Patent Application
20150316666
The present invention relates generally to techniques for analysis of seismic data. More specifically, it relates to techniques for monitoring and detection of seismic events such as earthquakes.
Earthquake detection is the foundation for many studies in observational seismology. Earthquake catalogs contain a database of the latitude, longitude, depth, origin time, and magnitude for every earthquake detected from seismograms recorded by at least 4 stations. These catalogs, however, are complete only to a certain minimum magnitude. Seismic events of smaller magnitude escape detection using standard techniques and are missing from the catalog. Thus, it remains a challenge to improve earthquake monitoring by detecting more earthquakes from massive volumes of continuous waveform data across a seismic network, especially those that cannot be found with existing detection methods.
In standard earthquake monitoring, an earthquake is independently detected by individual stations using an energy-based detection technique such as Short Term Averaging/Long Term Averaging (STA/LTA). Given a seismogram, STA/LTA computes the ratio of the short-term average (STA) power in a short window to the long-term average (LTA) power in a longer window as these windows slide through the waveform data. A detection is declared when the ratio STA/LTA exceeds a predetermined threshold. For example,
STA/LTA successfully detects earthquakes with impulsive, high signal-to-noise ratio (SNR) P and S wave arrivals, and can be used widely without prior knowledge of the event waveform or source information. However, STA/LTA fails to detect earthquakes, or may produce false detections, in various situations: 1) low SNR, 2) waveforms with non-impulsive, emergent arrivals, 3) if many earthquakes overlap in time, 4) competing cultural noise sources, and 5) sparsely recorded earthquakes—such as at only one station. Low-frequency earthquakes (LFEs) are hard to find because of 1) and 2), aftershocks and swarms are missing from catalogs because of 3), and potentially induced seismicity is poorly characterized because of 5). Many interesting seismological phenomena, absent from current catalogs, lie dormant in continuous waveform data, waiting to be discovered.
Researchers have attempted to overcome some limitations of STA/LTA energy-based detectors. Because seismic events that repeat in time, over a period of several years, are observed to have very similar waveforms when recorded at the same station, waveform cross-correlation can, in principle, exploit waveform similarity to provide more sensitive earthquake detection.
Waveform cross-correlation, also called matched filtering or template matching, can detect a known seismic signal in noisy data. It is a “one-to-many” search method that cross-correlates a template waveform vi with the signal of interest, with successive candidate time windows wi(t) of continuous waveform data, computing their normalized correlation coefficient, CC(t):
where t is the start time of each candidate window in the continuous data. The value CC(t) ranges between −1 and 1; any candidate window wi(t) that results in a CC(t) spike above some threshold is considered similar enough to the template waveform to call it a detection.
Template matching can be improved by using templates that include waveforms from multiple channels and stations. Detection is based on the network correlation coefficient, computed from CC(t) summed over all channels and stations.
Template matching is a versatile technique that has been applied to a wide range of seismicity studies. Waveform cross correlation for detection has also been used for nuclear monitoring and discrimination, as well as microseismic monitoring in geothermal fields and oil and gas reservoirs.
However, a major limitation of template matching is that it requires an a priori waveform template. It is thus unable to detect new unknown events with low SNR repeating signals. It remains an open problem to detect signals with similar waveforms in continuous data without any prior knowledge of the desired signal.
When the form of a desired signal is not known, autocorrelation can be used to perform an exhaustive, “many-to-many” search for a similar signal. The seismic signals of interest are known to have short duration (earthquake waveforms are usually a few seconds long on each channel), so the continuous data is partitioned into short overlapping windows, and every window is cross-correlated with every other window of the continuous data. Window pairs with CC(t) exceeding a detection threshold are marked as candidate events, which can then be post-processed with an additional cross-correlation on pairs of candidate events, or grouped into families and stacked to form less noisy template waveforms.
However, autocorrelation is computationally intensive, and ultimately infeasible for massive data sets because autocorrelation scales quadratically with data duration. Autocorrelation is also very sensitive to timing, so the time lag between adjacent windows in the continuous data needs to be short, which makes the number of windows large.
While autocorrelation can be feasible to detect similar seismic signals that span an hour of continuous data, it becomes completely impractical if the seismogram signals span days, weeks, months, or years.
In summary, standard energy detectors currently used in earthquake monitoring fail to find events when the signal-to-noise ratio is low or many events overlap in time. Waveform cross-correlation is capable of detecting such missing events, especially when the desired signal is known in advance. But finding an unknown repeating earthquake signal with waveform cross-correlation is computationally infeasible for long data durations. Thus, there remains a need for improved techniques for detecting seismic events.
