Study on Low-Frequency Abnormal Signal and Structural Characteristics of 2015 Azuoqi Ms5.8 Earthquake

In this article, the phenomenon of low-frequency abnormal signals before earthquakes, which reflects the three elements of earthquakes and the beneath structure change information, is discussed. Based on the data recorded at the Shizuishan (SZS), Wuhai (WUH) and Dongshenmiao seismic stations around the epicenter of the Ms5.8 earthquake in Azuoqi, Inner Mongolia, in 2015, the low-frequency abnormal signal from the seismic waves before this earthquake is extracted. At the same time, the autocorrelation method is used to extract the reflected waves of the main interface from teleseismic events recorded by the seismic array in the epicenter area, and then the change information from the beneath structure is obtained. It is explained in time and space that the low-frequency abnormal signal before the main earthquake, extracted from the continuous waveform, is directly related to the change in the underground structure near the epicenter, and it can be determined that the wave propagation direction f the crustal stress before the earthquake is from south to north, and it continues to accumulate near the epicenter until the main earthquake occurs.

SPATIAL AUTOCORRELATION ANALYSIS OF HOUSING DISTRIBUTION IN JOHOR BAHRU

Geographic location naturally generates spatial patterns that are either clustered, dispersed, or random. Moreover, Tobler’s First Law of Geography is essentially a testable assumption in the concept where geographic location matters and one method for quantifying Tobler’s law of geography is through measures of spatial autocorrelation. Therefore, the purpose of this study is to identify the spatial patterns of housing distribution in Johor Bahru through the spatial autocorrelation method. The result of the global spatial autocorrelation analysis demonstrates a high degree of clustering within the housing distribution, as well as the identification of a clustered pattern with a highly positive Moran’s I value of 0.995207. Following that, the LISA cluster map successfully identified individual clusters of each housing unit with their neighbours through the red and blue colours displayed on the map, as well as revealing home buyers’ preferences for a property in each location.

Predicting Tipping Points in Chaotic Maps with Period-Doubling Bifurcations

In this paper, bifurcation points of two chaotic maps are studied: symmetric sine map and Gaussian map. Investigating the properties of these maps shows that they have a variety of dynamical solutions by changing the bifurcation parameter. Sine map has symmetry with respect to the origin, which causes multistability in its dynamics. The systems’ bifurcation diagrams show various dynamics and bifurcation points. Predicting bifurcation points of dynamical systems is vital. Any bifurcation can cause a huge wanted/unwanted change in the states of a system. Thus, their predictions are essential in order to be prepared for the changes. Here, the systems’ bifurcations are studied using three indicators of critical slowing down: modified autocorrelation method, modified variance method, and Lyapunov exponent. The results present the efficiency of these indicators in predicting bifurcation points.

Spatial autocorrelation method for reliable measurements of two-station dispersion curves in heterogeneous ambient noise wavefields

Summary The microtremor survey method (MSM) is used to estimate S-wave velocity profiles from microtremors or ambient noise. Although array-based MSM analyses are usually used for shallow exploration purposes because of their robustness, the extraction of numerous phase velocity dispersion curves by two-station microtremor analysis is attractive because those dispersion curves can be used to construct high-resolution phase velocity maps by solving a least-squares problem. However, in exploration studies (>1 Hz), the reliability of two-station microtremor analysis can be affected by short data acquisition times and heterogeneous noise distributions mainly caused by anthropogenic noises. In this study, we propose a new approach to estimate surface-wave dispersion curves between station pairs considering a heterogeneous ambient noise distribution based on the spatial autocorrelation method. We first estimated azimuthal variations of noise energy from the complex coherencies between all station pairs in a receiver array, and then estimated dispersion curves between station pairs. Our field example demonstrates that modelling the azimuthal noise energy distribution allows us to use not only the real parts of complex coherencies, but also the imaginary parts, which are usually neglected when assuming a homogeneous noise field. The simultaneous use of the real and imaginary parts of complex coherencies improves the reliability and continuity of phase velocity estimations between station pairs. Because the stability of phase velocity estimations depends on the azimuths between station pairs, we carefully selected between-station azimuths that produce stable phase velocities. Selected phase velocities at 8 Hz can be used to construct high-resolution phase velocity maps with least-squares inversion. Because our approach does not require a regular receiver interval for two-station analysis, it allows for more flexible seismic array geometries. This is particularly important for MSM analyses in urban areas, where limited space is available to install seismic stations. We conclude that our proposed approach is effective in reconstructing high-resolution shallow structures in heterogeneous ambient noise fields.

