Search

The 2019 Mw 6.4 and Mw 7.1 Ridgecrest earthquake sequence occurred in the eastern California shear zone (ECSZ). The mainshock ruptured the Little Lake fault zone, and aftershocks extended from the Garlock fault in the south to the southern end of the 1872 M 7.5 Owens Valley earthquake rupture in the north. We present data from the Southern California Seismic Network (SCSN) and partner seismic networks recorded by the SCSN in the region. These time-series data and related products such as the SCSN earthquake picks and catalogs, available from the Southern California Earthquake Data Center, provide the most comprehensive seismic datasets for the 2019 Ridgecrest earthquake sequence.

Seismology has continuously recorded ground‐motion spanning up to decades. Blind, uninformed search for similar‐signal waveforms within this continuous data can detect small earthquakes missing from earthquake catalogs, yet doing so with naive approaches is computationally infeasible. We present results from an improved version of the Fingerprint And Similarity Thresholding (FAST) algorithm, an unsupervised data‐mining approach to earthquake detection, now available as open‐source software. We use FAST to search for small earthquakes in 6–11 yr of continuous data from 27 channels over an 11‐station local seismic network near the Diablo Canyon nuclear power plant in central California. FAST detected 4554 earthquakes in this data set, with a 7.5% false detection rate: 4134 of the detected events were previously cataloged earthquakes located across California, and 420 were new local earthquake detections with magnitudes −0.3≤ML≤2.4, of which 224 events were located near the seismic network. Although seismicity rates are low, this study confirms that nearby faults are active. This example shows how seismology can leverage recent advances in data‐mining algorithms, along with improved computing power, to extract useful additional earthquake information from long‐duration continuous data sets.

We analyze the background seismicity, initiation, and earliest stages of the Guy-Greenbrier, Arkansas, earthquake sequence, which was potentially induced by wastewater injection starting in July 2010, during the 3-month time period 2010-06-01 to 2010-09-01. High-resolution observations of low-magnitude seismicity, and the high-quality Arkansas public well database, facilitate detailed analysis of spatial and temporal correlations between earthquakes, wastewater injection, and hydraulic fracturing. We detected 14,604 earthquakes, with magnitudes −1.5≤ML≤2.9, using two sensitive, waveform-similarity-based event detection methods in parallel: Fingerprint And Similarity Thresholding (FAST), and template matching. We located the 1,740 largest earthquakes that form 16 spatially compact clusters, using P and S phases from 3 stations with the double-difference relocation algorithm and an improved velocity model constrained by the location of quarry blasts. We enhanced the temporal resolution of these event clusters by assigning smaller unlocated events to a cluster based on waveform similarity. Most clustered earthquakes during this time were both spatially and temporally correlated with hydraulic fracturing stimulation at several production wells. For one cluster, microseismicity was correlated with individual stages of stimulation. Many other wells had no detectable nearby seismicity during stimulation. We found a smaller number of events located on the Guy-Greenbrier Fault that were likely induced by wastewater injection. The concurrent presence of seismicity induced by hydraulic fracturing and wastewater injection presents a challenge for attribution and seismic hazard characterization, but the combination of precision seismology and high-quality well information allows us to disentangle the effects of these two processes.

In this work, we report on a novel application of Locality Sensitive Hashing (LSH) to seismic data at scale. Based on the high waveform similarity between reoccurring earthquakes, our application identifies potential earthquakes by searching for similar time series segments via LSH. However, a straightforward implementation of this LSH-enabled application has difficulty scaling beyond 3 months of continuous time series data measured at a single seismic station. As a case study of a data-driven science workflow, we illustrate how domain knowledge can be incorporated into the workload to improve both the efficiency and result quality. We describe several end-to-end optimizations of the analysis pipeline from pre-processing to post-processing, which allow the application to scale to time series data measured at multiple seismic stations. Our optimizations enable an over 100× speedup in the end-to-end analysis pipeline. This improved scalability enabled seismologists to perform seismic analysis on more than ten years of continuous time series data from over ten seismic stations, and has directly enabled the discovery of 597 new earthquakes near the Diablo Canyon nuclear power plant in California and 6123 new earthquakes in New Zealand.

Three novel HLA alleles, HLA-A*26:118, DQB1*02:65 and DPB1*05:01:07, were identified and confirmed by monoallelic sequencing.

Four novel HLA alleles identified in Korean individuals and confirmed by monoallelic sequencing.

The HLA-C*02:02:33 allele differs by a single nucleotide at codon 215 compared with HLA-C*02:02:02:01.

The HLA-C*01:135 allele differs by a single nucleotide at codon 265 compared with HLA-C*01:02:01:01.

The HLA-C*14:02:01:03 differs by a single nucleotide at intron 5 (2157 CT) compared with C*14:02:01:01.

