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The 2024 Hualien earthquake on April 3, eastern Taiwan, with a magnitude of 7.2, resulted in 18 fatalities and 1,155 injuries. Road networks suffered extensive damage, with 55 roadside landslides exacerbating the disruption. Preliminary mapping identified over 3,239 landslides with sizes ranging from 62 to 524,113 m2, primarily clustered in areas experiencing peak ground acceleration exceeding 250 cm/s2. Four landslide dams were formed post-event, including one near a hydropower station. The following impacts heighten concerns about hazards such as frequent unstable slope failures, debris flows caused by intense rainfall, and subsequent coseismic rockfalls occur due to strong aftershocks. [ABSTRACT FROM AUTHOR]

Bedload transport is a natural process that strongly affects the Earth's surface system. An important component of quantifying bedload transport flux and establishing early warning systems is the identification of the onset of bedload motion. Bedload transport can be monitored with high temporal resolution using passive acoustic methods, for example, hydrophones. Yet, an efficient method for identifying the onset of bedload transport from long‐term continuous acoustic data is still lacking. Benford's Law defines a probability distribution of the first‐digit of data sets and has been used to identify anomalies. Here, we apply Benford's law to continuous acoustic recordings from Baiyang hydrometric station, a tributary of Liwu River, Taroko National Park, Taiwan at the frequency of 32 kHz from stationary hydrophones deployed for 3 years since 2019. We construct a workflow to parse sound combinations of bedload transportation and analyze them in the context of hydrometric sensing constraining the onset, and recession of bedload transport. We identified three separate sound classes in the data related to the noise produced by the motion of pebbles, water flow, and air. We identify two bedload transport events that lasted 17 and 45 hr, respectively, covering about 0.35% of the total recorded time. The workflow could be transferred to other different catchments, events, or data sets. Due to the influence of instrument and background noise on the regularity of the residuals of the first‐digit, we recommend identifying the first‐digit distribution of the background noise and ruling it out before implementing this workflow. Plain Language Summary: Long‐term, high‐frequency monitoring of Earth surface processes brings huge data sets that can be of high quality. Benford's Law defines the specific probability distribution of the first‐digit of the data sets and has been used to identify anomalies and high‐energy events. We provide a workflow for applying Benford's Law to identify the onset of the motion of coarse sediment along the river bed at a time resolution of seconds. Since Benford's Law has demonstrated usefulness in acoustic amplitude analysis in this study, it could serve as a tool for identifying anomalous events in any kind of real‐time data series, which could be beneficial for generating event samples for machine learning applications. Key Points: Long‐term, high‐frequency acoustic monitoring constitutes huge‐volume data sets with a low signal‐to‐noise ratioThe distinct first‐digit distribution between signal and noise can used to filter out 99% of background noise from acoustic recordingsWe applied the method to a three‐year‐long acoustic data set in Baiyang, identifying two bedload transportation events [ABSTRACT FROM AUTHOR]

On August 7, 2021, a disastrous rainfall-induced landslide occurred at Mt. Silabaku in the Yusui Stream source area, Taiwan. The huge deposits from the Silabaku landslide severely damaged the Minbaklu Bridge, and minor collapses in the Yusui Stream disrupted the only major road, Expressway No. 20. More than 400 residents from the three adjacent villages were stranded for 19 days in the mountainous area. The analysis of rainfall data, seismic records, topographic and geologic data, and historical orthoimages of the slope conditions, landform evolution, and current status of the Silabaku landslide revealed that the landslide resulted from slate avalanche induced by heavy rains, with local failures during Typhoon Morakot in 2009. Based on the high-resolution topographic data of light detection and ranging and an unmanned aerial vehicle, the debris deposits accumulated along the Yusui Stream were estimated to be above 8 million m3. In addition, a fan volume of 2 million m3 changed the flow line of the Laonong River, and the thick deposits pose a considerable threat to the safety of the nearby villages and infrastructures. [ABSTRACT FROM AUTHOR]

Outburst flooding after a landslide dam breach causes global fatalities and devastation. Information on the timing, magnitude, and location of the landslide dam is crucial to hazard assessment. Despite recent efforts, successful real-time detection of landslide dams in mountain valleys and dam breakages is rare. Here, we present a series of seismic analysis including landslide detection, identification of landslide dam formations, and monitoring of dam breaches. We show the working of our analysis on a recent landslide dam that occurred in eastern Taiwan. The results indicate that our seismic analysis provides important information on the location and magnitude of landslides and the dam forming based on data acquired from a regional broadband seismic network. Furthermore, we see that the failure of the landslide dam is directly caught by the riverside seismic signals. To provide warning times for impending floods to downstream areas, we believe that proximal high-quality seismic signals along the river channel are viable options for an operational real-time monitoring system, for landslide dams occurring in mountain valleys. Our work can be a starting point to raise awareness in the community. [ABSTRACT FROM AUTHOR]

