Pesawaran, Lampung telah diguncang rentetan gempa bumi magnitudo rendah pada bulan Januari 2021. Gempa bumi tersebut tidak disertai dengan gempa utama dengan magnitudo besar sehingga dapat disebut dengan gempa swarm. Relokasi hiposenter telah dilakukan dengan mengunakan data dari jaringan stasiun BMKG untuk mengetahui sumber dari gempa swarm tersebut. Metode double-difference digunakan dan didapatkan hasil relokasi 19 dari 22 gempa swarm yang terjadi. Distribusi kedalaman hiposenter berkisar pada kedalaman 1,5-4,5 km sehingga dapat disebut dengan gempa kerak dangkal. Berdasarkan sayatan melintang terhadap kedalaman teramati bahwa sebaran gempa memiliki kemiringan ke arah Timur Laut. Lokasi gempa yang presisi juga menunjukan bahwa adanya deliniasi dan tepat berada di atas terduga Sesar Menanga. Berdasarkan analisis hasil relokasi hiposenter dapat disimpulkan bahwa gempa swarm Pesawaran disebabkan oleh aktivitas Sesar Menanga.

Permutation entropy (PE) is a complexity metric that encodes a time series into sequences of symbols and can be used to decipher between deterministic and stochastic behavior. This study investigates PE variations in seismic noise during three eruption cycles in 2011, 2017, and 2018 at Shinmoedake volcano, Japan. The volcano is monitored by a dedicated seismic network and by infrasound microphones that recorded continuously during the aforementioned eruptions. The frequency range 1–7 Hz was used in order to infer temporal changes of PE in seismic noise and minimize any human contributions. The results showed that PE values decreased before the occurrence of each eruption. By combining these results with other observations we can attribute this decrease in PE to two reasons: first, to the occurrence of volcanic tremor that is a deterministic signal, and second, to magma migration at shallower depth beneath Shinmoedake which can attenuate high-frequency seismic waves and thus result in a less stochastic signal. PE also exhibited a spike-like increase just before the onset of the three eruptions. In 2011 and 2017, this feature was probably associated with bubble growth and collapse due to the interaction between the aquifer and high temperature magma. In 2018 the aquifer had mostly evaporated; hence, the spike in PE values was likely generated by fracturing of solidified magma within the conduit as fresh magma was pushing its way upwards. These results show that PE is a potentially useful tool for monitoring seismic noise at volcanoes and can contribute toward forecasting volcanic eruptions in conjunction with other widely used methodologies. Graphical Abstract

Despite their usefulness for volcano monitoring, emergent seismic signals, such as volcanic tremor or signals generated by lahars, are difficult to identify with confidence in a timely fashion. Machine-learning algorithms offer an objective alternative to traditional methods of identifying such volcanoseismic signals, because they are able to handle quickly large amounts of data, while requiring little input from the user. In this work, we combine permutation entropy and centroid as well as dominant frequency with supervised machine learning to evaluate their potential in identifying volcanic tremor and lahar signals recorded during the 2009 Redoubt volcano eruption. The particular dataset was chosen for the reason that the properties and occurrence times of the volcanoseismic signals during the eruption are well known from previous studies. We find that the selected features can effectively discriminate both types of signals against the seismic background, especially for stations that are near the source. Results show that the identification success rate for volcanic tremor reaches up to 96%, whereas this rate becomes up to 91% for lahar signals. The calculation of the features as well as the application of the machine-learning algorithms is fast, allowing their implementation in the operational environment of a volcano observatory during a volcanic crisis. Finally, the proposed methodology can potentially be used to objectively identify other emergent seismic signals such as tectonic tremor along subduction zones, glacial tremor, or seismic signals generated during landslides.