Using explainability to design physics-aware CNNs for solving subsurface inverse problems

We present a novel method of using explainability techniques to design physics-aware neural networks. We demonstrate our approach by developing a convolutional neural network (CNN) for solving an inverse problem for shallow subsurface imaging. Although CNNs have gained popularity in recent years across many fields, the development of CNNs remains an art, as there are no clear guidelines regarding the selection of hyperparameters that will yield the best network. While optimization algorithms may be used to select hyperparameters automatically, these methods focus on developing networks with high predictive accuracy while disregarding model explainability (descriptive accuracy). However, the field of Explainable Artificial Intelligence (XAI) addresses the absence of model explainability by providing tools that allow developers to evaluate the internal logic of neural networks. In this study, we use the explainability methods Score-CAM and Deep SHAP to select hyperparameters, such as kernel sizes and network depth, to develop a physics-aware CNN for shallow subsurface imaging. We begin with a relatively deep Encoder-Decoder network, which uses surface wave dispersion images as inputs and generates 2D shear wave velocity subsurface images as outputs. Through model explanations, we ultimately find that a shallow CNN using two convolutional layers with an atypical kernel size of 3x1 yields comparable predictive accuracy but with increased descriptive accuracy. We also show that explainability methods can be used to evaluate the network's complexity and decision-making. We believe this method can be used to develop neural networks with high predictive accuracy while also providing inherent explainability.Comment: 26 pages, 14 figures, 4 table

Using Convolutional Neural Networks to Develop Starting Models for 2D Full Waveform Inversion

Non-invasive subsurface imaging using full waveform inversion (FWI) has the potential to fundamentally change engineering site characterization by enabling the recovery of high resolution 2D/3D maps of subsurface stiffness. Yet, the accuracy of FWI remains quite sensitive to the choice of the initial starting model due to the complexity and non-uniqueness of the inverse problem. In response, we present the novel application of convolutional neural networks (CNNs) to transform an experimental seismic wavefield acquired using a linear array of surface sensors directly into a robust starting model for 2D FWI. We begin by describing three key steps used for developing the CNN, which include: selection of a network architecture, development of a suitable training set, and performance of network training. The ability of the trained CNN to predict a suitable starting model for 2D FWI was compared against other commonly used starting models for a classic near-surface imaging problem; the identification of an undulating, two-layer, soil-bedrock interface. The CNN developed during this study was able to predict complex 2D subsurface images of the testing set directly from their seismic wavefields with an average mean absolute percent error of 6%. When compared to other common approaches, the CNN approach was able to produce starting models with smaller seismic image and waveform misfits, both before and after FWI. The ability of the CNN to generalize to subsurface models which were dissimilar to the ones upon which it was trained was assessed using a more complex, three-layered model. While the predictive ability of the CNN was slightly reduced, it was still able to achieve seismic image and waveform misfits comparable to the other commonly used starting models. This study demonstrates that CNNs have great potential as a tool for developing good starting models for FWI ...Comment: 29 pages, 16 figures, submitted to Geophysic

Shear-Wave Velocity Characterization of the USGS Hawaiian Strong-Motion Network on the Island of Hawaii and Development of an NEHRP Site-Class Map

To assess the level and nature of ground shaking in Hawaii for the purposes of earthquake hazard mitigation and seismic design, empirical ground-motion prediction models are desired. To develop such empirical relationships, knowledge of the subsurface site conditions beneath strong-motion stations is critical. Thus, as a first step to develop ground-motion prediction models for Hawaii, wspectral-analysis-of-surface-waves (SASW) profiling was performed at the 22 free-field U.S. Geological Survey (USGS) strong-motion sites on the Big Island to obtain shear-wave velocity (V(S)) data. Nineteen of these stations recorded the 2006 Kiholo Bay moment magnitude (M) 6.7 earthquake, and 17 stations recorded the triggered M 6.0 Mahukona earthquake. V(S) profiling was performed to reach depths of more than 100 ft. Most of the USGS stations are situated on sites underlain by basalt, based on surficial geologic maps. However, the sites have varying degrees of weathering and soil development. The remaining strong-motion stations are located on alluvium or volcanic ash. V(S30) (average V(S) in the top 30 m) values for the stations on basalt ranged from 906 to 1908 ft/s [National Earthquake Hazards Reduction Program (NEHRP) site classes C and D], because most sites were covered with soil of variable thickness. Based on these data, an NEHRP site-class map was developed for the Big Island. These new V(S) data will be a significant input into an update of the USGS statewide hazard maps and to the operation of ShakeMap on the island of Hawaii.George E. Brown, Jr. Network for Earthquake Engineering Simulation (NEES) under NSF CMS-0086605FEMA HSFEHQ-06-D-0162, HSFEHQ-04-D-0733U.S. Geological Survey, Department of the Interior 08HQGR0036Geotechnical Engineering Cente

hapConstructor: automatic construction and testing of haplotypes in a Monte Carlo framework

