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Rapid and reliable estimation of large earthquake magnitude (above 8) is key to mitigating the risks associated with strong shaking and tsunamis1. Standard early warning systems based on seismic waves fail to rapidly estimate the size of such large earthquakes2–5. Geodesy-based approaches provide better estimations, but are also subject to large uncertainties and latency associated with the slowness of seismic waves. Recently discovered speed-of-light prompt elastogravity signals (PEGS) have raised hopes that these limitations may be overcome6,7, but have not been tested for operational early warning. Here we show that PEGS can be used in real time to track earthquake growth instantaneously after the event reaches a certain magnitude. We develop a deep learning model that leverages the information carried by PEGS recorded by regional broadband seismometers in Japan before the arrival of seismic waves. After training on a database of synthetic waveforms augmented with empirical noise, we show that the algorithm can instantaneously track an earthquake source time function on real data. Our model unlocks ‘true real-time’ access to the rupture evolution of large earthquakes using a portion of seismograms that is routinely treated as noise, and can be immediately transformative for tsunami early warning.

Exploiting supercritical geothermal resources represents a frontier for the next generation of geothermal electrical power plant, as the heat capacity of supercritical fluids (SCF),which directly impacts on energy production, is much higher than that of fluids at subcritical conditions. Reconnaissance and location of intensively permeable and productive horizons at depth is the present limit for the development of SCF geothermal plants. We use, for the first time, teleseismic converted waves (i.e. receiver function) for discovering those horizons in the crust. Thanks to the capability of receiver function to map buried anisotropic materials, the SCF-bearing horizon is seen as the 4km-depth abrupt termination of a shallow, thick, ultra-high (>30%) anisotropic rock volume, in the center of the Larderello geothermal field. The SCF-bearing horizon develops within the granites of the geothermal field, bounding at depth the vapor-filled heavily-fractured rock matrix that hosts the shallow steam-dominated geothermal reservoirs. The sharp termination at depth of the anisotropic behavior of granites, coinciding with a 2 km-thick stripe of seismicity and diffuse fracturing, points out the sudden change in compressibility of the fluid filling the fractures and is a key-evidence of deep fluids that locally traversed the supercritical conditions. The presence of SCF and fracture permeability in nominally ductile granitic rocks open new scenarios for the understanding of magmatic systems and for geothermal exploitation.

Estimating the amount of erosion experienced by a sedimentary basin during its geological history plays a key role in basin modelling. In this paper, we present a novel probabilistic approach to estimate net erosion from porosity-depth data from a single well. Our approach uses a Markov chain Monte Carlo algorithm which readily allows us to deal with imprecise knowledge of the lithology-dependent compaction parameters in a joint inversion scheme using multiple lithologies. The results using synthetic data highlight the advantages of our approach over conventional techniques for net erosion estimation: (a) uncertainties on compaction parameters can be effectively mapped into a probabilistic solution for net erosion; (b) posterior uncertainties are easy to quantify; (c) the joint inversion scheme can automatically reconcile porosity data from different lithologies. Our results also underscore the critical role of prior assumptions on controlling the retrieved estimates for net erosion. Using real data from a well in the Barents Sea, we simulate three possible scenarios of variable prior assumptions on compaction parameters to demonstrate the general applicability of our approach. Strong prior assumptions on the compaction parameters led to unrealistic estimates of net erosion for the target well, indicating the assumptions are probably inappropriate. Our preferred strategy for this dataset is to include additional data to constrain the normal compaction trend of the sediments. This provides a net erosion estimate for the target well of about 2300 m with a standard deviation of 140 m which is in line with previous studies. Finally, we discuss potential guidelines to deal with real applications in which data from normally compacted sediments are not available. One is to use our algorithm as a hypothesis-testing tool to evaluate the results under a large set of assumed compaction parameters. A second is to infer compaction parameters and net erosion simultaneously from the target well porosity data. Although appealing and successful with synthetic data, this strategy provides results which are strongly dependent on the calibration data and the geological history of the sediments sampled by the target well. Highlights• A probabilistic approach to estimate net erosion from well-log compaction data is presented. • Uncertainties in the compaction model are effectively propagated in the solution for net erosion. • The novel methodology is tested against synthetic data and real data from a well in the Barents Sea.

Teleseismic receiver functions (RFs) were used to investigate the seismic structure of the southern margin of the Dublin Basin, a potential geothermal site. Through an inversion-based approach, the elastic properties and seismic anisotropy of sedimentary basin units were examined, using data from a linear array of closely spaced seismic stations. Our results were compared with sonic logs and lithostratigraphies from two nearby boreholes, NGE1 and NGE2 and colocated active seismic data. Including a high-frequency RF (up to 8 Hz) allowed us to compute S-wave velocity models with a vertical resolution [Formula: see text]. The results indicated the presence of a subvertical lateral discontinuity in [Formula: see text], in correspondence with the main basin-bounding fault (Blackrock-Newcastle Fault [BNF]). North of this discontinuity, a shallow low-velocity layer thickens (from 0.7 to 1.0 km thick) toward the inner basin, in agreement with the geometry of the shallowest reflector found by active seismics. A good correlation was also found between the sonic log at NGE1 and our velocity model. Station DB02 showed an increase in [Formula: see text] at a depth of approximately 0.7 km and a decrease in [Formula: see text] at approximately 1.4 km in depth. Two velocity jumps with matching polarities were also observed in the NGE1 sonic log at the contact between the Upper and Lower Calp formations (positive jump, 688 m deep), and between a calcarenite and a sandstone layers (negative jump, 1337 m deep). Moreover, the main velocity contrasts in our model agree with the major lithostratigraphic boundaries inferred from borehole-drilled samples. Two juxtaposed anisotropic layers are identified close to the BNF. Directions of the slow axis of anisotropy are consistent with the borehole structural data. From these observations, the presence of aligned open cracks within the sandstones, possibly fluid-filled, was inferred up to a depth of 2.3 km in the vicinity of the BNF.

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We report the preliminary results from a project (GAPSS-Geothermal Area Passive Seismic Sources), aimed at testing the resolving capabilities of passive exploration methods on a well-known geothermal area, namely the Larderello-Travale Geothermal Field (LTGF). Located in the western part of Tuscany (Italy), LTGF is the most ancient geothermal power field of the world. GAPSS consisted of up to 20 seismic stations deployed over an area of about 50 x 50 Km. During the first 12 months of measurements, we located more than 2000 earthquakes, with a peak rate of up to 40 shocks/day. Preliminary results from analysis of these signals include: (i) analysis of Shear-Wave-Splitting from local earthquake data, from which we determined the areal distribution of the most anisotropic regions; (ii) local-earthquake travel-time tomography for both P-and S-wave velocities; (iii) telesismic receiver function aimed at determining the high-resolution (<0.5km) S-velocity structure over the 0-20km depth range, and seismic anisotropy using the decomposition of the angular harmonics of the RF data-set; (iv) S-wave velocity profiling through inversion of the dispersive characteristics of Rayleigh waves from earthquakes recorded at regional distances. After presenting results from these different analyses, we eventually discuss their potential application to the characterisation and exploration of the investigated area.