The present inventors have discovered a computationally scalable solution to find unknown repeating seismic events. Through a unique combination of techniques such as data compression, discriminative feature extraction, locality-sensitive hashing, and similarity matrices, it is capable of detecting uncataloged earthquakes in long durations of continuous waveform data with dramatic improvements in computational efficiency compared with standard autocorrelation techniques. The approach is further improved through the combined use of three components of seismic data and an innovative technique for combining data measured by multiple sensors in a network.
In one aspect, the present invention provides a method to efficiently search massive continuous seismic station or network data to find seismic signals (e.g., earthquakes) that repeat or have similar waveforms. The method does not require a priori knowledge of any template waveforms, but is based instead on the assumption that similar sources have similar waveform signatures. Also provided is an over-network approach that finds similar waveforms over a distributed network of sensors using an algorithm to incorporate and combine data from seismic stations at different locations in the network.
Embodiments of the technique use waveform feature extraction to develop a compact description of seismic signals. After feature extraction, waveforms with similar features are grouped together using hashing techniques. This technique for earthquake detection uses the fact that similar seismic sources will result in similar waveform signatures over a sensor network to detect earthquakes of all sizes without a priori knowledge of their characteristics. The technique works for individual sensors, and it may also be implemented over a network of sensors.
Because it enables practical detection of events that have remained undetected using prior techniques, the invention solves a long-standing need for better information on what seismic sources are triggered—either intentionally or unintentionally—through energy (oil, gas, and geothermal) development. It also answers a need for improved earthquake monitoring in areas where earthquakes may be induced by human actions.
In one aspect, the invention provides a method for identifying seismic events. The method includes recording by a seismic sensor, extracting a sequence of overlapping time windows from the continuous time series data, and generating from each of the overlapping time windows a fingerprint to produce a sequence of fingerprints corresponding to the sequence of overlapping time windows. From each of the fingerprints a set of multiple hash signatures is generated using multiple distinct locality-sensitive hash functions to produce a sequence of hash signature sets corresponding to the sequence of fingerprints. For each of the hash signature sets, a set of corresponding hash buckets in distinct hash tables of a hash database is selected, where one or more of the multiple distinct locality-sensitive hash functions corresponds to each of the distinct hash tables. For each of the hash signature sets and corresponding fingerprint, a number of hash tables containing a matching fingerprint in the selected set of corresponding hash buckets is counted, to produce a similarity matrix whose elements represent similarity between pairs of overlapping time windows of the continuous time series data representative of seismic activity. Seismic events are then identified from the similarity matrix.
In some embodiments, seismic events are identified from the similarity matrix by combining the similarity matrix with similarity matrices derived from continuous time series data representative of seismic activity recorded at other seismic sensors to produce a network similarity matrix, and identifying seismic events from the network similarity matrix by applying a detection threshold. The matrices are preferably added by only adding nonzero elements and only adding lower (or, alternatively, upper) triangular elements, thereby making addition more computationally efficient because the matrices are sparse and symmetric.
In preferred embodiments, the fingerprint from each of the overlapping time windows is generated by calculating wavelet coefficients of a spectrogram of each of the overlapping time windows. In some embodiments, the fingerprint from each of the overlapping time windows is generated by combining fingerprints derived from continuous time series data representative of three components of seismic activity, one vertical, two horizontal.
The technique has a number of advantages: It is computationally efficient, does not require prior information on the nature of the seismic source, and can take advantage of entire sensor networks to enable detection at lower signal-to-noise ratios.
The invention has applications to earthquake monitoring, from the global scale to the scale of energy (oil, gas) reservoirs, geothermal power production fields, and wastewater disposal wells. It has applications to the oil and gas industry, finding previously unknown seismic sources during hydraulic fracturing operations, waste-water disposal, and during production. It has applications to the geothermal power industry, finding previously unknown seismic sources, particularly during active time periods when one or more sources may be active. It has applications to carbon sequestration, monitoring for possible failure of reservoir integrity that manifests itself through seismic waves. It has applications to earthquake and volcano monitoring more generally, finding sources that were not previously detected, particularly during periods of high activity.