Joint ambient noise autocorrelation and receiver function analysis of the Moho

SUMMARY In the field of seismic interferometry, cross-correlations are used to extract Green’s function from ambient noise data. By applying a single station variation of the method, using autocorrelations, we are in principle able to retrieve zero-offset reflections in a stratified Earth. These reflections are valuable as they do not require an active seismic source and, being zero-offset, are better constrained in space than passive earthquake based measurements. However, studies that target Moho signals with ambient noise autocorrelations often give ambiguous results with unclear Moho reflections. Using a modified processing scheme and phase-weighted stacking, we determine the Moho P-wave reflection time from vertical autocorrelation traces for a test station with a known simple crustal structure (HYB in Hyderabad, India). However, in spite of the simplicity of the structure, the autocorrelation traces show several phases not related to direct reflections. Although we are able to match some of these additional phases in a qualitative way with synthetic modelling, their presence makes it hard to identify the reflection phases without prior knowledge. This prior knowledge can be provided by receiver functions. Receiver functions (arising from mode conversions) are sensitive to the same boundaries as autocorrelations, so should have a high degree of comparability and opportunity for combined analysis but in themselves are not able to independently resolve VP, VS and Moho depth. Using the timing suggested by the receiver functions as a guide, we observe the Moho S-wave reflection on the horizontal autocorrelation of the north component but not on the east component. The timing of the S reflection is consistent with the timing of the PpSs–PsPs receiver function multiple, which also depends only on the S velocity and Moho depth. Finally, we combine P receiver functions and autocorrelations from HYB in a depth–velocity stacking scheme that gives us independent estimates for VP, VS and Moho depth. These are found to be in good agreement with several studies that also supplement receiver functions to obtain unique crustal parameters. By applying the autocorrelation method to a portion of the EASI transect crossing the Bohemian Massif in central Europe, we find approximate consistency with Moho depths determined from receiver functions and spatial coherence between stations, thereby demonstrating that the method is also applicable for temporary deployments. Although application of the autocorrelation method requires great care in phase identification, it has the potential to resolve both average crustal P and S velocities alongside Moho depth in conjunction with receiver functions.

Estimation of shallow subsurface S-wave velocity structure in urban area based on inversion of apparent dispersion curve

Due to its efficiency, convenience, non-destructive nature and strong anti-interference capability, the microtremor survey method (MSM) has found wide applications in urban geological surveys. The spatial autocorrelation method is diffusely applied to extract the dispersion curves from microtremor signals, which needs to satisfy the assumption that the energy of the fundamental Rayleigh wave is dominant. However, for layered media containing a layer with a significant low- or high-velocity contrast, this assumption is distinctly incorrect for certain frequency ranges. We present a processing methodology comprising the extraction and inversion of the apparent dispersion curves based on extended spatial autocorrelation method and fast simulated-annealing algorithm. We analyse synthetic microtremor signals for three selected geological models, and then compare the S-wave velocity structures estimated from their inversions with the actual models. Subsequently, a filed data example is given to detect the shallow stratigraphic structures in Guangzhou city, China, in which the new MSM was used. The estimated two-dimensional S-wave velocity model provided an accurate description of the thickness and depth of the strata in the study area, based on a priori information. Moreover, the S-wave velocity structures estimated from the MSM and the results from the drilling match very well, indicating that MSM is a reliable geophysical technique in urban geological surveys. Combined with available borehole information, MSM can be a very robust and effective method for detecting the shallow three-dimensional velocity structures in an urban area.