Assessment of earthquake location uncertainties for the design of local seismic networks

The ability to estimate earthquake source locations,along with the appraisal of relevant uncertainties,is paramount in monitoring both natural and human-induced micro-seismicity.For this purpose,a monitoring network must be designed to minimize the location errors introduced by geometrically unbalanced networks.In this study,we first review different sources of errors relevant to the localization of seismic events,how they propagate through localization algorithms,and their impact on outcomes.We then propose a quantitative method,based on a Monte Carlo approach,to estimate the uncertainty in earthquake locations that is suited to the design,optimization,and assessment of the performance of a local seismic monitoring network.To illustrate the performance of the proposed approach,we analyzed the distribution of the localization uncertainties and their related dispersion for a highly dense grid of theoretical hypocenters in both the horizontal and vertical directions using an actual monitoring network layout.The results expand,quantitatively,the qualitative indications derived from purely geometrical parameters(azimuthal gap(AG))and classical detectability maps.The proposed method enables the systematic design,optimization,and evaluation of local seismic monitoring networks,enhancing monitoring accuracy in areas proximal to hydrocarbon production,geothermal fields,underground natural gas storage,and other subsurface activities.This approach aids in the accurate estimation of earthquake source locations and their associated uncertainties,which are crucial for assessing and mitigating seismic risks,thereby enabling the implementation of proactive measures to minimize potential hazards.From an operational perspective,reliably estimating location accuracy is crucial for evaluating the position of seismogenic sources and assessing possible links between well activities and the onset of seismicity.

Dating long thrust systems on Mercury:New clues on the thermal evolution of the planet

The global tectonics of Mercury is dominated by contractional features mainly represented by lobate scarps,high relief ridges,and wrinkle ridges.These structures are the expression of thrust faults and are linear or arcuate features widely distributed on Mercury.Locally,these structures are arranged in long systems characterized by a preferential orientation and non-random spatial distribution.In this work we identified five thrust systems,generally longer than 1000 km.They were named after the main structure or crater encompassed by the system as:Thakur,Victoria,Villa Lobos,Al-Hamadhani,and Enterprise.In order to gain clues about their formation,we dated them using the buffered crater counting technique,which can be applied to derive the ages of linear landforms such as faults,ridges and channels.To estimate the absolute age for the end of the thrust system's activity,we applied both Le Feuvre and Wieczorek Production Function and Neukum Production Functions.Moreover,to further confirm the results obtained with the buffered crater counting method,the classic stratigraphic approach has been adopted,in which a faulted and an unfaulted craters were dated for each system.The results gave consistent ages and suggested that the most movements along major structures all over Mercury most likely ended at about 3.6-3.8 Ga.This gives new clues to better understand the tectonics of the planet and,therefore,its thermal evolution.Indeed,the early occurrence of tectonic activity in the planet's history,well before than predicted by the thermophysical models,coupled with the orientation and spatial distribution of the thrust systems,suggests that other processes beside global contraction,like mantle downwelling or tidal despinning,could have contributed to the first stage of the planet's history.

Topography,structural and exhumation history of the Admiralty Mountains region,northern Victoria Land,Antarctica

The Admiralty Mountains region forms the northern termination of the northern Victoria Land,Antarctica.Few quantitative data are available to reconstruct the Cenozoic morpho-tectonic evolution of this sector of the Antarctic plate,where the Admiralty Mountains region forms the northern termination of the western shoulder of the Mesozoic-Cenozoic West Antarctica Rift System.In this study we combine new low-temperature thermochronological data(apatite fission-track and(U-Th-Sm)/He analyses)with structural and topography analysis.The regional pattern of the fission-track ages shows a general tendency to older ages(80-60 Ma)associated with shortened mean track-lengths in the interior,and younger fission-track ages clustering at 38-26 Ma with long mean track-lengths in the coastal region.Differently from other regions of Victoria Land,the younger ages are found as far as 50-70 km inland.Single grain apatite(U-Th-Sm)/He ages cluster at 50-30 Ma with younger ages in the coastal domain.Topography analysis reveals that the Admiralty Mountains has high local relief,with an area close to the coast,180 km long and 70 km large,having the highest local relief of>2500 m.This coincides with the location of the youngest fission-track ages.The shape of the area with highest local relief matches the shape of a recently detected low velocity zone beneath the northern TAM,indicating that high topography of the Admiralty Mountains region is likely sustained by a mantle thermal anomaly.We used the obtained constraints on the amount of removed crustal section to reconstruct back-eroded profiles and calculate the erosional load in order to test flexural uplift models.We found that our back-eroded profiles are better reproduced by a constant elastic thickness of intermediate values(Te=20-30 km).This suggests that,beneath the Admiralty Mountains,the elastic properties of the lithosphere are different with respect to other TAM sectors,likely due to a stationary Cenozoic upper mantle thermal anomaly in the region.

Cenozoic Tectonic Uplift History of Western Qinling: Evidence from Sedimentary and Fission-Track Data

The western Qinling （秦岭） orogenic belt is one of the outermost ranges in the northeas- tern Tibetan Plateau. Its tectonic uplift history is therefore essential to insight on the evolution history of the plateau. However, the timing of deformation and uplift history is still poorly known. Fortunately, its Cenozoic orogenic history is recorded in an excellent synorogenic sedimentary sequence exposed in the Tianshui （天水）Ba- sin, the northeastern foot of western Qinling. Ac- cording to sedimentary-tectonic analysis of the Yaodian （尧店） and Lamashan （喇嘛山） sections based on the previous magnetostratigraphy stu- dies, we speculated that two stages （occurred at 9.2-7.4 and -3.6 Ma） of variation in depositional facies were attributed to the uplift and deforma- tion of the western Qinling, and the modern structure geomorphic frame of the northeastern Tibet formed after 2.6 Ma. Furthermore, four stages of active processes along the western Qinling occurred at 49--41, 34-27, 25-19 and -13 Ma, are deciphered from an integrated detrital apatite fission-track data of the Ganquan （甘泉）, Yaodian main sections and seven small ones. The former two are represents the exhumation episodes triggered by tectonism and the others attributed to the volcanic signals.

Local earthquakes detection: A benchmark dataset of 3-component seismograms built on a global scale

Machine learning is becoming increasingly important in scientific and technological progress,due to its ability to create models that describe complex data and generalize well.The wealth of publicly-available seismic data nowadays requires automated,fast,and reliable tools to carry out a multitude of tasks,such as the detection of small,local earthquakes in areas characterized by sparsity of receivers.A similar application of machine learning,however,should be built on a large amount of labeled seismograms,which is neither immediate to obtain nor to compile.In this study we present a large dataset of seismograms recorded along the vertical,north,and east components of 1487 broad-band or very broad-band receivers distributed worldwide;this includes 629,0953-component seismograms generated by 304,878 local earthquakes and labeled as EQ,and 615,847 ones labeled as noise(AN).Application of machine learning to this dataset shows that a simple Convolutional Neural Network of 67,939 parameters allows discriminating between earthquakes and noise single-station recordings,even if applied in regions not represented in the training set.Achieving an accuracy of 96.7,95.3,and 93.2% on training,validation,and test set,respectively,we prove that the large variety of geological and tectonic settings covered by our data supports the generalization capabilities of the algorithm,and makes it applicable to real-time detection of local events.We make the database publicly available,intending to provide the seismological and broader scientific community with a benchmark for time-series to be used as a testing ground in signal processing.