Upper crustal shear-wave velocity structure Beneath Western Java, Indonesia from seismic ambient noise tomography

This paper presents the depth inversion of Rayleigh wave group velocity to obtain an S-wave velocity model from seismic ambient noise cross-correlation in western Java, Indonesia. This study utilizes the vertical component data of a temporary seismograph network deployed in 2016, which was used in a previous study to estimate fundamental mode Rayleigh wave group velocity maps. In this study, the Neighborhood Algorithm was applied to invert the Rayleigh wave group velocities into 1D shear-wave velocity (Vs) profiles, which were then interpolated to produce a high-resolution, pseudo-3D Vs model. These tomographic images of Vs extend to ~ 20 km depth and show a pronounced NE-SW contrast of low and high Vs in the depth range 1–5 km that correlates well with the Bouguer anomaly map. We interpret the low Vs in the northeastern part of the study area as associated with alluvial and volcanic products from the Sunda Shelf and modern volcanic arc, whereas the high Vs in the southwestern part is associated with volcanic arc products from earlier episodes of subduction. We also obtained the depth of the northern Java Basin, which is in the range of 5–6 km, and the Garut Basin, which extends to 5 km depth. For greater depths, Vs gradually increases throughout western Java, which reflects the crystalline basement. This study provides estimates of the shallow crustal Vs structure underneath West Java with higher resolution than previous tomographic studies, which could be useful for supporting future earthquake studies in the region.

Upper Crustal Shear-wave Velocity Structure Beneath Western Java, Indonesia from Seismic Ambient Noise Tomography

This paper presents the depth inversion of Rayleigh wave group velocity to obtain an S-wave velocity model from seismic ambient noise cross-correlation in the western part of Java, Indonesia. This study utilizes the vertical component data of a temporary seismograph network deployed in 2016, which was used in a previous study to estimate fundamental mode Rayleigh wave group velocity maps. In this study, the Neighbourhood Algorithm was applied to invert the Rayleigh wave group velocities into 1D shear-wave velocity (Vs) profiles, which were then interpolated to produce a high-resolution, pseudo-3D Vs model. These tomographic images of Vs extend to ~20 km depth and show a pronounced NE-SW contrast of low and high Vs in the depth range 1-5 km that correlates well with the Bouguer anomaly map. We interpret the low Vs in the northeastern part of the study area as associated with alluvial and volcanic products from the Sunda Shelf and modern volcanic arc, whereas the high Vs in the southwestern part is associated with volcanic arc products from earlier episodes of subduction. We also obtained the depth of the northern Java basin, which is in the range of 5-7 km, and the Garut Basin, which extends to 5 km depth. For greater depths, Vs gradually increases throughout western Java, which reflects the crystalline basement.

CC-FJpy: A Python Package for Extracting Overtone Surface-Wave Dispersion from Seismic Ambient-Noise Cross Correlation

In the past two decades, seismic ambient-noise cross correlation (CC) has been one of the most important technologies in seismology. Usually, only the fundamental-mode surface-wave dispersion was extracted from the ambient noise. Recently, with the frequency–Bessel transform (F-J) method, overtone dispersion can also be extracted from the ambient noise and it adds significant value in inversion. This method has also been verified to be effective for array seismic records of earthquake events. In this article, we describe our algorithm and a Python package called CC-FJpy. For the F-J method, we use the Nvidia’s graphics processing unit to accelerate the computation, which can achieve a 100-fold computational efficiency. We have encapsulated our experiences and technologies into CC-FJpy and tested the CC-FJpy by ambient-noise and earthquake data to ensure its speed and ease of use. Our open-source package CC-FJpy can benefit the development of surface-wave studies using ambient noise and make it easier to start with high-mode surface waves.

Common-midpoint spatial autocorrelation analysis of seismic ambient noise obtained from spatially unaliased sensor distribution

We introduce a passive surface wave method using seismic ambient noise obtained from dozens of receivers forming spatially unaliased two-dimensional (2D) arrays. The method delineates two- or three-dimensional (2D or 3D) S-wave velocity ( VS) models to depths of several hundreds of meters, without using any sources. Typical data acquisition uses 50 to 100 vertical-component 2 Hz geophones on the surface with 5 to 30 m receiver spacing. Cableless seismographs with GPS record 20 to 60 minutes of ambient noise. We establish a 2D grid covering the investigation area and use a common midpoint spatial autocorrelation (CMP-SPAC) method to calculate phase velocities, resulting in a dispersion curve for each grid point. The method provides dozens of dispersion curves in the investigation area. We use a one-dimensional (1D) non-linear inversion to estimate a 1D VS profile for each grid point, and then construct pseudo 2D or pseudo 3D VS models from the 1D VS profiles. Precision and accuracy of the CMP-SPAC method was tested with a numerical simulation using a 3D finite-difference method. The results of the simulation demonstrated the applicability of the method to complex velocity structures. We applied the method to an active fault investigation in China. Sixty-four cableless seismographs were deployed in an investigation area 330 × 660 m (217,800 m2) with 5 m and 30 m receiver spacings for dense and sparse grids, respectively. A 3D VS model was obtained to a depth of 150 m from CMP-SPAC analysis. The resultant 3D VS model indicates approximately 50 m of vertical displacement on a known fault.

Spectral Probability Density for Broadband Seismic Ambient Noise Level

The time-series approach is commonly utilized to get to the estimation of the likelihood thickness work of control ghostly densities (PDF PSD) of waveform information. This paper is concerned with the introduction of the evaluation of waveform commotion to degree the likelihood thickness work (PDF) be done inside, we utilized the metadata from a stock, a parser occurrence of DNP (Denpasar, Bali, Indonesia), IGBI (Ingas, Bali, Indonesia), and PLAI (Plampang, NTB, Indonesia) from BMKG IA-Networks and computations are based on the schedule utilized by McNamara Demonstrate. The point of this paper to characterize the current and past execution of the stations and recognizing the data on clamor levels at BMKG IA-Networks Station. The result of this paper shows the consistency of the unearthly is displayed the DNP, IGBI, and PLAI organize to confirm the quality of information conjointly acts as a test execution broadband arrange to the time taken by the broadband organize within the field and examination the Lombok earthquake in 2018.