Summary: Haplotypes carry important information that can direct investigators towards underlying susceptibility variants, and hence multiple tagging single nucleotide polymorphisms (tSNPs) are usually studied in candidate gene association studies. However, it is often unknown which SNPs should be included in haplotype analyses, or which tests should be performed for maximum power. We have developed a program, hapConstructor, which automatically builds multi-locus SNP sets to test for association in a case-control framework. The multi-SNP sets considered need not be contiguous; they are built based on significance. An important feature is that the missing data imputation is carried out based on the full data, for maximal information and consistency. HapConstructor is implemented in a Monte Carlo framework and naturally extends to allow for significance testing and false discovery rates that account for the construction process and to related individuals. HapConstructor is a useful tool for exploring multi-locus associations in candidate genes and regions

Deep Downhole Seismic Testing at the Waste Treatment Plant Site, Hanford, WA,Volume VI. S-Wave Measurements in Borehole C4997 Seismic Records, Wave-Arrival Identifications and Interpreted S-Wave Velocity Profile.

Velocity measurements in shallow sediments from ground surface to approximately 370 to 400 feet bgs were collected by Redpath Geophysics using impulsive S- and P-wave seismic sources (Redpath 2007). Measurements below this depth within basalt and sedimentary interbeds were made by UTA between October and December 2006 using the T-Rex vibratory seismic source in each of the three boreholes. Results of these measurements including seismic records, wave-arrival identifications and interpreted velocity profiles are presented in the following six volumes: I. P-Wave Measurements in Borehole C4993 II. P-Wave Measurements in Borehole C4996 III. P-Wave Measurements in Borehole C4997 IV. S-Wave Measurements in Borehole C4993 V. S-Wave Measurements in Borehole C4996 VI. S-Wave Measurements in Borehole C4997 In this volume (VI), all S-wave measurements are presented that were performed in Borehole C4997 at the WTP with T-Rex as the seismic source and the Lawrence Berkeley National Laboratory (LBNL) 3-D wireline geophone as the at-depth borehole receiver

Deep Downhole Seismic Testing at the Waste Treatment Plant Site, Hanford, WA,Volume II. P-Wave Measurements in Borehole C4996 Seismic Records, Wave-Arrival Identifications and Interpreted P-Wave Velocity Profile.

In this volume (II), all P-wave measurements are presented that were performed in Borehole C4996 at the Waste Treatment Plant (WTP) with T-Rex as the seismic source and the Lawrence Berkeley National Laboratory (LBNL) 3-D wireline geophone as the at-depth borehole receiver. P-wave measurements were performed over the depth range of 360 to 1400 ft, typically in 10-ft intervals. However, in some interbeds, 5-ft depth intervals were used, while below about 1180 ft, depth intervals of 20 ft were used. Compression (P) waves were generated by moving the base plate of T-Rex for a given number of cycles at a fixed frequency as discussed in Section 2. This process was repeated so that signal averaging in the time domain was performed using 3 to about 15 averages, with 5 averages typically used. In addition to the LBNL 3-D geophone, called the lower receiver herein, a 3-D geophone from Redpath Geophysics was fixed at a depth of 22 ft in Borehole C4996, and a 3-D geophone from the University of Texas was embedded near the borehole at about 1.5 ft below the ground surface. This volume is organized into 12 sections as follows: Section 1: Introduction, Section 2: Explanation of Terminology, Section 3: Vp Profile at Borehole C4996, Sections 4 to 6: Unfiltered P-wave records of lower vertical receiver, reaction mass, and reference receiver, Sections 7 to 9: Filtered P-wave signals of lower vertical receiver, reaction mass and reference receiver, Section 10: Expanded and filtered P-wave signals of lower vertical receiver, and Sections 11 and 12: Waterfall plots of unfiltered and filtered lower vertical receiver signals