanism data with faults known beforehand show that faults are correctly identified especially when the instability of the selected fault plane is significantly higher than that of the auxiliary plane, which can be quantitatively expressed by the instability ratio of the fault plane to that of the auxiliary plane being higher than ~1.3. This constraint can improve further the ability to identify subsurface seismic faults. We apply this technique to the case of geothermal-induced earthquakes that occurred in Pohang, South Korea during 2016-2017. A total of 53 well-constrained and well-located focal mechanism data are inverted to derive a stress condition, during which faults are identified as higher instability nodal planes. These earthquakes occurred in spatially distinct portions of the region associated with water injection through two respective boreholes (PX-1 and PX-2). For the PX-2-related earthquakes, 70% of identified faults are well aligned in their locations and orientations with a large-scale fault, indicating that these earthquakes occurred on the patches of this fault. This fault is responsible for the 2017 Mw 5.5 main earthquake. Fault planes whose instability ratio is higher than ~1.3 are all consistent with this plane. There is more variation in identified fault orientations in PX-1 earthquakes. However, a few fault planes with high instability ratios are generally subparallel to one another. The locations and orientations of these high instability ratio planes are well aligned with a large-scale fault, which is different from, but subparallel to the PX-2 fault. This study demonstrates the possibility of identifying and imaging subsurface seismic faults only using faulting mechanics without other additional information.

How to cite: Chang, C.: Identifying seismic fault geometry from focal mechanisms based on fault instability ratio during a stress inversion, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5622, https://doi.org/10.5194/egusphere-egu23-5622, 2023.

The scientific project TESIRA (TEst Site IRpinia fAult), funded in 2021 by the University of Naples “Federico II”, aims at acquiring multidisciplinary geophysical data above an active fault in an intramontane basin of the Southern Apennines and to achieve, through the integration of this multivariate dataset, an accurate 3D geophysical imaging of the shallow structure of the fault zone in order to understand the link between shallow faulting and petrophysical changes, which affect rock permeability and surface degassing. The target structure is the southern branch of the Irpinia Fault, one of the structures with highest seismogenic potential in the Mediterranean region, causing the 4^th Italian earthquake of last century (1980, Ms=6.9, Pantosti & Valensise, 1990) and generating a modest surface throw at Pantano San Gregorio Magno (Salerno).

A microgravimetric survey and a 3D and 2D Electrical Resistivity measurements survey were acquired between September 2021 and January 2022. 3D seismic data were acquired in July 2022, using two overlapping arrays with a dense geophone distribution covering an area of about four hectares, with a detail of 2.5x2.5m. Moreover, four 2D seismic profiles intersect the 3D volume. An aeromagnetic survey, an extension of the gravimetric survey and a sampling of the CO₂ surface degassing will be completed within this year. We will show the preliminary results of the individual surveys. Later, the different geophysical and geochemical measurements will be integrated using cooperative inversion and machine learning techniques.  We hope that this multidisciplinary approach will provide a more comprehensive understanding of the interaction between surface faulting and basin development in this key area of the Southern Apennines.

References

Pantosti, D.; Valensise, G.; [1990] Faulting Mechanism and Complexity of the November 23, 1980, Campania-Lucania Earthquake, Inferred From Surface Observations, JGR, 95, 319-341

How to cite: Bruno, P. P. G., Ferrara, G., Improta, L., Maraio, S., Di Fiore, V., Iacopini, D., Fusco, M., Punzo, M., Paoletti, V., Cavuoto, G., and De Martini, P. M.: High-resolution 3-D geophysical imaging across a seismogenic fault: the TEst Site IRpinia fAult (TESIRA) project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11176, https://doi.org/10.5194/egusphere-egu23-11176, 2023.

Subsurface characterisation of geothermal fields is important for the expansion of geothermal energy as a low-carbon resource. Faults and fractures provide secondary permeability, thus, their characteristics are crucial parameters in deep geothermal fields. Analysis of ambient seismic noise provides a relatively cheap and widely accessible method for constraining faults and fractures in geothermal settings.

Three-component (3C) beamforming is an array-based method that extracts the polarizations, azimuths, and phase velocities of coherent waves as a function of frequency from ambient seismic noise, offering a comprehensive understanding of the seismic wavefield. 3C beamforming can be used to determine surface wave velocities as a function of depth and the direction of propagation of waves. It is assumed that anisotropic velocities relate to the presence of faults, giving an indication of the maximum depth of the permeability essential for fluid circulation and heat flow throughout a geothermal field. Previous results suggests that some structures have a stronger effect on surface wave velocities than others. Numerical models are essential to study these relationships in more detail.