Regional monitoring of rock slope failures using the seismic technique is rarely undertaken due to significant source location errors; this method also still lacks the signal features needed to understand events of this type because of the complex mass movement involved. To better comprehend these types of events, 10 known events along highways in Taiwan were analyzed. First, a hybrid method (GeoLoc) composed of cross-correlation-based and amplitude-attenuation-based approaches was applied, and it produced a maximum location error of 3.19 km for the 10 events. We then analyzed the ratio of local magnitude (ML) and duration magnitude (MD) and found that a threshold of 0.85 yields successful classification between rock slope failure and earthquake. Further, GeoLoc can retrieve the seismic parameters, such as signal amplitude at the source (A0) and ML of events, which are crucial for constructing scaling law with source volume (V). Indeed, Log(V) = 1.12 ML + 3.08 and V = 77 290 A00.44 derived in this study provide the lower bound of volume estimation, as the seismic parameters based on peak amplitudes cannot represent the full process of mass loss. Second, while video records correspond to seismic signals, the processes of toppling and sliding present column- and V-shaped spectrograms, respectively. The impacts of rockfall link directly to the pulses of seismic signals. Here, all spectrogram features of events can be identified for events with volumes larger than 2000 m3 , corresponding to the farthest epicenter distance of ∼ 2.5 km. These results were obtained using the GeoLoc scheme for providing the government with rapid reports for reference. Finally, a recent event on 12 June 2020 was used to examine the GeoLoc scheme's feasibility. We estimated the event's volume using two scalings: 3838 and 3019 m3. These values were roughly consistent with the volume estimation of 5142 m3 from the digital elevation model. The physical processes, including rockfall, toppling, and complex motion behaviors of rock interacting with slope inferred from the spectrogram features were comprehensively supported by the video record and field investigation. We also demonstrated that the GeoLoc scheme, which has been implemented in Sinwulyu catchment, Taiwan, can provide fast reports, including the location, volume, and physical process of events, to the public soon after they occur. [ABSTRACT FROM AUTHOR]

We detected 12 landquakes in Taiwan with collapse areas between 0.27 and 2.48 km2 associated with rock collapse, rockslide and debris and/or rock avalanche during the passage of Typhoon Morakot in 2009. These events were recorded by seismic stations of the Broadband Array in Taiwan for Seismology. Their locations were determined by a cross-correlation technique that maximizes the coherency of horizontal envelope function among seismic stations with a mean location error of 1.92 km. We applied time-frequency analysis to estimate the bandwidth of seismic energy generated by the landquakes. The predominant frequency ranges from 0.5 to 5.0 Hz, with higher-frequency signals likely caused by block impact. We extracted signal duration (SD), peak ground velocity, rise time (TR), area of velocity envelope function (AE) from the closest station and estimated the collapse area (AC) and run-out distance (DR) by mapping satellite images. Based on aforementioned seismic and geometrical parameters, we defined the potential of initial impact (PI), the frequency of rock impact signal (fI) and the mean quasi-front velocity (Vf) as indicators of landquake types, especially for events with dam formation (dam-formation-type events). We also derived an empirical linear relationship between the envelope area (AE) and collapse area (AC) with a high correlation coefficient of 0.83. Our automatic approach is very effective for rapid determination of landquake centroid location and collapse area, and for identifying dam-formation event using records from existing real-time broad-band seismic networks, thus providing an important alternative for landquake hazard mitigation. [ABSTRACT FROM AUTHOR]

Presents a study which introduced an algorithm based on dynamic programming to find the hardware and software partitioning and mapping of communicating processes in a task graph. Details on two published works on fine-grain hardware/software partitioning which use dynamic programming; Illustration of the process decomposition in a task graph; Scheduling using dynamic programming.

Landslides have caused extensive infrastructure damage and caused human fatalities for centuries. Intense precipitation and large earthquakes are considered to be two major landslide triggers, particularly in the case of catastrophic landslides. The most widely accepted mechanistic explanation for landslides is the effective-stress dependent shear strength reduction due to increases in pore water pressure. The Chashan landslide site, selected for the present study, has been intensively studied from geological, geophysical, geodetic, geotechnical, hydrological, and seismological perspectives. Our seismic monitoring of daily relative velocity changes (dv/v) indicated that landslide material decreases coincided with the first half of the rainy period and increased during the latter half of the rainy period. The geodetic surveys before and after the rainy period identified vertical subsidence without horizontal movement. The results from the multidisciplinary investigation enabled us to draw a conceptual model of the landslide recovery process induced by water loading. Where all sliding materials were stable (safety factor > 1.0), unconsolidated landslide colluvium and impermeable sliding surfaces trapped the seepage water to form a water tank, provided that compact forces were acting on the materials below the sliding boundary. The vertical force of compaction facilitates an increase in the cohesion and strength of landslide materials, thereby increasing the landslide materials' stability. We demonstrated that the recovery process periodically occurs only under the combined conditions of prolonged and intense precipitation and the related stability conditions. [ABSTRACT FROM AUTHOR]

Hazards from gravity-driven instabilities on hillslope (termed 'landquake' in this study) are an important problem facing us today. Rapid detection of landquake events is crucial for hazard mitigation and emergency response. Based on the real-time broadband data in Taiwan, we have developed a near real-time landquake monitoring system, which is a fully automatic process based on waveform inversion that yields source information (e.g., location and mechanism) and identifies the landquake source by examining waveform fitness for different types of source mechanisms. This system has been successfully tested offline using seismic records during the passage of the 2009 Typhoon Morakot in Taiwan and has been in online operation during the typhoon season in 2015. In practice, certain levels of station coverage (station gap < 180°), signal-to-noise ratio (SNR ≥ 5.0), and a threshold of event size (volume >106 m3 and area > 0.20 km2) are required to ensure good performance (fitness > 0.6 for successful source identification) of the system, which can be readily implemented in other places in the world with real-time seismic networks and high landquake activities. [ABSTRACT FROM AUTHOR]