Embodiments of the invention efficiently find similar seismic signals without prior knowledge of their form. First, seismic waveform data measured by seismic sensors is preprocessed to extract short overlapping time windows. The data preferably includes one vertical and two horizontal components, each of which is processed. Next, key discriminative features are extracted from each window to create its fingerprint, i.e., a compact proxy that identifies a window of seismic data. A database of fingerprints is created using locality-sensitive hashing (LSH). Given a query seismic waveform, its fingerprint is calculated and hashed to efficiently identify all other fingerprints in the database that resemble it. Each row of the symmetric similarity matrix represents the results of the hash-based matching between the database of hashed fingerprints and the hashed fingerprint of the query seismic waveform; the rows and columns of the similarity matrix represent the same fingerprints.
The feature extraction steps are illustrated in
Using standard seismic sensor equipment, continuous waveform data 500 is recorded at a station for hours, days, weeks, or longer. The data includes the vertical component, and preferably also includes two horizontal components as well. The amplitude of the data can have a large dynamic range, which is the reason why earthquake magnitudes are measured with a logarithmic scale. A bandpass filter is preferably applied to the data to help eliminate effects of correlated noise at lower frequencies, which may interfere with the ability to detect the uncataloged earthquakes; the passband should exclude the frequency range of noise in the data channel, which is specific to the station. The filtered data is decimated from its original sampling rate of 100 sps to 20 sps, so that the Nyquist frequency is 10 Hz. The decimation should downsample the data to the lowest possible frequency while ensuring that the Nyquist frequency remains above the frequency range of the expected seismic signals. Sampling rates from 20 to 100 sps are typical for seismic network data.
After filtering and decimating the time series data, the technique computes its spectrogram 502, which consists of intensity values for each frequency and time, using the short-time Fourier transform. Specifically, overlapping time windows in the continuous time series, separated by some lag, are extracted, a Hamming tapering function is applied to each window, and the FFT of each window is computed. As an illustrative example, shown in
In order to facilitate comparison and detection of short duration seismic signals, the spectrogram 502 is itself divided into overlapping windows in the time dimension. Each of these windows is referred to as a spectral image 504. The spectral window length Lfp and spectral window lag τfp for spectral images may be, for example, Lfp=100 samples and τfp=10 samples, respectively. A shorter spectral image lag increases detection sensitivity and timing precision, at the expense of additional runtime. Since the time window lag for spectrogram generation was 0.1 s, Lfp=(100 samples)*(0.1 s/sample)=10 s, and τfp=(10 samples)*(0.1 s/sample)=1 s. Preferably, Lfp should range from 5 s for small, short duration earthquakes, to 20 s for larger earthquakes with longer duration; Lfp should be long enough to include the entire seismic signal waveform of interest, but not longer—otherwise, noise also enters the window, which degrades the shape of the waveform. Preferably, τfp should range from 0.5 s to 2 s; its minimum value would be 0.1 s (1 sample), but this is inefficient. An empirical rule for choosing τfp would be 5-20 samples.
The total number of spectral image windows, and ultimately the number of fingerprints Nfp, is:
where Nt is the number of time samples in the spectrogram. For example, Nfp=604,781 windows, for 1 week of data sampled at 20 sps, with spectrogram time window lag=2 samples, Lfp=100 samples, and τfp=10 samples.
Because the spectrogram content varies slowly with time, we can find similar seismic signals even with a longer spectral image lag. The 1 s spectral image time lag is much longer than the 0.1 s lag used in time series autocorrelation, which contributes to fast performance of our detector: fewer spectral images from the same continuous data, and fewer fingerprints to first calculate and then compare for similarity.
Although the spectral image length is 10 s in this example, this does not directly correspond to 10 s of waveform data, but instead includes more information. The first time sample in the spectral image contains 10 s of waveform data from the time series, converted to its frequency content. The second time sample in the spectral image contains data from 0.1 s to 10.1 s—with an extra 0.1 s of data at the end, since the lag between windows for spectrogram generation was 0.1 s. The third time sample has data from 0.2 s to 10.2 s, and so on. Since the spectral image has Lfp=100 time samples, the 100th sample has data from 9.9 s to 19.9 s. Therefore, the spectral image actually contains content from 19.9 s of waveform data.
Preferably, to prepare the data for a recursive wavelet transform, each spectral image is downsampled to the highest power of 2 less than the current number of samples in the time dimension. (We already downsampled in frequency to 25=32.) Since we started with 100 samples in the time domain, we downsample to 26=64 samples. At the end, each spectral image has dimensions 32 samples by 64 samples.
