Fluid-driven processes triggering the 2010 Beni-Ilmane earthquake sequence (Algeria): evidence from local earthquake tomography and 4D Vp/Vs models

Abacha, Issam; Bendjama, Hichem; Boulahia, Oualid; Yelles‐Chaouche, Abdelkrim; Roubeche, Khaled; Rahmani, Sofiane Taki-Eddine; Melaim, Mohamed Anis; Tikhamarine, El-Mahdi

doi:10.1007/s10950-022-10130-8

Cited by 5 publications

(2 citation statements)

References 68 publications

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“…This sequence was characterized by To provide a unique perspective on the Mihoub region, our research takes a distinct approach by employing local tomography with the LOTOS software (Koulakov, 2009). This code has been successfully applied in various geological and tectonic studies in Algeria, including the El Asnam earthquake in 1980 (Bellalem et al, 2015), the Boumerdes earthquake in 2003 (Kherroubi et al, 2017), and the Beni-Ilmane earthquake sequence (Abacha et al, 2014, Abacha et al, 2023. Additionally, LOTOS has been employed globally, such as in volcano structure analysis (Koulakov et al, 2013), crustal structure imaging (Totaro et al, 2014), transition from seamount to ridge subduction (Dinc et al, 2010), velocity and attenuation studies (Koulakov et al, 2010), and identi cation of anomalies in the East African Rift (Jakovlev et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Subsurface Structures of the 2016 Mihoub Earthquake Sequence in North-Central Algeria Using Local Earthquake Tomography

kameche,

hammoud,

issam

et al. 2024

Preprint

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This study investigates the Mihoub region within the Blida Mountains of the Tellian Atlas(Abacha et al., 2014), an area prone to earthquakes due to the Africa-Eurasia plate convergence since the Late Cretaceous. We present both the 1910 Aumale earthquake (Ms 6.6 and I0=VIII) and the 2016 seismic sequence (Mw 5.3). Using local seismic tomography (LET) with Local Tomography Software in its twelfth version (LOTOS 12), we analyze velocity anomaly patterns and their correlation with subsurface structures in the Mihoub area. Our dataset includes 503 well-localized aftershocks from the 2016 seismic sequence. Results show consistent patterns, with positive anomalies associated with rigid block structures and negative anomalies associated with basins, recent formations, and tectonic structures. In particular, a NE-SW corridor of low-velocity anomalies aligns with Miocene and Oligocene formations. Mainshock and aftershock epicenters from the 2016 sequence align with the boundary between low and high-velocity anomalies, indicating the Mihoub Fault zone. Fault depth analysis suggests a maximum depth of 8 km, consistent with previous research. Vertical sections confirm similar azimuths and dips of fault segments involved in the 2016 and 2014 mainshocks. This study provides valuable insights into velocity anomaly patterns and their relationship to fault structures, enhancing the understanding of fault systems and seismic hazards in the Mihoub area.

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Section: Introductionmentioning

confidence: 99%

Subsurface Structures of the 2016 Mihoub Earthquake Sequence in North-Central Algeria Using Local Earthquake Tomography

kameche,

hammoud,

issam

et al. 2024

Preprint

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“…A key aspect of VT seismicity is that it contains much more information than that presented in each waveform. Recent studies have performed source modeling analysis (Sigmundsson et al, 2018;Sigmundsson et al, 2022;Cubuk-Sabuncu et al, 2021), focal mechanisms analysis, and 4D tomography showing the temporal evolution of volcanic structures in (Abacha et al, 2023). (Díaz-Moreno et al, 2015) used spatial and temporal analyses of VT foci evolution; for example, in their study, VTs generated during magma injection were assumed to reflect the effect of hydraulic fracturing, highlighting areas of the crust where stress was propagating as a consequence of magma migration.…”

Section: Introductionmentioning

confidence: 99%

Multi-station volcano tectonic earthquake monitoring based on transfer learning

Titos,

Gutiérrez,

Benítez

et al. 2023

Front. Earth Sci.

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Introduction: Developing reliable seismic catalogs for volcanoes is essential for investigating underlying volcanic structures. However, owing to the complexity and heterogeneity of volcanic environments, seismic signals are strongly affected by seismic attenuation, which modifies the seismic waveforms and their spectral content observed at different seismic stations. As a consequence, the ability to properly discriminate incoming information is compromised. To address this issue, multi-station operational frameworks that allow unequivocal real-time management of large volumes of volcano seismic data are needed.Methods: In this study, we developed a multi-station volcano tectonic earthquake monitoring approach based on transfer learning techniques. We applied two machine learning systems—a recurrent neural network based on long short-term memory cells (RNN–LSTM) and a temporal convolutional network (TCN)—both trained with a master dataset and catalogue belonging to Deception Island volcano (Antarctica), as blind-recognizers to a new volcanic environment (Mount Bezymianny, Kamchatka; 6 months of data collected from June to December 2017, including periods of quiescence and eruption).Results and discussion: When the systems were re-trained under a multi correlation-based approach (i.e., only seismic traces detected at the same time at different seismic stations were selected), the performances of the systems improved substantially. We found that the RNN-based system offered the most reliable recognition by excluding low confidence detections for seismic traces (i.e., those that were only partially similar to those of the baseline). In contrast, the TCN-based network was capable of detecting a greater number of events; however, many of those events were only partially similar to the master events of the baseline. Together, these two approaches offer complementary tools for volcano monitoring. Moreover, we found that our approach had a number of advantages over the classical short time average over long time-average (STA/LTA) algorithm. In particular, the systems automatically detect VTs in a seismic trace without searching for optimal parameter settings, which makes it a portable, scalable, and economical tool with relatively low computational cost. Moreover, besides obtaining a preliminary seismic catalog, it offers information on the confidence of the detected events. Finally, our approach provides a useful tentative label for subsequent analysis carried out by a human operator. Ultimately, this study contributes a new framework for rapid and easy volcano monitoring based on temporal changes in monitored seismic signals.

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