The new allele, A*11:251N, differs from A*11:01:01 by insertion of two nucleotides at position 204-205.

The new allele HLA-C*03:03:35 showed one nucleotide difference with C*03:03:01:01 at position 408 (G>T).

HLA-DQB1*04:01:05 differs from DQB1*04:01:01 by a single nucleotide substitution at codon 30 (TAC>TAT).

Extracting earthquake signals from continuous waveform data recorded by networks of seismic sensors is a critical and challenging task in seismology. Earthquakes occur infrequently in long-duration data and may produce weak signals, which are challenging to detect while limiting the number of false discoveries. Earthquake detection based on waveform similarity has demonstrated success in detecting weak signals from small events, but existing techniques either require prior knowledge of the event waveform or have poor scaling properties that limit use to small data sets. In this paper, we describe ongoing research into the use of similarity search for large-scale earthquake detection. We describe Fingerprint and Similarity Thresholding (FAST), a new earthquake detection method that leverages locality-sensitive hashing to enable waveform-similarity-based earthquake detection in long-duration continuous seismic data. We demonstrate the detection capability of FAST and compare different fingerprinting schemes by performing numerical experiments on test data, with an emphasis on false alarm reduction.

The HLA-C*14:02:01:03 differs by a single nucleotide at intron 5 (2157 C>T) compared with C*14:02:01:01.

Data mining techniques applied to continuous waveforms reveal many previously unknown small earthquakes., Seismology is experiencing rapid growth in the quantity of data, which has outpaced the development of processing algorithms. Earthquake detection—identification of seismic events in continuous data—is a fundamental operation for observational seismology. We developed an efficient method to detect earthquakes using waveform similarity that overcomes the disadvantages of existing detection methods. Our method, called Fingerprint And Similarity Thresholding (FAST), can analyze a week of continuous seismic waveform data in less than 2 hours, or 140 times faster than autocorrelation. FAST adapts a data mining algorithm, originally designed to identify similar audio clips within large databases; it first creates compact “fingerprints” of waveforms by extracting key discriminative features, then groups similar fingerprints together within a database to facilitate fast, scalable search for similar fingerprint pairs, and finally generates a list of earthquake detections. FAST detected most (21 of 24) cataloged earthquakes and 68 uncataloged earthquakes in 1 week of continuous data from a station located near the Calaveras Fault in central California, achieving detection performance comparable to that of autocorrelation, with some additional false detections. FAST is expected to realize its full potential when applied to extremely long duration data sets over a distributed network of seismic stations. The widespread application of FAST has the potential to aid in the discovery of unexpected seismic signals, improve seismic monitoring, and promote a greater understanding of a variety of earthquake processes.

New allele, B*15:367, differs from B*15:11:01 by a single nucleotide exchange at codon 222 (GAG→AAG).

The new allele, A*24:313, showed one nucleotide difference with A*24:02:01 (595GA).

The HLA-C*15:02:01:03 allele differs from the HLA-C*15:02:01:01 allele by a single-nucleotide substitution in the 5' untranslated region (-42 CA).

HLA-B*15:263 differs from HLA-B*15:18:01 by a single nucleotide exchange at position 824, CG (codon 251 TCTTGT).

The C*06:99 allele substitutes one nucleotide of C*06:02:01:01 at codon 50 (CCG→CTG), Pro to Leu.

The new allele, HLA-A*30:81, differs from A*30:01:01 by one nucleotide substitution at codon 272 (CTG→ATG).

New allele, B*15:367, differs from B*15:11:01 by a single nucleotide exchange at codon 222 (GAG→AAG).

The C*06:99 allele substitutes one nucleotide of C*06:02:01:01 at codon 50 (CCG→CTG), Pro to Leu.

The new allele, HLA-C*03:04:36, differs from C*03:04:01:01 by one nucleotide substitution at codon 207 (GGC→GGT).

The new allele B*40:186 shows a one nucleotide substitution compared with B*40:01:02 at codon 56 (GGG → AGG) resulting in coding change, Gly to Arg.

The C*07:244 changes single nucleotide of C*07:02:01 at codon 75 (CGA → CAA), Arg to Gln.

The new allele, HLA-A*30:81, differs from A*30:01:01 by one nucleotide substitution at codon 272 (CTG→ATG).

The new allele, HLA-C*03:04:36, differs from C*03:04:01:01 by one nucleotide substitution at codon 207 (GGC→GGT).

The new allele B*40:186 shows a one nucleotide substitution compared with B*40:01:02 at codon 56 (GGG → AGG) resulting in coding change, Gly to Arg.

The new allele A*02:07:02 shows a single nucleotide substitution compared with A*02:07:01 at codon 233 (ACC → ACT) without any amino acid change.