In the next stage of the processing pipeline, a two-dimensional Haar wavelet transform of each spectral image 504 is computed to obtain its wavelet coefficient representation 506. This facilitates lossy data compression, while remaining robust to small noise perturbations. Wavelets are a mathematical tool for multi-resolution analysis: they hierarchically decompose data into its overall average shape, plus successive levels of detail describing deviations from the average shape, from coarsest to finest resolution. A discrete wavelet transform (DWT) has different kinds of basis functions (one example is the Haar basis), is localized in both the time and frequency domains, and can express nonstationary, burst-like signals (such as earthquakes) using only a few wavelet coefficients.
In general, a DWT recursively transforms data to wavelet coefficients, which makes the algorithm fast. In the 1D case, at each recursive step, the average between two adjacent numbers in the data is computed, then the “detail coefficients”, or the difference between the first number in each pair and the average, are computed until only one average value remains.
Computing a 2D Haar wavelet transform involves calculating the recursive average and detail coefficients in two dimensions. The generalization from 1D to 2D is well known in the art.
The technique provides a compact, diagnostic representation of the original spectral image that can identify similar seismic signals and discriminate between seismic signals and noise. The technique keeps only a fraction of the total Haar wavelet coefficients that best retain key discriminative features of the original spectral image, and discards the rest. Earthquake signals constitute a small percentage of the continuous data, since most of it is noise, so we expect representative Haar wavelet coefficients for earthquakes to strongly deviate from those of noise. To maximize the discriminative value of the Haar coefficients, we retain the top k Haar coefficients with the largest deviation from their typical values. The size of the deviation for a given Haar coefficient is quantified using a standardized score, or z-score, based on the empirical distribution of that coefficient over the data set.
To obtain Haar coefficient z-scores, we first take the Haar coefficients for all spectral image windows and place them in a M×N matrix we call H, where M=32*64=2048 Haar coefficients (since each spectral image had dimensions 32*64), and N=Nfp=604,781 spectral images. We normalize each column (Haar coefficients from one spectral image) of the matrix, dividing by its L2 norm. Then for each row i of the matrix, we compute the mean μi and standard deviation σi for Haar coefficient i, over all windows j:
The z-score for each Haar coefficient i and window j is then
The z-score distribution has zero mean and unit standard deviation. Adding more continuous data, and therefore more Haar coefficients from spectral image windows, would change μi, σi, and consequently the z-score distribution.
Preferably, the predetermined threshold is k=800 (out of 32×64=2048) coefficients with the highest amplitude z-scores, which corresponds to just under 40% of all the coefficients. Reasonable values for k are 200-800; higher values are not recommended because the resulting fingerprints would no longer be sparse.
Furthermore, the wavelet coefficient z-score amplitudes can be reduced to only their signs: +1 for positive coefficients, −1 for negative coefficients, and 0 for the discarded coefficients to obtain a compressed wavelet coefficient representation 508. This provides additional data compression, capturing the main characteristics of the image in compact form while remaining robust to noise degradation.
The final feature extraction step is to generate the binary fingerprint 510 from the top amplitude wavelet coefficient z-score signs. The fingerprint is represented in binary so that the Min-Hash and LSH algorithms may be used for efficient similarity search. The following encoding scheme may be used to represent each of the top wavelet coefficients as a two-bit binary sequence: Negative coefficients are represented by 01, Zero is represented by 00, Positive coefficients are represented by 10. At this point, each spectral image is thus represented by a binary fingerprint 510.
A measured continuous data waveform thus produces a large collection of fingerprints: sparse, compact items that characterize the spectral images extracted from the time series waveform data.
To enable scalable search for similar earthquake signals, fingerprints are grouped into hash buckets, and comparisons between fingerprints (or their original waveforms) are limited to those in a matching hash bucket group. In a preferred embodiment, similar fingerprints are grouped together using Min-Hash and LSH algorithms. Using this technique, the similarity search time for N fingerprints increases near-linearly with data duration. In contrast, the corresponding O(N2) complexity of autocorrelation similarity search is practically infeasible for large N.
As described earlier, template matching and autocorrelation techniques use the correlation coefficient CC(t) in Eq. 1 to measure how similar two waveforms are. However, CC(t) is not an ideal metric to evaluate similarity of two binary fingerprints, which can only have 0 or 1 values. Instead, embodiments of the present invention preferably compare two binary fingerprints using the Jaccard similarity as a similarity metric. Jaccard similarity J(A,B) for two binary fingerprints A and B is defined as
The numerator contains the number of elements where both A and B are equal to 1, while the denominator counts elements where either A, B, or both A and B are equal to 1.