Here we present a numerical simulation of wave propagation through a model of the subsurface, with anisotropy depicted as faults. This is employed by a rotated staggered grid (RSG) finite-difference (FD) scheme. We model a homogeneous half-space with a fault-like structure (40 m width), changing fault parameters, such as depth, width, velocities and internal conditions of the fault (“fill”). We generate surface waves from a single source as well as multiple sources emulating an ambient noise wavefield.

We then use 3C beamforming on the synthetic data to characterise the modelled wavefield and observe the types of waves present. The polarisation and beam power of the synthetic data denote the composition of the synthetic wavefield and what percentage are retro- and prograde Rayleigh waves and Love waves. To investigate the strength of anisotropy introduced by a single fault we propagate surface waves across the fault in different directions, estimating velocities from array recordings using the beamformer. We are further able to assess the sensitivity of Rayleigh waves towards anisotropy at depth by considering Rayleigh waves at different frequencies sampling different depths.

How to cite: Kennedy, H., Löer, K., Gilligan, A., and Finger, C.: Characterising faults in geothermal fields using surface waves: a numerical study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5756, https://doi.org/10.5194/egusphere-egu23-5756, 2023.

The accurate characterization of microearthquake sequences allows seismologists to shed light on the physical processes involved in earthquake nucleation, the deformation processes underlying rupture activation and propagation, and to image faults geometry at depth. The current methodologies used for this purpose first need the event detection and the phase-picking - usually manual-based - and earthquake locations, which require plenty of work even by expert analysts particularly in the case of microearthquake signals, commonly noise contaminated. Thus, improving standard procedures through semi-automatic or fully-automatic workflows would be an essential step forward towards the more efficient analysis of seismic sequences.

Here we show the results of a semi-automated template matching and machine-learning based workflow applied for the characterization of the foreshock-mainshock-aftershock microearthquake sequence occurred close to Castelsaraceno village (High Agri Valley, Southern Apennines, Italy) in August 2020. The analyses were performed on seismic data mainly recorded by a local seismic network belonging to the High Agri Valley geophysical Observatory (HAVO) deployed in the study area and located at a maximum epicentral distance of ~20 km from the seismicity cluster.

The application of the semi-automated single-station template matching technique to the continuous data-streams of the two nearest stations of the HAVO network (from 28th July to 12th October 2020) allowed us to detect more than twice the number of microearthquakes previously identified by standard manual detections. The phase-picking was automatically performed through a deep-learning algorithm (Phasenet) on the 202 ultimate detected microearthquakes. Finally, an automatic multi-step absolute and relative earthquake location procedure was carried out.

A total of 76 events were identified as belonging to the Castelsaraceno sequence, which occurred in a short time span (7-12 August) and in a limited range of depths (10 -12 km). Both the Ml 2.1 foreshock doublet and the Ml 2.9 mainshock occurred on 7 August ruptured the same seismogenic patch, thus suggesting the presence of a persistent asperity. The integrated analysis of the aftershocks distribution, the focal mechanism of the mainshock, and the geological framework of the study area, allowed revealing the seismogenic fault, not currently mapped in literature: a NE-SW striking (225°), high-angle (55°) fault with a left-lateral transtensional (rake -30°) kinematic. We also hypothesize that the seismic sequence occurred at depth in a brittle layer of the crystalline basement confined between two regions with more ductile rheology; futhermore, the estimated b-value (0.73±0.04) indicates the occurrence of the sequence in a relatively low-heterogeneity material and suggests the unimportant effect of pore-fluid pressure in driving its evolution. 

How to cite: Panebianco, S., Serlenga, V., Satriano, C., Cavalcante, F., and Stabile, T. A.: The analysis of the Castelsaraceno microearthquake sequence (southern Italy) through a semi-automated template matching and machine-learning approach reveals an anti-apenninic fault, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5677, https://doi.org/10.5194/egusphere-egu23-5677, 2023.

The Istituto Nazionale di Geofisica e Vulcanologia (INGV) monitors the Italian peninsula seismicity by using the data recorded by the Italian National Seismic Network (RSN) together with the ones gathered by other permanent regional networks (PRN). Earthquakes are real-time located by the INGV surveillance system and manually revised by the Italian Seismic Bulletin (BSI) group analysts.

Starting from a catalog composed by homogeneous absolute locations (CLASS; Latorre et al., 2023), obtained by using a 3D regional-scale velocity model, we generated a catalog of relative seismic locations (CARS) of about 310,000 events occurred in Italy during 1981-2018.

