Los Alamos Seismic Network EES-17 SS Team Challenge: Identify Odd LASN Events [Slides]
An overview of local earthquake signals is provided, and the team's signals recorded are shown.
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Engineering topics
Roberts, Peter Morse
Publications and source records attributed to Roberts, Peter Morse.
Seismicity Monitoring in North-Central New Mexico by the Los Alamos Seismic Network
The initial seismic stations of the Los Alamos Seismic Network (LASN) were installed in 1973, as a part of research on monitoring of nuclear testing. The extent of the network rapidly expanded by the late 1970s. By the middle 1980s, spatial coverage of the network was drastically reduced, due to a loss of funding. Since then, however, it has been possible to expand the coverage of the network with additional stations and to slowly upgrade network equipment to match contemporary instrument standards. These improvements will make it possible to keep recording the network’s data and to initiate real-time exchange of data with other institutions to improve earthquake monitoring throughout New Mexico and neighboring states. During more than 40 yr of operation, the network has provided a slow but steady increase in the volume of earthquake seismograms available to study seismicity and tectonics in north-central New Mexico. The current network covers an area from the Valles Caldera on the west, to within the Rio Grande rift on the east. LASN has yielded locations for about 900 earthquakes in north-central New Mexico between 1973 and 2013 (the most recent locations available). Epicenters of these have a complex pattern, with some that can be attributed to the deformation of the rift, though most are spread in areas west of the rift. A lack of seismicity in and near the Valles Caldera reinforces an earlier observation, based on far fewer earthquakes, which first called attention to this paucity of seismicity and attributed it to ductile deformation resulting from elevated crustal temperatures.
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Using Machine Learning to Discern Eruption in Noisy Environments: A Case Study Using CO2-Driven Cold-Water Geyser in Chimayó, New Mexico
In this paper, we present an approach based on machine learning (ML) to distinguish eruption and precursory signals of Chimayó geyser (New Mexico, U.S.A.) under noisy environmental conditions. This geyser can be considered a natural analog of CO 2 intrusion into shallow water aquifers. By studying this geyser, we can understand upwelling of CO 2 -rich fluids from depth, which has relevance to leak monitoring in a CO 2 sequestration project. ML methods such as random forests (RFs) are known to be robust multiclass classifiers and perform well under unfavorable, noisy conditions. However, the extent of the RF method’s accuracy is poorly understood for this CO 2 -driven geysering application. The current study aims to quantify the performance of RF classifiers to discern the geyser state. Toward this goal, we first present the data collected from the seismometer that is installed near the Chimayó geyser. The seismic signals collected at this site contain different types of noises such as daily temperature variations, animal movement near the geyser, and human activity. First, we filter the signals from these noises by combining the Butterworth high-pass (BH) filter and an autoregressive (AR) method in a multilevel fashion. We show that by combining these filtering techniques in a hierarchical fashion leads to a reduction in noise in the seismic data without removing the precursors and eruption event signals. We then use RF on the filtered data to classify the state of geyser into three classes: remnant noise, precursor, and eruption states. RF classifier is constructed based on the comprehensive features extracted using the Tsfresh Python package. We show that the classification accuracy using RF on the filtered data is greater than 90%. We also evaluate the accuracy of other classical time-series methods such as dynamic time warping (DTW) on filtered data along with RF on partially filtered data in which we remove the daily trends. Classification accuracy shows that DTW performs poorly (44%) and RF on partially filtered data performs decently (87%). Denoising seismic signals from both daily trends and human activity enhances RF classifier performance by 7%. Finally, these aspects make the proposed ML framework attractive for event discrimination and signal enhancement under noisy conditions, with strong potential for application to monitor leaks in CO 2 sequestration.
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Los Alamos Seismic Network (LASN) Status, September 2017
This report provides an overview of the LANL Seismic Network.
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New Seismic Monitoring Station at Mohawk Ridge, Valles Caldera
Two new broadband digital seismic stations were installed in the Valles Caldera in 2011 and 2012. The first is located on the summit of Cerros del Abrigo (station code CDAB) and the second is located on the flanks of San Antonio Mountain (station code SAMT). Seismic monitoring stations in the caldera serve multiple purposes. These stations augment and expand the current coverage of the Los Alamos Seismic Network (LASN), which is operated to support seismic and volcanic hazards studies for LANL and northern New Mexico (Figure 1). They also provide unique continuous seismic data within the caldera that can be used for scientific studies of the caldera’s substructure and detection of very small seismic signals that may indicate changes in the current and evolving state of remnant magma that is known to exist beneath the caldera. Since the installation of CDAB and SAMT, several very small earthquakes have already been detected near San Antonio Mountain just west of SAMT (Figure 2). These are the first events to be seen in that area. Caldera stations also improve the detection and epicenter determination quality for larger local earthquakes on the Pajarito Fault System east of the Preserve and the Nacimiento Uplift to the west. These larger earthquakes are a concern to LANL Seismic Hazards assessments and seismic monitoring of the Los Alamos region, including the VCNP, is a DOE requirement. Currently the next closest seismic stations to the caldera are on Pipeline Road (PPR) just west of Los Alamos, and Peralta Ridge (PER) south of the caldera. There is no station coverage near the resurgent dome, Redondo Peak, in the center of the caldera. Filling this “hole” is the highest priority for the next new LASN station. We propose to install this station in 2018 on Mohawk Ridge just east of Redondito, in the same area already occupied by other scientific installations, such as the MCON flux tower operated by UNM.
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