In a preferred embodiment, an algorithm called Min-Wise Independent Permutation (Min-Hash) is used to further reduce dimensionality of each binary fingerprint from a 4096-element bit vector to a shorter integer array. Min-Hash uses several random hash functions h(X), where each hash function maps a sparse, binary, high-dimensional fingerprint X to one integer, h(X). Min-Hash has the important property that the probability of two fingerprints A and B mapping to the same integer is equal to their Jaccard similarity:
Pr[h(A)=h(B)]=J(A,B) Eq. 6
Thus, Min-Hash reduces dimensionality in a probabilistic manner, while preserving the similarity between fingerprints A and B.
The output of Min-Hash is an array of p unsigned integers called a Min-Hash Signature (MHS), given a sparse binary fingerprint as input. The MHS estimates the Jaccard similarity by counting all of the matching integers from the MHS of both A and B, and then dividing by p. As p increases, the estimate of the Jaccard similarity improves. The p Min-Hash functions are constructed by drawing p×4096 (4096 is the number of bits in a fingerprint) independent and identically distributed random samples from a uniform distribution, returned by calling any uniform hash function, to get an array r(i,j), where i=1, . . . , p, and j=1, . . . , 4096. Then, to obtain the output of a Min-Hash function hi(X) for a given fingerprint X, we use the index of the k non-zero bits in the fingerprint to select k of the values r(i,j) generated previously (e.g., if we consider the first hash function h1(X) out of all p hash functions, and the index of one of the non-zero bits in the fingerprint X is j=4, then r(1,4) is selected). Out of all the k selected values r(i,j), we select the minimum value and assign the index j that obtains the minimum as the output of the hash function hi(X). We further reduce the size of the output by only keeping 8 bits, so that the number of bits of a MHS is 8p; each integer in the MHS has a value between 0 and 255. Table 1 shows an example of MHS arrays for two similar fingerprints A and B—notice that the MHS are almost the same.
LSH uses the MHS to determine how to store the fingerprint in the database. The 8p bits of each MHS are partitioned into b groups with 8r bits in each group (p=b*r). The 8r bits in each of the b groups are used to generate b hash keys, where each hash key belongs to exactly one out of b hash tables. Each hash key retrieves a hash bucket, which can contain multiple values. We generate b*Nfp hash keys and values for each MHS from all Nfp fingerprints and insert all of the values into the hash buckets in the b hash tables given their corresponding hash keys. The values stored in the hash buckets are 32-bit integers, where each value is a reference that uniquely identifies a fingerprint.
For example,
We used these LSH parameter values: r=5 hash functions per hash table to determine the hash bucket (preferably, r ranges from 1 to 8), and b=100 hash tables (preferably, b >>1), so the MHS for each fingerprint had p=5*100=500 integers. If r is too low, we have a small number of hash buckets, so each bucket may have too many fingerprints, which would increase the runtime to search for similar fingerprints, as well as the number of false detections. If r is too high, fingerprints would be spread thin among too many hash buckets, so we may miss detections if similar fingerprints end up in different hash buckets. Increasing b improves the probability of finding two fingerprints in the same bucket even if they are only slightly similar (as shown in
A query fingerprint will always match itself as a similar fingerprint in the database, but this trivial information is not useful. Also, we are not interested in “near-repeat” pairs where a fingerprint is reported as similar to itself, but offset by a few time samples. Therefore a “near-repeat exclusion” parameter, nr=5, is used to avoid returning any fingerprint within nr samples of the query fingerprint as a match.
The theoretical probability that two fingerprints hash to the same bucket (have a hash collision) in at least v out of b tables, with r hash functions per table, as a function of their Jaccard similarity s, is:
Performance of this technique is a major advance over prior techniques. As an illustration,
In tests by the inventors, this technique for waveform fingerprinting and similarity search can successfully detect cataloged earthquakes in single component waveform data. In addition, it also detected a significant number of uncataloged events such as aftershocks. Performance may be enhanced further by including data from other channels at the same station, allowing detection of a substantial number of additional events with even lower SNR than using one channel.
Accordingly, in some embodiments, multiple channels (components) of data are used to improve fingerprinting with efficient similarity search.