We inverted absolute P- and S- waves arrival times derived from data collected by RSN plus PRN for the period 1981-2008 and only by RSN for 2009-2018 to apply the double-difference relocation algorithm (Waldhauser and Ellsworth, 2000).

For the second period, we combined the absolute travel times with relative ones obtained by waveforms cross-correlations analysis performed on pairs of similar events. The time domain cross‐correlation method proposed by Schaff et al., 2004 and Schaff & Waldhauser, 2005 was applied to seismograms of all pairs of events separated by 10 km or less and recorded at common stations. Seismograms were filtered in the 1–15 Hz frequency range using a four pole, zero phase band‐pass Butterworth filter. The correlation measurements were performed on 1.0 s long window for P- and S-waves. We collected a total of ~17 million P- and ~23 million S-wave delay times, retaining all measurements with correlation coefficients greater than 0.7.

1D velocity models characterising 18 different (geologically, seismically and tectonically homogeneous) Italian macroareas (after Pastori et al. B2-2019-2021, Wp1-task4) were used in the location procedure.

To cope with the memory limits (15,000 events with less than 200 readings) of the HypoDD code so to use it in a steady operational mode, we first subdivided the study region in the 18 macroareas related to the velocity models, then we additional discretize each in 100x100km2 cells, overlapped by the 80% in longitude and latitude. We repeatedly produced hypocentral locations of the same events that we merged by computing a final weighted mean location.

We present the double-difference catalog of Italian seismicity, allowing to depict alignments clearer with respect to the starting catalog (CLASS), to be related to seismogenic faults and/or to regional structures along the whole Italian peninsula.

How to cite: Michele, M., Di Stefano, R., Chiaraluce, L., Latorre, D., and Castello, B.: CARS - Catalog of Relative Seismic Locations of 1981-2018 Italian Seismicity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14033, https://doi.org/10.5194/egusphere-egu23-14033, 2023.

Machine learning is rapidly becoming ubiquitous in the Earth Sciences promising to provide scalable algorithms for data-mining, interpretation, and model building. Initially heralded for its ability to exclude complicated physics from data analysis, recent innovations seek to merge machine learning  solutions with conventional physics-based methods in order to enhance their capability

We present a novel eikonal tomography approach for Rayleigh wave phase velocity and azimuthal anisotropy based on the elliptical-anisotropic eikonal equation, by formulating the tomography problem as the training of a physics informed neural network (PINN). The PINN eikonal tomography (pinnET) neural network utilizes deep neural networks as universal function approximators and extracts traveltimes and medium properties during the optimization process. Whereas classical eikonal tomography uses a generic non-physics-based interpolation and regularization step to reconstruct traveltime surfaces, optimizing the network parameters in pinnET means solving a physics constrained traveltime surface reconstruction inversion, tackling measurement noise and resolving the underlying velocities that govern the physics. The fast and slow velocity and the anisotropic direction information can be directly evaluated from the trained medium property networks. Checkerboard tests indicate that the input velocity model can be well recovered by using this approach and synthetic data.

We demonstrate this approach by applying it to multi-frequency surface wave data from ChinArray phase II sampling the north-eastern Tibetan plateau. We are able to use much less data to achieve similar subsurface images because of the benefit of including the physics constraint while reconstructing the traveltime surfaces. We are able to obtain excellent results using only 10 sources.  Comparing results from pinnET with conventional eikonal tomography, we find good agreement with distinct low velocity structures beneath the Songpang-Ganzi block, Qilian and Western Qinling Orogen. Large phase velocity uncertainties occur in a small part of the southeastern Ordos Block, the western Songpan-Ganzi Block and the eastern Sichuan basin, which correspond to the reduced data coverage dependent on the selection of the 10 sources. We also verify the accuracy and reliability of the pinnET by choosing only one station as virtual source, the retrieved velocities show relatively good resolution which is much better than in conventional eikonal tomography using similar sized datasets. The method is memory efficient because compressing the traveltimes as outputs to a NN is a concept akin to compressed sensing and offers advantages over traditional anisotropic eikonal tomography or neural network approaches.

How to cite: de Ridder, S., Chen, Y., Rost, S., Guo, Z., and Chen, Y.: Rayleigh wave tomography in the north-eastern margin of the Tibetan Plateau by way of training physics-informed neural networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15556, https://doi.org/10.5194/egusphere-egu23-15556, 2023.