Each of the three channel waveforms 150 is processed independently and in parallel, following the steps described above for one channel, to obtain three channels of spectrograms 152, three channels of filtered spectrograms 154, and three channels of spectral images 156 and three corresponding wavelet transformed images (i.e., arrays of wavelet coefficients) 158. The wavelet transformed images from the three channels are stacked (i.e., arrays are concatenated, keeping the time dimensions aligned) to get a single composite wavelet transformed image 160, having three times the frequency samples and the same number of time samples. For example, three arrays with 32 wavelet-transformed-frequency samples and 64 wavelet-transformed-time samples are combined to obtain a single array with 96 wavelet-transformed-frequency samples and 64 wavelet-transformed-time samples. Next, data compression is applied, as with the single channel case, by keeping only the top k=2400 (out of 96×64=6144) coefficients to obtain a compressed wavelet representation 162, then the binary fingerprint 164 is computed. The resulting fingerprints, which are three times as large as those from a single channel, are hashed into the database and searched in the same way to get detections. The same parameters may be used, although adjusting the values of r, b, v, and the detection threshold for ftable by trial and error may produce better detection performance.
In a preferred embodiment, detection of repeating seismic events is performed by constructing a similarity matrix for each channel. To detect similar earthquakes using one channel of continuous data, every fingerprint is used as a search query for the database, so the output of similarity search is a list of pairs of similar fingerprint indices, converted to times in the continuous data, with associated similarity values. This list of pairs can be visualized as a sparse, symmetric Nfp×Nfp similarity matrix.
An example of a similarity matrix is shown in
Seismic events are identified from the similarity matrix by applying a threshold criterion for detection. The detection threshold used for 1 week of continuous data was ftable=0.33, meaning that the fingerprint pair needed to be present in the same hash bucket in at least 33 out of b=100 hash tables. This threshold was set by visual inspection: most pairs above the threshold looked like earthquakes, and most pairs below looked like noise. It is desirable to set the threshold to minimize both the number of false positive detections and false negative (missed) detections. The threshold value depends on the SNR of the data set; further research is required to automate the threshold calculation.
Some post-processing is required to convert a list of pairs of similar fingerprint times to a list of earthquake detection times. First, the list of pairs can have duplicate pairs with similarity above the event detection threshold of ftable=0.33, when they represent the same pair with slight time offsets. For example, take three pairs: (395172, 161542) with similarity 0.92, (395173, 161543) with similarity 1.00, and (395174, 161544) with similarity 0.76. Only the pair (395173, 161543) with the highest similarity 1.00 is retained; all other duplicate pairs within 21 s of the times for the highest similarity pair are removed. Next we create a list of event detection times. We sort the pairs in decreasing order of similarity, then add each event in the pair to the detection list. Sometimes we can encounter a duplicate event: for example, pair (245266 s, 1335 s) has similarity 0.79, so we add both events to the detection list, then later we have pair (1332 s, 547 s), with lower similarity 0.75. We classify the 1332 s event as a duplicate of the 1335 s event, since they are within 21 s of each other, and we do not add the 1332 s event to the list. Finally we have a list of event detections, with each event defined by its time in the continuous data in seconds, and its associated similarity. For the multiple-station case, described below, this post-processing method is applied to the network similarity matrix. The time window length used for removing duplicate pairs and events (here 21 s) should be about twice the spectral image window length (here 10 s).
Further improvements in performance are provided by embodiments in which the fingerprinting algorithm is used to process waveform data from multiple stations at distinct, but nearby, geographical locations. Combining data from multiple stations allows a unique multi-site over-network detection of repeating seismic signals with higher performance than the single-station detection. Events with even lower SNR may be detected while also keeping the number of false detections very low. Finding a coherent signal recorded at several stations located at different distances and azimuths from the source allows for better discrimination between signal and noise, and the ability to find its source location given a velocity model of the area.
Seismic events are detected from the similarity matrix by identifying non-zero elements above a predetermined threshold. For example,
The techniques of the present invention may be implemented, for example, as a system including a seismic sensor station or network or stations in communication with a computer for processing data input to the computer or stored by the computer in accordance with the methods disclosed herein and displaying or otherwise outputting results of the processing. The present invention may also be realized as a digital storage medium tangibly embodying machine-readable instructions executable by a computer, where the instructions implement the techniques of the invention described herein.
This application claims priority from U.S. Provisional Patent Application 61/988,580 filed May 5, 2014, which is incorporated herein by reference. This application claims priority from U.S. Provisional Patent Application 62/046,871 filed Sep. 5, 2014, which is incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| 62046871 | Sep 2014 | US | |
| 61988580 | May 2014 | US |
